@article {pmid38042665, year = {2023}, author = {Aboubakr, O and Houillier, C and Choquet, S and Dupont, S and Hoang-Xuan, K and Mathon, B}, title = {Epileptic seizures in patients with primary central nervous system lymphoma: A systematic review.}, journal = {Revue neurologique}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neurol.2023.08.021}, pmid = {38042665}, issn = {0035-3787}, abstract = {BACKGROUND: Primary central nervous system lymphoma (PCNSL) accounts for less than 5% of primary brain tumors. Epileptic seizures are a common manifestation of brain tumors; however, literature on the prevalence, characteristics, and oncological implications of seizures in patients with PCNSL is limited, and the management of antiepileptic drugs (AEDs) is unclear. This review aimed to summarize the existing knowledge on seizures in PCNSL, their potential association with surgery, oncological treatment, survival rates, and management of AEDs.<br><br>METHODS: A systematic review was performed according to the PRISMA recommendations and included articles published between 1953 and 2023 describing seizures in patients with PCNSL.<br><br>RESULTS: The search identified 282 studies, of which 21 were included. Up to 33% of patients with PCNSL developed seizures, mostly at the initial presentation. Little information was found on changes in seizure incidence through the course of the disease, and no details were found on seizure frequency, the percentage of treatment-resistant patients, or the evolution of seizures at remission. Younger age, cortical location, and immunodeficiency have been identified as potential risk factors for seizures, but evidence is very limited. The growing use of vigorous treatments including intensive chemotherapy with autologous stem cell transplantation and immunotherapy with CAR-T cells is associated with a higher incidence of seizures. The association between seizure development and patient mortality in PCNSL remains unknown. There are no data on AED prophylaxis or the use of specific AEDs in PCNSL.<br><br>CONCLUSIONS: Further studies are needed to investigate seizures in larger cohorts of PCNSL, to clarify their prevalence, better characterize them, identify risk factors, analyze survival rates, and make recommendations on AED management. We recommend following general practice guidelines for seizures symptomatic of brain tumors and not to prescribe AED prophylaxis in PCNSL.}, }
@article {pmid38042393, year = {2023}, author = {Tang, Y and Hu, Y and Zhuang, J and Feng, C and Zhou, X}, title = {Uncovering individual variations in bystander intervention of injustice through intrinsic brain connectivity patterns.}, journal = {NeuroImage}, volume = {285}, number = {}, pages = {120468}, doi = {10.1016/j.neuroimage.2023.120468}, pmid = {38042393}, issn = {1095-9572}, abstract = {When confronted with injustice, individuals often intervene as third parties to restore justice by either punishing the perpetrator or helping the victim, even at their own expense. However, little is known about how individual differences in third-party intervention propensity are related to inter-individual variability in intrinsic brain connectivity patterns and how these associations vary between help and punishment intervention. To address these questions, we employed a novel behavioral paradigm in combination with resting-state fMRI and inter-subject representational similarity analysis (IS-RSA). Participants acted as third-party bystanders and needed to decide whether to maintain the status quo or intervene by either helping the disadvantaged recipient (Help condition) or punishing the proposer (Punish condition) at a specific cost. Our analyses focused on three brain networks proposed in the third-party punishment (TPP) model: the salience (e.g., dorsal anterior cingulate cortex, dACC), central executive (e.g., dorsolateral prefrontal cortex, dlPFC), and default mode (e.g., dorsomedial prefrontal cortex, dmPFC; temporoparietal junction, TPJ) networks. IS-RSA showed that individual differences in resting-state functional connectivity (rs-FC) patterns within these networks were associated with the general third-party intervention propensity. Moreover, rs-FC patterns of the right dlPFC and right TPJ were more strongly associated with individual differences in the helping propensity rather than the punishment propensity, whereas the opposite pattern was observed for the dmPFC. Post-hoc predictive modeling confirmed the predictive power of rs-FC in these regions for intervention propensity across individuals. Collectively, these findings shed light on the shared and distinct roles of key regions in TPP brain networks at rest in accounting for individual variations in justice-restoring intervention behaviors.}, }
@article {pmid38036744, year = {2023}, author = {Griggs, WS and Norman, SL and Deffieux, T and Segura, F and Osmanski, BF and Chau, G and Christopoulos, V and Liu, C and Tanter, M and Shapiro, MG and Andersen, RA}, title = {Decoding motor plans using a closed-loop ultrasonic brain-machine interface.}, journal = {Nature neuroscience}, volume = {}, number = {}, pages = {}, pmid = {38036744}, issn = {1546-1726}, support = {F30 EY032799/EY/NEI NIH HHS/United States ; R01 NS123663/NS/NINDS NIH HHS/United States ; T32 GM008042/GM/NIGMS NIH HHS/United States ; T32 NS105595/NS/NINDS NIH HHS/United States ; }, abstract = {Brain-machine interfaces (BMIs) enable people living with chronic paralysis to control computers, robots and more with nothing but thought. Existing BMIs have trade-offs across invasiveness, performance, spatial coverage and spatiotemporal resolution. Functional ultrasound (fUS) neuroimaging is an emerging technology that balances these attributes and may complement existing BMI recording technologies. In this study, we use fUS to demonstrate a successful implementation of a closed-loop ultrasonic BMI. We streamed fUS data from the posterior parietal cortex of two rhesus macaque monkeys while they performed eye and hand movements. After training, the monkeys controlled up to eight movement directions using the BMI. We also developed a method for pretraining the BMI using data from previous sessions. This enabled immediate control on subsequent days, even those that occurred months apart, without requiring extensive recalibration. These findings establish the feasibility of ultrasonic BMIs, paving the way for a new class of less-invasive (epidural) interfaces that generalize across extended time periods and promise to restore function to people with neurological impairments.}, }
@article {pmid38036086, year = {2023}, author = {Çetin, E and Bilgin, S and Bilgin, G}, title = {A novel wearable ERP-based BCI approach to explicate hunger necessity.}, journal = {Neuroscience letters}, volume = {818}, number = {}, pages = {137573}, doi = {10.1016/j.neulet.2023.137573}, pmid = {38036086}, issn = {1872-7972}, abstract = {This study aimed to design a Brain-Computer Interface system to detect people's hunger status. EEG signals were recorded in various scenarios to create a database. We extracted the time-domain and frequency-domain features from these signals and applied them to the inputs of various Machine Learning algorithms. We compared the classification performances and reached the best-performing algorithm. The highest success score of 97.62% was achieved using the Multilayer Perceptron Neural Network algorithm in Event-Related Potential analysis.}, }
@article {pmid38033812, year = {2023}, author = {Zhu, Y and Yang, Y and Ni, G and Li, S and Liu, W and Gao, Z and Zhang, X and Zhang, Q and Wang, C and Zhou, J}, title = {On-demand electrically controlled melatonin release from PEDOT/SNP composite improves quality of chronic neural recording.}, journal = {Frontiers in bioengineering and biotechnology}, volume = {11}, number = {}, pages = {1284927}, pmid = {38033812}, issn = {2296-4185}, abstract = {Long-time and high-quality signal acquisition performance from implantable electrodes is the key to establish stable and efficient brain-computer interface (BCI) connections. The chronic performance of implantable electrodes is hindered by the inflammatory response of brain tissue. In order to solve the material limitation of biological interface electrodes, we designed sulfonated silica nanoparticles (SNPs) as the dopant of Poly (3,4-ethylenedioxythiophene) (PEDOT) to modify the implantable electrodes. In this work, melatonin (MT) loaded SNPs were incorporated in PEDOT via electrochemical deposition on nickel-chromium (Ni-Cr) alloy electrode and carbon nanotube (CNT) fiber electrodes, without affecting the acute neural signal recording capacity. After coating with PEDOT/SNP-MT, the charge storage capacity of both electrodes was significantly increased, and the electrochemical impedance at 1 kHz of the Ni-Cr alloy electrodes was significantly reduced, while that of the CNT electrodes was significantly increased. In addition, this study inspected the effect of electrically triggered MT release every other day on the quality and longevity of neural recording from implanted neural electrodes in rat hippocampus for 1 month. Both MT modified Ni-Cr alloy electrodes and CNT electrodes showed significantly higher spike amplitude after 26-day recording. Significantly, the histological studies showed that the number of astrocytes around the implanted Ni-Cr alloy electrodes was significantly reduced after MT release. These results demonstrate the potent outcome of PEDOT/SNP-MT treatment in improving the chronic neural recording quality possibly through its anti-inflammatory property.}, }
@article {pmid38033535, year = {2023}, author = {Zhao, Y and Luo, H and Chen, J and Loureiro, R and Yang, S and Zhao, H}, title = {Learning based motion artifacts processing in fNIRS: a mini review.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1280590}, pmid = {38033535}, issn = {1662-4548}, abstract = {This paper provides a concise review of learning-based motion artifacts (MA) processing methods in functional near-infrared spectroscopy (fNIRS), highlighting the challenges of maintaining optimal contact during subject movement, which can lead to MA and compromise data integrity. Traditional strategies often result in reduced reliability of the hemodynamic response and statistical power. Recognizing the limited number of studies focusing on learning-based MA removal, we examine 315 studies, identifying seven pertinent to our focus area. We discuss the current landscape of learning-based MA correction methods and highlight research gaps. Noting the absence of standard evaluation metrics for quality assessment of MA correction, we suggest a novel framework, integrating signal and model quality considerations and employing metrics like ΔSignal-to-Noise Ratio (ΔSNR), confusion matrix, and Mean Squared Error. This work aims to facilitate the application of learning-based methodologies to fNIRS and improve the accuracy and reliability of neurovascular studies.}, }
@article {pmid38032825, year = {2023}, author = {Zou, J and Zhang, Y and Li, J and Tian, X and Ding, N}, title = {Human attention during goal-directed reading comprehension relies on task optimization.}, journal = {eLife}, volume = {12}, number = {}, pages = {}, pmid = {38032825}, issn = {2050-084X}, support = {2021ZD0204105//STI2030-Major Project/ ; 32222035//National Natural Science Foundation of China/ ; 32300856//National Natural Science Foundation of China/ ; 2019KB0AC02//Major Scientific Project of Zhejiang Laboratory/ ; 226-2023-00091//Fundamental Research Funds for the Central Universities/ ; }, mesh = {Humans ; *Eye Movements ; *Comprehension ; Goals ; Attention ; Neural Networks, Computer ; }, abstract = {The computational principles underlying attention allocation in complex goal-directed tasks remain elusive. Goal-directed reading, that is, reading a passage to answer a question in mind, is a common real-world task that strongly engages attention. Here, we investigate what computational models can explain attention distribution in this complex task. We show that the reading time on each word is predicted by the attention weights in transformer-based deep neural networks (DNNs) optimized to perform the same reading task. Eye tracking further reveals that readers separately attend to basic text features and question-relevant information during first-pass reading and rereading, respectively. Similarly, text features and question relevance separately modulate attention weights in shallow and deep DNN layers. Furthermore, when readers scan a passage without a question in mind, their reading time is predicted by DNNs optimized for a word prediction task. Therefore, we offer a computational account of how task optimization modulates attention distribution during real-world reading.}, }
@article {pmid38029425, year = {2023}, author = {Shen, Z and Liang, Q and Chang, Q and Liu, Y and Zhang, Q}, title = {Topological Hydrogels for Long-Term Brain Signal Monitoring, Neuromodulation, and Stroke Treatment.}, journal = {Advanced materials (Deerfield Beach, Fla.)}, volume = {}, number = {}, pages = {e2310365}, doi = {10.1002/adma.202310365}, pmid = {38029425}, issn = {1521-4095}, abstract = {Stroke is the primary cause of disability without effective rehabilitation methods. Emerging brain-machine interfaces offer promise for regulating brain neural circuits and promoting the recovery of brain function disorders. Implantable probes play key roles in brain-machine interfaces, which are subject to two irreconcilable tradeoffs between conductivity and modulus match/transparency. In this work, we incorporated mechanically interlocked polyrotaxane in topological hydrogels to solve the two tradeoffs at the molecular level through the pulley effect of polyrotaxane. The unique performance of the topological hydrogels enabled them to acquire brain neural information and conduct neuromodulation. The probe was capable of continuously recording local field potentials for 8 weeks. Optogenetic neuromodulation in the primary motor cortex to regulate brain neural circuits and control limb behavior was realized using the probe. Most importantly, optogenetic neuromodulation was conducted using the probe, which effectively reduced the infarct regions of the brain tissue and promoted locomotor function recovery. This work exhibits a significant scientific advancement in the design concept of neural probes for developing brain-machine interfaces and seeking brain disease therapies. This article is protected by copyright. All rights reserved.}, }
@article {pmid38027514, year = {2023}, author = {Perna, A and Angotzi, GN and Berdondini, L and Ribeiro, JF}, title = {Advancing the interfacing performances of chronically implantable neural probes in the era of CMOS neuroelectronics.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1275908}, pmid = {38027514}, issn = {1662-4548}, abstract = {Tissue penetrating microelectrode neural probes can record electrophysiological brain signals at resolutions down to single neurons, making them invaluable tools for neuroscience research and Brain-Computer-Interfaces (BCIs). The known gradual decrease of their electrical interfacing performances in chronic settings, however, remains a major challenge. A key factor leading to such decay is Foreign Body Reaction (FBR), which is the cascade of biological responses that occurs in the brain in the presence of a tissue damaging artificial device. Interestingly, the recent adoption of Complementary Metal Oxide Semiconductor (CMOS) technology to realize implantable neural probes capable of monitoring hundreds to thousands of neurons simultaneously, may open new opportunities to face the FBR challenge. Indeed, this shift from passive Micro Electro-Mechanical Systems (MEMS) to active CMOS neural probe technologies creates important, yet unexplored, opportunities to tune probe features such as the mechanical properties of the probe, its layout, size, and surface physicochemical properties, to minimize tissue damage and consequently FBR. Here, we will first review relevant literature on FBR to provide a better understanding of the processes and sources underlying this tissue response. Methods to assess FBR will be described, including conventional approaches based on the imaging of biomarkers, and more recent transcriptomics technologies. Then, we will consider emerging opportunities offered by the features of CMOS probes. Finally, we will describe a prototypical neural probe that may meet the needs for advancing clinical BCIs, and we propose axial insertion force as a potential metric to assess the influence of probe features on acute tissue damage and to control the implantation procedure to minimize iatrogenic injury and subsequent FBR.}, }
@article {pmid38027506, year = {2023}, author = {Tao, J and Dan, Y and Zhou, D}, title = {Possibilistic distribution distance metric: a robust domain adaptation learning method.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1247082}, pmid = {38027506}, issn = {1662-4548}, abstract = {The affective Brain-Computer Interface (aBCI) systems, which achieve predictions for individual subjects through training on multiple subjects, often cannot achieve satisfactory results due to the differences in Electroencephalogram (EEG) patterns between subjects. One tried to use Subject-specific classifiers, but there was a lack of sufficient labeled data. To solve this problem, Domain Adaptation (DA) has recently received widespread attention in the field of EEG-based emotion recognition. Domain adaptation (DA) learning aims to solve the problem of inconsistent distributions between training and test datasets and has received extensive attention. Most existing methods use Maximum Mean Discrepancy (MMD) or its variants to minimize the problem of domain distribution inconsistency. However, noisy data in the domain can lead to significant drift in domain means, which can affect the adaptability performance of learning methods based on MMD and its variants to some extent. Therefore, we propose a robust domain adaptation learning method with possibilistic distribution distance measure. Firstly, the traditional MMD criterion is transformed into a novel possibilistic clustering model to weaken the influence of noisy data, thereby constructing a robust possibilistic distribution distance metric (P-DDM) criterion. Then the robust effectiveness of domain distribution alignment is further improved by a fuzzy entropy regularization term. The proposed P-DDM is in theory proved which be an upper bound of the traditional distribution distance measure method MMD criterion under certain conditions. Therefore, minimizing P-DDM can effectively optimize the MMD objective. Secondly, based on the P-DDM criterion, a robust domain adaptation classifier based on P-DDM (C-PDDM) is proposed, which adopts the Laplacian matrix to preserve the geometric consistency of instances in the source domain and target domain for improving the label propagation performance. At the same time, by maximizing the use of source domain discriminative information to minimize domain discrimination error, the generalization performance of the learning model is further improved. Finally, a large number of experiments and analyses on multiple EEG datasets (i.e., SEED and SEED-IV) show that the proposed method has superior or comparable robustness performance (i.e., has increased by around 10%) in most cases.}, }
@article {pmid38027480, year = {2023}, author = {Xu, Y and Zhao, H and Ieracitano, C}, title = {Editorial: Advances in brain-computer interface technologies for closed-loop neuromodulation.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1327533}, doi = {10.3389/fnins.2023.1327533}, pmid = {38027480}, issn = {1662-4548}, }
@article {pmid38027473, year = {2023}, author = {Zhang, M and Huang, J and Ni, S}, title = {Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1270785}, pmid = {38027473}, issn = {1662-4548}, abstract = {INTRODUCTION: The electroencephalographic (EEG) based on the motor imagery task is derived from the physiological electrical signal caused by the autonomous activity of the brain. Its weak potential difference changes make it easy to be overwhelmed by noise, and the EEG acquisition method has a natural limitation of low spatial resolution. These have brought significant obstacles to high-precision recognition, especially the recognition of the motion intention of the same upper limb.<br><br>METHODS: This research proposes a method that combines signal traceability and Riemannian geometric features to identify six motor intentions of the same upper limb, including grasping/holding of the palm, flexion/extension of the elbow, and abduction/adduction of the shoulder. First, the EEG data of electrodes irrelevant to the task were screened out by low-resolution brain electromagnetic tomography. Subsequently, tangential spatial features are extracted by the Riemannian geometry framework in the covariance matrix estimated from the reconstructed EEG signals. The learned Riemannian geometric features are used for pattern recognition by a support vector machine with a linear kernel function.<br><br>RESULTS: The average accuracy of the six classifications on the data set of 15 participants is 22.47%, the accuracy is 19.34% without signal traceability, the accuracy is 18.07% when the features are the filter bank common spatial pattern (FBCSP), and the accuracy is 16.7% without signal traceability and characterized by FBCSP.<br><br>DISCUSSION: The results show that the proposed method can significantly improve the accuracy of intent recognition. In addressing the issue of temporal variability in EEG data for active Brain-Machine Interfaces, our method achieved an average standard deviation of 2.98 through model transfer on different days' data.}, }
@article {pmid38025739, year = {2023}, author = {Chung, PC and Lin, IF}, title = {Sensitivity analysis of selection bias: a graphical display by bias-correction index.}, journal = {PeerJ}, volume = {11}, number = {}, pages = {e16411}, pmid = {38025739}, issn = {2167-8359}, mesh = {Humans ; Selection Bias ; Surveys and Questionnaires ; *Insurance, Health ; Odds Ratio ; *Informed Consent ; }, abstract = {BACKGROUND: In observational studies, how the magnitude of potential selection bias in a sensitivity analysis can be quantified is rarely discussed. The purpose of this study was to develop a sensitivity analysis strategy by using the bias-correction index (BCI) approach for quantifying the influence and direction of selection bias.<br><br>METHODS: We used a BCI, a function of selection probabilities conditional on outcome and covariates, with different selection bias scenarios in a logistic regression setting. A bias-correction sensitivity plot was illustrated to analyze the associations between proctoscopy examination and sociodemographic variables obtained using the data from the Taiwan National Health Interview Survey (NHIS) and of a subset of individuals who consented to having their health insurance data further linked.<br><br>RESULTS: We included 15,247 people aged &#x2265;20 years, and 87.74% of whom signed the informed consent. When the entire sample was considered, smokers were less likely to undergo proctoscopic examination (odds ratio (OR): 0.69, 95% CI [0.57-0.84]), than nonsmokers were. When the data of only the people who provided consent were considered, the OR was 0.76 (95% CI [0.62-0.94]). The bias-correction sensitivity plot indicated varying ORs under different degrees of selection bias.<br><br>CONCLUSIONS: When data are only available in a subsample of a population, a bias-correction sensitivity plot can be used to easily visualize varying ORs under different selection bias scenarios. The similar strategy can be applied to models other than logistic regression if an appropriate BCI is derived.}, }
@article {pmid38023311, year = {2023}, author = {Cantillo-Negrete, J and Carino-Escobar, RI and Ortega-Robles, E and Arias-Carrión, O}, title = {A comprehensive guide to BCI-based stroke neurorehabilitation interventions.}, journal = {MethodsX}, volume = {11}, number = {}, pages = {102452}, pmid = {38023311}, issn = {2215-0161}, abstract = {Brain-Computer Interfaces (BCIs) offer the potential to facilitate neurorehabilitation in stroke patients by decoding user intentions from the central nervous system, thereby enabling control over external devices. Despite their promise, the diverse range of intervention parameters and technical challenges in clinical settings have hindered the accumulation of substantial evidence supporting the efficacy and effectiveness of BCIs in stroke rehabilitation. This article introduces a practical guide designed to navigate through these challenges in conducting BCI interventions for stroke rehabilitation. Applicable regardless of infrastructure and study design limitations, this guide acts as a comprehensive reference for executing BCI-based stroke interventions. Furthermore, it encapsulates insights gleaned from administering hundreds of BCI rehabilitation sessions to stroke patients.•Presents a comprehensive methodology for implementing BCI-based upper extremity therapy in stroke patients.•Provides detailed guidance on the number of sessions, trials, as well as the necessary hardware and software for effective intervention.}, }
@article {pmid38021246, year = {2023}, author = {Hooks, K and El-Said, R and Fu, Q}, title = {Decoding reach-to-grasp from EEG using classifiers trained with data from the contralateral limb.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1302647}, pmid = {38021246}, issn = {1662-5161}, abstract = {Fundamental to human movement is the ability to interact with objects in our environment. How one reaches an object depends on the object's shape and intended interaction afforded by the object, e.g., grasp and transport. Extensive research has revealed that the motor intention of reach-to-grasp can be decoded from cortical activities using EEG signals. The goal of the present study is to determine the extent to which information encoded in the EEG signals is shared between two limbs to enable cross-hand decoding. We performed an experiment in which human subjects (n = 10) were tasked to interact with a novel object with multiple affordances using either right or left hands. The object had two vertical handles attached to a horizontal base. A visual cue instructs what action (lift or touch) and whether the left or right handle should be used for each trial. EEG was recorded and processed from bilateral frontal-central-parietal regions (30 channels). We trained LDA classifiers using data from trials performed by one limb and tested the classification accuracy using data from trials performed by the contralateral limb. We found that the type of hand-object interaction can be decoded with approximately 59 and 69% peak accuracy in the planning and execution stages, respectively. Interestingly, the decoding accuracy of the reaching directions was dependent on how EEG channels in the testing dataset were spatially mirrored, and whether directions were labeled in the extrinsic (object-centered) or intrinsic (body-centered) coordinates.}, }
@article {pmid38021234, year = {2023}, author = {Liang, W and Jin, J and Xu, R and Wang, X and Cichocki, A}, title = {Variance characteristic preserving common spatial pattern for motor imagery BCI.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1243750}, pmid = {38021234}, issn = {1662-5161}, abstract = {INTRODUCTION: The common spatial patterns (CSP) algorithm is the most popular technique for extracting electroencephalogram (EEG) features in motor imagery based brain-computer interface (BCI) systems. CSP algorithm embeds the dimensionality of multichannel EEG data to extract features of motor imagery tasks. Most previous studies focused on the optimization of the time domain and the spectrum domain of EEG signal to improve the effectiveness of CSP, whereas ignoring the constraint on the projected feature space.<br><br>METHODS: This study proposed a variance characteristic preserving CSP (VPCSP) that is modified by a regularization item based on graph theory. Specifically, we calculated the loss of abnormalities of the projected data while preserving the variance characteristic locally. Then the loss could be rewritten as a matrix with the introduction of the Laplace matrix, which turned it into a generalized eigenvalue problem equivalent to CSP. This study evaluated the proposed method on two public EEG datasets from the BCI competition. The modified method could extract robust and distinguishable features that provided higher classification performance. Experimental results showed that the proposed regularization improved the effectiveness of CSP significantly and achieved superior performance compared with reported modified CSP algorithms significantly.<br><br>RESULTS: The classification accuracy of the proposed method achieved 87.88 %, 90.07 %, and 76.06 % on public dataset IV part I, III part IVa and the self-collected dataset, respectively. Comparative experiments are conducted on two public datasets and one self-collected dataset. Results showed that the proposed method outperformed the reported algorithm.<br><br>DISCUSSION: The proposed method can extract robust features to increase the performance of BCI systems. And the proposal still has expandability. These results show that our proposal is a promising candidate for the performance improvement of MI-BCI.}, }
@article {pmid38021231, year = {2023}, author = {Fernández-Rodríguez, &#xc1; and Martínez-Cagigal, V and Santamaría-Vázquez, E and Ron-Angevin, R and Hornero, R}, title = {Influence of spatial frequency in visual stimuli for cVEP-based BCIs: evaluation of performance and user experience.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1288438}, pmid = {38021231}, issn = {1662-5161}, abstract = {Code-modulated visual evoked potentials (c-VEPs) are an innovative control signal utilized in brain-computer interfaces (BCIs) with promising performance. Prior studies on steady-state visual evoked potentials (SSVEPs) have indicated that the spatial frequency of checkerboard-like stimuli influences both performance and user experience. Spatial frequency refers to the dimensions of the individual squares comprising the visual stimulus, quantified in cycles (i.e., number of black-white squares pairs) per degree of visual angle. However, the specific effects of this parameter on c-VEP-based BCIs remain unexplored. Therefore, the objective of this study is to investigate the role of spatial frequency of checkerboard-like visual stimuli in a c-VEP-based BCI. Sixteen participants evaluated selection matrices with eight spatial frequencies: C001 (0 c/°, 1×1 squares), C002 (0.15 c/°, 2×2 squares), C004 (0.3 c/°, 4×4 squares), C008 (0.6 c/°, 8×8 squares), C016 (1.2 c/°, 16×16 squares), C032 (2.4 c/°, 32×32 squares), C064 (4.79 c/°, 64×64 squares), and C128 (9.58 c/°, 128×128 squares). These conditions were tested in an online spelling task, which consisted of 18 trials each conducted on a 3×3 command interface. In addition to accuracy and information transfer rate (ITR), subjective measures regarding comfort, ocular irritation, and satisfaction were collected. Significant differences in performance and comfort were observed based on different stimulus spatial frequencies. Although all conditions achieved mean accuracy over 95% after 2.1 s of trial duration, C016 stood out in terms user experience. The proposed condition not only achieved a mean accuracy of 96.53% and 164.54 bits/min with a trial duration of 1.05s, but also was reported to be significantly more comfortable than the traditional C001 stimulus. Since both features are key for BCI development, higher spatial frequencies than the classical black-to-white stimulus might be more adequate for c-VEP systems. Hence, we assert that the spatial frequency should be carefully considered in the development of future applications for c-VEP-based BCIs.}, }
@article {pmid38019938, year = {2023}, author = {Zhang, Y and Coid, J}, title = {Childhood Adversity Determines the Syndemic Effects of Violence, Substance Misuse, and Sexual Behavior on Psychotic Spectrum Disorder Among Men.}, journal = {Schizophrenia bulletin}, volume = {}, number = {}, pages = {}, doi = {10.1093/schbul/sbad165}, pmid = {38019938}, issn = {1745-1701}, support = {RP-PG-6407-10500//National Institute for Health Research/ ; 82001409//National Natural Science Foundation of China/ ; }, abstract = {BACKGROUND AND HYPOTHESIS: Childhood adversity (CA) increases the risk for several adult psychiatric conditions. It is unclear why some exposed individuals experience psychotic symptoms and others do not. We investigated whether a syndemic explained a psychotic outcome determined by CA.<br><br>STUDY DESIGN: We used self-reported cross-sectional data from 7461 British men surveyed in different population subgroups. Latent class analysis (LCA) identified categorical psychopathological outcomes. LCs were tested by interaction analysis between syndemic factors derived from confirmatory factor analysis according to CA experiences. Pathway analysis using partial least squares path modeling.<br><br>RESULTS: A 4-class model with excellent fit identified an LC characterized by both psychotic and anxiety symptoms (class 4). A syndemic model of joint effects, adducing a 3-component latent variable of substance misuse (SM), high-risk sexual behavior (SH), violence and criminality (VC) showed synergy between components and explained the psychotic outcome (class 4). We found significant interactions between factor scores on the multiplicative scale, specific only to class 4 (psychosis), including SM&#x2005;&#xd7;&#x2005;SH, SH&#x2005;&#xd7;&#x2005;VC, and SM&#x2005;&#xd7;&#x2005;VC (OR&#x2005;>&#x2005;1, P&#x2005;<&#x2005;.05); and on the additive scale SM&#x2005;&#xd7;&#x2005;SH (relative excess risk due to interaction >0, P&#x2005;<&#x2005;.05), but only for men who experienced CA.<br><br>CONCLUSION: Multiplicative synergistic interactions between SM, SH, and VC constituted a mechanism determining a psychotic outcome, but not for anxiety disorder, mixed anxiety disorder/depression, or depressive disorder. This was specific to men who had experienced CA along direct and syndemic pathways. Population interventions should target SM and VC in adulthood but prioritize primary prevention strategies for CA.}, }
@article {pmid38019907, year = {2023}, author = {Zhang, R and Pan, S and Zheng, S and Liao, Q and Jiang, Z and Wang, D and Li, X and Hu, A and Li, X and Zhu, Y and Shen, X and Lei, J and Zhong, S and Zhang, X and Huang, L and Wang, X and Huang, L and Shen, L and Song, BL and Zhao, JW and Wang, Z and Yang, B and Guo, X}, title = {Lipid-anchored proteasomes control membrane protein homeostasis.}, journal = {Science advances}, volume = {9}, number = {48}, pages = {eadj4605}, doi = {10.1126/sciadv.adj4605}, pmid = {38019907}, issn = {2375-2548}, abstract = {Protein degradation in eukaryotic cells is mainly carried out by the 26S proteasome, a macromolecular complex not only present in the cytosol and nucleus but also associated with various membranes. How proteasomes are anchored to the membrane and the biological meaning thereof have been largely unknown in higher organisms. Here, we show that N-myristoylation of the Rpt2 subunit is a general mechanism for proteasome-membrane interaction. Loss of this modification in the Rpt2-G2A mutant cells leads to profound changes in the membrane-associated proteome, perturbs the endomembrane system, and undermines critical cellular processes such as cell adhesion, endoplasmic reticulum-associated degradation and membrane protein trafficking. Rpt2[G2A/G2A] homozygous mutation is embryonic lethal in mice and is sufficient to abolish tumor growth in a nude mice xenograft model. These findings have defined an evolutionarily conserved mechanism for maintaining membrane protein homeostasis and underscored the significance of compartmentalized protein degradation by myristoyl-anchored proteasomes in health and disease.}, }
@article {pmid38018832, year = {2023}, author = {Brannigan, JFM and Fry, A and Opie, NL and Campbell, BCV and Mitchell, PJ and Oxley, TJ}, title = {Endovascular Brain-Computer Interfaces in Poststroke Paralysis.}, journal = {Stroke}, volume = {}, number = {}, pages = {}, doi = {10.1161/STROKEAHA.123.037719}, pmid = {38018832}, issn = {1524-4628}, abstract = {Stroke is a leading cause of paralysis, most frequently affecting the upper limbs and vocal folds. Despite recent advances in care, stroke recovery invariably reaches a plateau, after which there are permanent neurological impairments. Implantable brain-computer interface devices offer the potential to bypass permanent neurological lesions. They function by (1) recording neural activity, (2) decoding the neural signal occurring in response to volitional motor intentions, and (3) generating digital control signals that may be used to control external devices. While brain-computer interface technology has the potential to revolutionize neurological care, clinical translation has been limited. Endovascular arrays present a novel form of minimally invasive brain-computer interface devices that have been deployed in human subjects during early feasibility studies. This article provides an overview of endovascular brain-computer interface devices and critically evaluates the patient with stroke as an implant candidate. Future opportunities are mapped, along with the challenges arising when decoding neural activity following infarction. Limitations arise when considering intracerebral hemorrhage and motor cortex lesions; however, future directions are outlined that aim to address these challenges.}, }
@article {pmid38016453, year = {2023}, author = {Wang, R and Zhou, T and Li, Z and Zhao, J and Li, X}, title = {Using oscillatory and aperiodic neural activity features for identifying idle state in SSVEP-based BCIs reduces false triggers.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad1054}, pmid = {38016453}, issn = {1741-2552}, abstract = {OBJECTIVE: In existing studies, rhythmic (oscillatory) components were used as main features to identify brain states, such as control and idle states, while non-rhythmic (aperiodic) components were ignored. Recent studies have shown that aperiodic (1/f) activity is functionally related to cognitive processes. It is not clear if aperiodic activity can distinguish brain states in asynchronous brain-computer interfaces (BCIs) to reduce false triggers. In this paper, we propose an asynchronous method based on the fusion of oscillatory and aperiodic features for steady-state visual evoked potential (SSVEP)-based BCIs.<br><br>METHODS: The proposed method first evaluates the oscillatory and aperiodic components of control and idle states using irregular-resampling auto-spectral analysis (IRASA). Oscillatory features are then extracted using the spectral power of the fundamental, second-harmonic, and third-harmonic frequencies of the oscillatory component, and aperiodic features are extracted using the slope and intercept of the first-order polynomial of the spectral fit of the aperiodic component under a log-logarithmic axis. The process produces two types of feature pools (oscillatory and aperiodic features). Next, feature selection (dimensionality reduction) is applied to the feature pools using Bonferroni-corrected p-values from a two-way analysis of variance (ANOVA). Last, these spatial-specific statistically significant features are used as input for classification to identify the idle state.<br><br>RESULTS: On a 7-target dataset from 15 subjects, the mix of oscillatory and aperiodic features achieved an average accuracy of 88.39% compared to 83.53% when using oscillatory features alone (4.86% improvement). The results demonstrated that the proposed idle state recognition method achieved enhanced performance by incorporating aperiodic features.<br><br>CONCLUSIONS: Our results demonstrated that (1) aperiodic features were effective in recognizing idle states and (2) fusing features of oscillatory and aperiodic components enhanced classification performance by 4.86% compared to oscillatory features alone.}, }
@article {pmid38016450, year = {2023}, author = {Ahmadipour, P and Sani, OG and Pesaran, B and Shanechi, MM}, title = {Multimodal subspace identification for modeling discrete-continuous spiking and field potential population activity.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad1053}, pmid = {38016450}, issn = {1741-2552}, abstract = {OBJECTIVE: Learning dynamical latent state models for multimodal spiking and field potential activity can reveal their collective low-dimensional dynamics and enable better decoding of behavior through multimodal fusion. Toward this goal, developing unsupervised learning methods that are computationally efficient is important, especially for real-time learning applications such as brain-machine interfaces (BMIs). However, efficient learning remains elusive for multimodal spike-field data due to their heterogeneous discrete-continuous distributions and different timescales.<br><br>APPROACH: Here, we develop a multiscale subspace identification (multiscale SID) algorithm that enables computationally efficient learning for modeling and dimensionality reduction for multimodal discrete-continuous spike-field data. We describe the spike-field activity as combined Poisson and Gaussian observations, for which we derive a new analytical subspace identification method. Importantly, we also introduce a novel constrained optimization approach to learn valid noise statistics, which is critical for multimodal statistical inference of the latent state, neural activity, and behavior. We validate the method using numerical simulations and spike-LFP population activity recorded during a naturalistic reach and grasp behavior.<br><br>RESULTS: We find that multiscale SID accurately learned dynamical models of spike-field signals and extracted low-dimensional dynamics from these multimodal signals. Further, it fused multimodal information, thus better identifying the dynamical modes and predicting behavior compared to using a single modality. Finally, compared to existing multiscale expectation-maximization learning for Poisson-Gaussian observations, multiscale SID had a much lower training time while being better in identifying the dynamical modes and having a better or similar accuracy in predicting neural activity and behavior.<br><br>SIGNIFICANCE: Overall, multiscale SID is an accurate learning method that is particularly beneficial when efficient learning is of interest, such as for online adaptive BMIs to track non-stationary dynamics or for reducing offline training time in neuroscience investigations.}, }
@article {pmid38016198, year = {2023}, author = {Liu, M and Jiang, N and Qin, C and Xue, Y and Wu, J and Qiu, Y and Yuan, Q and Chen, C and Huang, L and Zhuang, L and Wang, P}, title = {Multimodal spatiotemporal monitoring of basal stem cell-derived organoids reveals progression of olfactory dysfunction in Alzheimer's disease.}, journal = {Biosensors &amp; bioelectronics}, volume = {246}, number = {}, pages = {115832}, doi = {10.1016/j.bios.2023.115832}, pmid = {38016198}, issn = {1873-4235}, abstract = {Olfactory dysfunction (OD) is a highly prevalent symptom and an early sign of neurodegenerative diseases in humans. However, the roles of peripheral olfactory system in disease progression and the mechanisms behind neurodegeneration remain to be studied. Olfactory epithelium (OE) organoid is an ideal model to study pathophysiology in vitro, yet the reliance on 3D culture condition limits continual in situ monitoring of organoid development. Here, we combined impedance biosensors and live imaging for real-time spatiotemporal analysis of OE organoids morphological and physiological features during Alzheimer's disease (AD) progression. The impedance measurements showed that organoids generated from basal stem cells of APP/PS1 transgenic mice had lower proliferation rate than that from wild-type mice. In concert with the biosensor measurements, live imaging enabled to visualize the spatial and temporal dynamics of organoid morphology. Abnormal protein aggregation and accumulation, including amyloid plaques and neurofibrillary tangles, was found in AD organoids and increased as disease progressed. This multimodal in situ bioelectrical measurement and imaging provide a new platform for investigating onset mechanisms of OD, which would shed new light on early diagnosis and treatment of neurodegenerative disease.}, }
@article {pmid38015667, year = {2023}, author = {Li, R and Zhao, X and Wang, Z and Xu, G and Hu, H and Zhou, T and Xu, T}, title = {A Novel Hybrid Brain-Computer Interface Combining the Illusion-induced VEP and SSVEP.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3337525}, pmid = {38015667}, issn = {1558-0210}, abstract = {Traditional single-modality brain-computer interface (BCI) systems are limited by their reliance on a single characteristic of brain signals. To address this issue, incorporating multiple features from EEG signals can provide robust information to enhance BCI performance. In this study, we designed and implemented a novel hybrid paradigm that combined illusion-induced visual evoked potential (IVEP) and steady-state visual evoked potential (SSVEP) with the aim of leveraging their features simultaneously to improve system efficiency. The proposed paradigm was validated by two experimental studies, which encompassed feature analysis of IVEP with a static paradigm, and performance evaluation of hybrid paradigm in comparison with the conventional SSVEP paradigm. The characteristic analysis yielded significant differences in response waveforms among different motion illusions. The performance evaluation of the hybrid BCI demonstrates the advantage of integrating illusory stimuli into the SSVEP paradigm. This integration effectively enhanced the spatio-temporal features of EEG signals, resulting in higher classification accuracy and information transfer rate (ITR) within a short time window when compared to traditional SSVEP-BCI in four-command task. Furthermore, the questionnaire results of subjective estimation revealed that proposed hybrid BCI offers less eye fatigue, and potentially higher levels of concentration, physical condition, and mental condition for users. This work first introduced the IVEP signals in hybrid BCI system that could enhance performance efficiently, which is promising to fulfill the requirements for efficiency in practical BCI control systems.}, }
@article {pmid38015349, year = {2023}, author = {Wu, X and Liu, Y and Wang, X and Zheng, L and Pan, L and Wang, H}, title = {Developmental Impairments of Synaptic Refinement in the Thalamus of a Mouse Model of Fragile X Syndrome.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {38015349}, issn = {1995-8218}, abstract = {While somatosensory over-reactivity is a common feature of autism spectrum disorders such as fragile X syndrome (FXS), the thalamic mechanisms underlying this remain unclear. Here, we found that the developmental elimination of synapses formed between the principal nucleus of V (PrV) and the ventral posterior medial nucleus (VPm) of the somatosensory system was delayed in fragile X mental retardation 1 gene knockout (Fmr1 KO) mice, while the developmental strengthening of these synapses was disrupted. Immunohistochemistry showed excessive VGluT2 puncta in mutants at P12-13, but not at P7-8 or P15-16, confirming a delay in somatic pruning of PrV-VPm synapses. Impaired synaptic function was associated with a reduction in the frequency of quantal AMPA events, as well as developmental deficits in presynaptic vesicle size and density. Our results uncovered the developmental impairment of thalamic relay synapses in Fmr1 KO mice and suggest that a thalamic contribution to the somatosensory over-reactivity in FXS should be considered.}, }
@article {pmid38013860, year = {2023}, author = {Anton, NE and Ziliak, MC and Stefanidis, D}, title = {Augmenting mental imagery for robotic surgery using neurofeedback: results of a randomized controlled trial.}, journal = {Global surgical education : journal of the Association for Surgical Education}, volume = {2}, number = {1}, pages = {62}, pmid = {38013860}, issn = {2731-4588}, abstract = {BACKGROUND: Mental imagery (MI) can enhance surgical skills. Research has shown that through brain-computer interface (BCI), it is possible to provide feedback on MI strength. We hypothesized that adding BCI to MI training would enhance robotic skill acquisition compared with controls.<br><br>METHODS: Surgical novices were recruited. At baseline, participants completed the Mental Imagery Questionnaire (MIQ) and the Vandenburg Mental Rotation Test (MRT). Students also performed several tasks on a robotic simulator. Participants were stratified based on MIQ and robotic skill and randomized into three groups: controls, MI, and MI and BCI training. All participants completed five 2-h training sessions. One hour was devoted to practicing robotic skill on the simulator. Additionally, controls completed crosswords for one hour, the MI group completed MI training and crosswords for one hour, and the MI&#x2009;+&#x2009;BCI group completed MI training and MI-related BCI training. Following training, participants completed the same baseline assessments. A Kruskal-Wallis test was used to determine differences between groups. Mann-Whitney U tests were performed to determine specific differences between groups.<br><br>RESULTS: Twenty-seven undergraduates participated. There were post-test differences on the MRT and knot tying task. Sub-analyses revealed that the MI&#x2009;+&#x2009;BCI group significantly outperformed the other groups on knot tying. There were no appreciable differences between the control and MI groups on any measures.<br><br>CONCLUSIONS: Augmenting MI training with BCI led to significantly enhanced MI and robotic skill acquisition than traditional MI or robotic training methods. To optimize surgical skill acquisition in robotic and other surgical skills curricula, educators should consider utilizing MI with BCI training.}, }
@article {pmid38013626, year = {2023}, author = {Wei, Q and Yu, H and Wang, PS and Xie, JJ and Dong, HL and Wu, ZY and Li, HF}, title = {Biallelic variants in the COQ4 gene caused hereditary spastic paraplegia predominant phenotype.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {}, number = {}, pages = {}, doi = {10.1111/cns.14529}, pmid = {38013626}, issn = {1755-5949}, support = {82171238//National Natural Science Foundation of China/ ; 82201513//National Natural Science Foundation of China/ ; }, abstract = {INTRODUCTION: Hereditary spastic paraplegias (HSPs) comprise a group of neurodegenerative disorders characterized by progressive degeneration of upper motor neurons. Homozygous or compound heterozygous variants in COQ4 have been reported to cause primary CoQ10 deficiency-7 (COQ10D7), which is a mitochondrial disease.<br><br>AIMS: We aimed to screened COQ4 variants in a cohort of HSP patients.<br><br>METHODS: A total of 87 genetically unidentified HSP index patients and their available family members were recruited. Whole exome sequencing (WES) was performed in all probands. Functional studies were performed to identify the pathogenicity of those uncertain significance variants.<br><br>RESULTS: In this study, five different COQ4 variants were identified in three Chinese HSP pedigrees and two variants were novel, c.87dupT (p.Arg30*), c.304C>T (p.Arg102Cys). More importantly, we firstly described two early-onset pure HSP caused by COQ4 variants. Functional studies in patient-derived fibroblast lines revealed a reduction cellular CoQ10 levels and the abnormal mitochondrial structure.<br><br>CONCLUSIONS: Our findings revealed that bilateral variants in the COQ4 gene caused HSP predominant phenotype, expanding the phenotypic spectrum of the COQ4-related disorders.}, }
@article {pmid38012418, year = {2023}, author = {Lei, A and Yu, H and Lu, S and Lu, H and Ding, X and Tan, T and Zhang, H and Zhu, M and Tian, L and Wang, X and Su, S and Xue, D and Zhang, S and Zhao, W and Chen, Y and Xie, W and Zhang, L and Zhu, Y and Zhao, J and Jiang, W and Church, G and Chan, FK and Gao, Z and Zhang, J}, title = {A second-generation M1-polarized CAR macrophage with antitumor efficacy.}, journal = {Nature immunology}, volume = {}, number = {}, pages = {}, pmid = {38012418}, issn = {1529-2916}, support = {82373238//National Natural Science Foundation of China (National Science Foundation of China)/ ; 31871453//National Natural Science Foundation of China (National Science Foundation of China)/ ; 91857116//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2019M662035//China Postdoctoral Science Foundation/ ; }, abstract = {Chimeric antigen receptor (CAR) T cell therapies have successfully treated hematological malignancies. Macrophages have also gained attention as an immunotherapy owing to their immunomodulatory capacity and ability to infiltrate solid tumors and phagocytize tumor cells. The first-generation CD3ζ-based CAR-macrophages could phagocytose tumor cells in an antigen-dependent manner. Here we engineered induced pluripotent stem cell-derived macrophages (iMACs) with toll-like receptor 4 intracellular toll/IL-1R (TIR) domain-containing CARs resulting in a markedly enhanced antitumor effect over first-generation CAR-macrophages. Moreover, the design of a tandem CD3ζ-TIR dual signaling CAR endows iMACs with both target engulfment capacity and antigen-dependent M1 polarization and M2 resistance in a nuclear factor kappa B (NF-κB)-dependent manner, as well as the capacity to modulate the tumor microenvironment. We also outline a mechanism of tumor cell elimination by CAR-induced efferocytosis against tumor cell apoptotic bodies. Taken together, we provide a second-generation CAR-iMAC with an ability for orthogonal phagocytosis and polarization and superior antitumor functions in treating solid tumors relative to first-generation CAR-macrophages.}, }
@article {pmid38010934, year = {2023}, author = {Han, J and Gu, X and Yang, GZ and Lo, B}, title = {Noise-Factorized Disentangled Representation Learning for Generalizable Motor Imagery EEG Classification.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2023.3337072}, pmid = {38010934}, issn = {2168-2208}, abstract = {MotorImagery(MI)Electroencephalography(EEG) is one of the most common Brain-Computer Interface (BCI) paradigms that has been widely used in neural rehabilitation and gaming. Although considerable research efforts have been dedicated to developing MI EEG classification algorithms, they are mostly limited in handling scenarios where the training and testing data are not from the same subject or session. Such poor generalization capability significantly limits the realization of BCI in real-world applications. In this paper, we proposed a novel framework to disentangle the representation of raw EEG data into three components, subject/session-specific, MI-taskspecific, and random noises, so that the subject/session-specific feature extends the generalization capability of the system. This is realized by a joint discriminative and generative framework, supported by a series of fundamental training losses and training strategies. We evaluated our framework on three public MI EEG datasets, and detailed experimental results show that our method can achieve superior performance by a large margin compared to current state-of-the-art benchmark algorithms.}, }
@article {pmid38010159, year = {2023}, author = {Ohkubo, M}, title = {The emergence of non-cryogenic quantum magnetic sensors: Synergistic advancement in magnetography together with SQUID.}, journal = {The Review of scientific instruments}, volume = {94}, number = {11}, pages = {}, doi = {10.1063/5.0167372}, pmid = {38010159}, issn = {1089-7623}, abstract = {Emerging non-superconductor quantum magnetic sensors, such as optically pumped magnetometer, fluxgate, magnetic tunnel junction, and diamond nitrogen-vacancy center, are approaching the performance of superconductor quantum interference devices (SQUIDs). These sensors are enabling magnetography for human bodies and brain-computer interface. Will they completely replace the SQUID magnetography in the near future?}, }
@article {pmid38007914, year = {2023}, author = {Bordes, A and El Bendary, Y and Goudard, G and Masson, V and Gourfinkel-An, I and Mathon, B}, title = {Benefits of vagus nerve stimulation on psychomotor functions in patients with severe drug-resistant epilepsy.}, journal = {Epilepsy research}, volume = {198}, number = {}, pages = {107260}, doi = {10.1016/j.eplepsyres.2023.107260}, pmid = {38007914}, issn = {1872-6844}, abstract = {PURPOSE: Patients with severe drug-resistant epilepsy (DRE) experience psychomotor disorders. Our study aimed to assess the psychomotor outcomes after vagus nerve stimulation (VNS) in this population.<br><br>METHODS: We prospectively evaluated psychomotor function in 17 adult patients with severe DRE who were referred for VNS. Psychomotor functions were examined, in the preoperative period and at 18 months post-surgery, by a psychomotor therapist using a full set of the following specific tests: the Rey-Osterrieth complex figure (ROCF) test, the Zazzo's cancelation task (ZCT), the Piaget-Head test and the paired images test.<br><br>RESULTS: At 18 months post-VNS surgery, the Piaget-head scores increased by 3 points (p&#xa0;=&#xa0;0.008) compared to baseline. Performances were also improved for ROCF test both in copy (+2.4 points, p&#xa0;=&#xa0;0.001) and recall (+2.0 points, p&#xa0;=&#xa0;0.008) tasks and for the paired images test (accuracy index: +28.6 %, p&#xa0;=&#xa0;0.03). Regarding the ZCT findings, the efficiency index increased in both single (+16 %, p&#xa0;=&#xa0;0.005) and dual (+17.1 %, p&#xa0;<&#xa0;0.001) tasks. QoL improved in 88.2 % of patients.<br><br>CONCLUSIONS: Patients with severe DRE treated with VNS experienced improved performance in terms of global psychomotor functions. Perceptual organization, visuospatial memory, laterality awareness, sustained attention, concentration, visual scanning, and inhibition were significantly improved.}, }
@article {pmid38007763, year = {2023}, author = {Tian, C}, title = {Research on Brain Signals Classification Based on Deep Learning.}, journal = {Studies in health technology and informatics}, volume = {308}, number = {}, pages = {381-388}, doi = {10.3233/SHTI230863}, pmid = {38007763}, issn = {1879-8365}, mesh = {Humans ; *Neural Networks, Computer ; *Deep Learning ; Algorithms ; Electroencephalography/methods ; Brain/diagnostic imaging ; }, abstract = {With the continuous expansion of brain-computer communication, the precise identification of brain signals has become an essential task for brain-computer equipment. However, existing classification methods are primarily concentrated on the extraction features of brain signals and obtain unacceptable performance when directly used the model to a new brain signals data, which is caused by the different people has extraordinary brain signals. In this work, we utilize the deep learning methods not only extract the features of brain signals but also learn the order information of brain signals, which can satisfy the universal brain signals. Indeed, we utilize the classification features dimension distance loss function to optimize the proposed model and enhance the classification accuracy and we compare our model with existing classification methods to evaluate proposed model. From our extensive experimental results and analysis, we can conclude that our model can achieve the classification of brain signals with the reasonable accuracy and acceptable costs.}, }
@article {pmid38007753, year = {2023}, author = {He, R}, title = {Perspective of Signal Processing-Based on Brain-Computer Interfaces Using Machine Learning Methods.}, journal = {Studies in health technology and informatics}, volume = {308}, number = {}, pages = {295-302}, doi = {10.3233/SHTI230853}, pmid = {38007753}, issn = {1879-8365}, mesh = {*Artificial Intelligence ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Algorithms ; Machine Learning ; Signal Processing, Computer-Assisted ; }, abstract = {The application of artificial intelligence (AI) algorithms is an indispensable portion of developing brain-computer interfaces (BCI). With the continuous development of AI concepts and related technologies. AI algorithms such as neural networks play an increasingly powerful and extensive role in brain-computer interfaces. However, brain-computer interfaces are still facing many technical challenges. Due to the limitations of AI algorithms, brain-computer interfaces not only work with limited accuracy, but also can only be applied to certain simple scenarios. In order to explore the future directions and improvements of AI algorithms in the area of brain-computer interfaces, this paper will review and analyse the advanced applications of AI algorithms in the field of brain-computer interfaces in recent years and give possible future enhancements and development directions for the controversial parts of them. This review first presents the effects of different AI algorithms in BCI applications. A multi-objective classification method is compared with evolutionary algorithms in feature extraction of data. Then, a kind of supervised learning algorithm based on Event Related Potential (ERP) tags is presented to achieve a high accuracy in the process of pattern recognition. Finally, as an important experimental paradigm for BCI, a combined TFD-PSR-CSP feature extraction method, is explained for the problem of motor imagery. The "Discussion" part comprehensively analyses the advantages and disadvantages of the above algorithms and proposes a deep learning-based artificial intelligence algorithm in order to solve the problems arising from the above algorithms.}, }
@article {pmid38007721, year = {2023}, author = {Li, Y}, title = {CNN-Based Image Analysis for EEG Signal Characterization.}, journal = {Studies in health technology and informatics}, volume = {308}, number = {}, pages = {20-30}, doi = {10.3233/SHTI230820}, pmid = {38007721}, issn = {1879-8365}, mesh = {Humans ; *Algorithms ; Electroencephalography/methods ; Neural Networks, Computer ; Recognition, Psychology ; *Brain-Computer Interfaces ; }, abstract = {This article focuses on an attempt to classify and recognize the characterized images of EEG signals directly. For EEG signals, the recognition and judgment of different signals has been the key direction of research. CNN (Convolutional Neural Network) models are usually used for recognition of EEG raw signals about movement and Imagery Dataset. However, the images of EEG raw signals are basically unreadable for researchers, so characterization is a common tool. However, direct recognition of the characterized images is a relatively empty area in the existing research because it requires much higher machine performance than the traditional raw signal recognition. However, feeding the extracted feature images into a CNN and training them can be an efficient and intuitive response to the potential of EEG for brain mapping. The main goal of this research is to examine the discriminative capabilities of traditional visual and image neural networks for pictures described by EEG data. This is not typical in contemporary brain-computer interface research. The direct recognition of the described photos uses a lot of GPU (graphics computing unit) resources, but for the characterized images are easier for people to read than the original images. This work indicates the viability of direct research on defined pictures and increases the application scenario of EEG signals.}, }
@article {pmid38007085, year = {2023}, author = {Chen, A and Hao, S and Han, Y and Fang, Y and Miao, Y}, title = {Exploring the effects of different BCI-based attention training games on the brain: A functional near-infrared spectroscopy study.}, journal = {Neuroscience letters}, volume = {818}, number = {}, pages = {137567}, doi = {10.1016/j.neulet.2023.137567}, pmid = {38007085}, issn = {1872-7972}, abstract = {BCI games have been widely employed as non-invasive therapeutic interventions for conditions, but their efficacy remains a subject of debate. This study explores the efficacy of two prevalent forms of Brain-Computer Interface (BCI)-based attention training games: video games (VG) and physical games (PG). The effectiveness of these games has been examined through the lens of neuroscience, using functional Near-Infrared Spectroscopy (fNIRS) to monitor cortical activation. After the fNIRS test, subjects completed an Intrinsic Motivation Inventory (IMI) questionnaire. PG tasks activated six channels (L-PFC, R-PFC and R-TL), while VG tasks activated only one (R-PFC). Furthermore, females exhibited stronger activation during PG tasks, while males had none in either. Our findings suggest that under equivalent game rules and themes, PG may prove more effective for cognitive rehabilitation than VG, with stronger intrinsic motivation. We also found this result may exhibit gender differences. Finally, this research offers valuable insights for the future design of BCI-based games from a neuroscience perspective.}, }
@article {pmid38006734, year = {2023}, author = {Wang, X and Wang, Y and Qi, W and Kong, D and Wang, W}, title = {BrainGridNet: A two-branch depthwise CNN for decoding EEG-based multi-class motor imagery.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {170}, number = {}, pages = {312-324}, doi = {10.1016/j.neunet.2023.11.037}, pmid = {38006734}, issn = {1879-2782}, abstract = {Brain-computer interfaces (BCIs) based on motor imagery (MI) enable the disabled to interact with the world through brain signals. To meet demands of real-time, stable, and diverse interactions, it is crucial to develop lightweight networks that can accurately and reliably decode multi-class MI tasks. In this paper, we introduce BrainGridNet, a convolutional neural network (CNN) framework that integrates two intersecting depthwise CNN branches with 3D electroencephalography (EEG) data to decode a five-class MI task. The BrainGridNet attains competitive results in both the time and frequency domains, with superior performance in the frequency domain. As a result, an accuracy of 80.26 percent and a kappa value of 0.753 are achieved by BrainGridNet, surpassing the state-of-the-art (SOTA) model. Additionally, BrainGridNet shows optimal computational efficiency, excels in decoding the most challenging subject, and maintains robust accuracy despite the random loss of 16 electrode signals. Finally, the visualizations demonstrate that BrainGridNet learns discriminative features and identifies critical brain regions and frequency bands corresponding to each MI class. The convergence of BrainGridNet's strong feature extraction capability, high decoding accuracy, steady decoding efficacy, and low computational costs renders it an appealing choice for facilitating the development of BCIs.}, }
@article {pmid38005515, year = {2023}, author = {Wolf, P and Götzelmann, T}, title = {VEPdgets: Towards Richer Interaction Elements Based on Visually Evoked Potentials.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {22}, pages = {}, pmid = {38005515}, issn = {1424-8220}, abstract = {For brain-computer interfaces, a variety of technologies and applications already exist. However, current approaches use visual evoked potentials (VEP) only as action triggers or in combination with other input technologies. This paper shows that the losing visually evoked potentials after looking away from a stimulus is a reliable temporal parameter. The associated latency can be used to control time-varying variables using the VEP. In this context, we introduced VEP interaction elements (VEP widgets) for a value input of numbers, which can be applied in various ways and is purely based on VEP technology. We carried out a user study in a desktop as well as in a virtual reality setting. The results for both settings showed that the temporal control approach using latency correction could be applied to the input of values using the proposed VEP widgets. Even though value input is not very accurate under untrained conditions, users could input numerical values. Our concept of applying latency correction to VEP widgets is not limited to the input of numbers.}, }
@article {pmid38005437, year = {2023}, author = {Farabbi, A and Mainardi, L}, title = {Domain-Specific Processing Stage for Estimating Single-Trail Evoked Potential Improves CNN Performance in Detecting Error Potential.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {22}, pages = {}, pmid = {38005437}, issn = {1424-8220}, mesh = {*Electroencephalography/methods ; Evoked Potentials ; Neural Networks, Computer ; Wavelet Analysis ; Algorithms ; *Brain-Computer Interfaces ; }, abstract = {We present a novel architecture designed to enhance the detection of Error Potential (ErrP) signals during ErrP stimulation tasks. In the context of predicting ErrP presence, conventional Convolutional Neural Networks (CNNs) typically accept a raw EEG signal as input, encompassing both the information associated with the evoked potential and the background activity, which can potentially diminish predictive accuracy. Our approach involves advanced Single-Trial (ST) ErrP enhancement techniques for processing raw EEG signals in the initial stage, followed by CNNs for discerning between ErrP and NonErrP segments in the second stage. We tested different combinations of methods and CNNs. As far as ST ErrP estimation is concerned, we examined various methods encompassing subspace regularization techniques, Continuous Wavelet Transform, and ARX models. For the classification stage, we evaluated the performance of EEGNet, CNN, and a Siamese Neural Network. A comparative analysis against the method of directly applying CNNs to raw EEG signals revealed the advantages of our architecture. Leveraging subspace regularization yielded the best improvement in classification metrics, at up to 14% in balanced accuracy and 13.4% in F1-score.}, }
@article {pmid38004858, year = {2023}, author = {Su, K and Qiu, Z and Xu, J}, title = {A 14-Bit, 12 V-to-100 V Voltage Compliance Electrical Stimulator with Redundant Digital Calibration.}, journal = {Micromachines}, volume = {14}, number = {11}, pages = {}, pmid = {38004858}, issn = {2072-666X}, support = {2021ZD0200401//STI 2030-Major Project/ ; 62176232//National Natural Science Foundation of China grant/ ; SN-ZJU-SIAS-002//Starry Night Science Fund of Zhejiang University Shanghai Institute for Advanced Study/ ; }, abstract = {Electrical stimulation is an important technique for modulating the functions of the nervous system through electrical stimulus. To implement a more competitive prototype that can tackle the domain-specific difficulties of existing electrical stimulators, three key techniques are proposed in this work. Firstly, a load-adaptive power saving technique called over-voltage detection is implemented to automatically adjust the supply voltage. Secondly, redundant digital calibration (RDC) is proposed to improve current accuracy and ensure safety during long-term electrical stimulation without costing too much circuit area and power. Thirdly, a flexible waveform generator is designed to provide arbitrary stimulus waveforms for particular applications. Measurement results show the stimulator can adjust the supply voltage from 12 V to 100 V automatically, and the measured effective resolution of the stimulation current reaches 14 bits in a full range of 6.5 mA. Without applying charge balancing techniques, the average mismatch between the cathodic and anodic current pulses in biphasic stimulus is 0.0427%. The proposed electrical stimulator can generate arbitrary stimulus waveforms, including sine, triangle, rectangle, etc., and it is supposed to be competitive for implantable and wearable devices.}, }
@article {pmid38002653, year = {2023}, author = {Zhang, Y and Zeng, H and Zhou, H and Li, J and Wang, T and Guo, Y and Cai, L and Hu, J and Zhang, X and Chen, G}, title = {Predicting the Outcome of Patients with Aneurysmal Subarachnoid Hemorrhage: A Machine-Learning-Guided Scorecard.}, journal = {Journal of clinical medicine}, volume = {12}, number = {22}, pages = {}, pmid = {38002653}, issn = {2077-0383}, support = {52277232, 81971099, 82171273, 82171275//National Natural Science Foundation of China/ ; LR23E070001//Zhejiang Provincial Natural Science Foundation of China/ ; 2022C03133//Key R&amp;D Program of Zhejiang/ ; }, abstract = {Aneurysmal subarachnoid hemorrhage (aSAH) frequently causes long-term disability, but predicting outcomes remains challenging. Routine parameters such as demographics, admission status, CT findings, and blood tests can be used to predict aSAH outcomes. The aim of this study was to compare the performance of traditional logistic regression with several machine learning algorithms using readily available indicators and to generate a practical prognostic scorecard based on machine learning. Eighteen routinely available indicators were collected as outcome predictors for individuals with aSAH. Logistic regression (LR), random forest (RF), support vector machines (SVMs), and fully connected neural networks (FCNNs) were compared. A scorecard system was established based on predictor weights. The results show that machine learning models and a scorecard achieved 0.75~0.8 area under the curve (AUC) predicting aSAH outcomes (LR 0.739, RF 0.749, SVM 0.762~0.793, scorecard 0.794). FCNNs performed best (~0.95) but lacked interpretability. The scorecard model used only five factors, generating a clinically useful tool with a total cutoff score of ≥5, indicating poor prognosis. We developed and validated machine learning models proven to predict outcomes more accurately in individuals with aSAH. The parameters found to be the most strongly predictive of outcomes were NLR, lymphocyte count, monocyte count, hypertension status, and SEBES. The scorecard system provides a simplified means of applying predictive analytics at the bedside using a few key indicators.}, }
@article {pmid38002552, year = {2023}, author = {Popa, LL and Chira, D and Strilciuc, &#x218; and Mureșanu, DF}, title = {Non-Invasive Systems Application in Traumatic Brain Injury Rehabilitation.}, journal = {Brain sciences}, volume = {13}, number = {11}, pages = {}, pmid = {38002552}, issn = {2076-3425}, abstract = {Traumatic brain injury (TBI) is a significant public health concern, often leading to long-lasting impairments in cognitive, motor and sensory functions. The rapid development of non-invasive systems has revolutionized the field of TBI rehabilitation by offering modern and effective interventions. This narrative review explores the application of non-invasive technologies, including electroencephalography (EEG), quantitative electroencephalography (qEEG), brain-computer interface (BCI), eye tracking, near-infrared spectroscopy (NIRS), functional near-infrared spectroscopy (fNIRS), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) in assessing TBI consequences, and repetitive transcranial magnetic stimulation (rTMS), low-level laser therapy (LLLT), neurofeedback, transcranial direct current stimulation (tDCS), transcranial alternative current stimulation (tACS) and virtual reality (VR) as therapeutic approaches for TBI rehabilitation. In pursuit of advancing TBI rehabilitation, this narrative review highlights the promising potential of non-invasive technologies. We emphasize the need for future research and clinical trials to elucidate their mechanisms of action, refine treatment protocols, and ensure their widespread adoption in TBI rehabilitation settings.}, }
@article {pmid38002543, year = {2023}, author = {Lian, J and Qiao, X and Zhao, Y and Li, S and Wang, C and Zhou, J}, title = {EEG-Based Target Detection Using an RSVP Paradigm under Five Levels of Weak Hidden Conditions.}, journal = {Brain sciences}, volume = {13}, number = {11}, pages = {}, pmid = {38002543}, issn = {2076-3425}, support = {2021ZD0201600//the STI 2030-Major Projects/ ; Z201100006820144//the Beijing Nova Program/ ; }, abstract = {Although target detection based on electroencephalogram (EEG) signals has been extensively investigated recently, EEG-based target detection under weak hidden conditions remains a problem. In this paper, we proposed a rapid serial visual presentation (RSVP) paradigm for target detection corresponding to five levels of weak hidden conditions quantitively based on the RGB color space. Eighteen subjects participated in the experiment, and the neural signatures, including P300 amplitude and latency, were investigated. Detection performance was evaluated under five levels of weak hidden conditions using the linear discrimination analysis and support vector machine classifiers on different channel sets. The experimental results showed that, compared with the benchmark condition, (1) the P300 amplitude significantly decreased (8.92 ± 1.24 μV versus 7.84 ± 1.40 μV, p = 0.021) and latency was significantly prolonged (582.39 ± 25.02 ms versus 643.83 ± 26.16 ms, p = 0.028) only under the weakest hidden condition, and (2) the detection accuracy decreased by less than 2% (75.04 ± 3.24% versus 73.35 ± 3.15%, p = 0.029) with a more than 90% reduction in channel number (62 channels versus 6 channels), determined using the proposed channel selection method under the weakest hidden condition. Our study can provide new insights into target detection under weak hidden conditions based on EEG signals with a rapid serial visual presentation paradigm. In addition, it may expand the application of brain-computer interfaces in EEG-based target detection areas.}, }
@article {pmid38000320, year = {2023}, author = {Xu, F and Pan, D and Zheng, H and Ouyang, Y and Jia, Z and Zeng, H}, title = {EESCN: A novel spiking neural network method for EEG-based emotion recognition.}, journal = {Computer methods and programs in biomedicine}, volume = {243}, number = {}, pages = {107927}, doi = {10.1016/j.cmpb.2023.107927}, pmid = {38000320}, issn = {1872-7565}, abstract = {BACKGROUND AND OBJECTIVE: Although existing artificial neural networks have achieved good results in electroencephalograph (EEG) emotion recognition, further improvements are needed in terms of bio-interpretability and robustness. In this research, we aim to develop a highly efficient and high-performance method for emotion recognition based on EEG.<br><br>METHODS: We propose an Emo-EEGSpikeConvNet (EESCN), a novel emotion recognition method based on spiking neural network (SNN). It consists of a neuromorphic data generation module and a NeuroSpiking framework. The neuromorphic data generation module converts EEG data into 2D frame format as input to the NeuroSpiking framework, while the NeuroSpiking framework is used to extract spatio-temporal features of EEG for classification.<br><br>RESULTS: EESCN achieves high emotion recognition accuracies on DEAP and SEED-IV datasets, ranging from 94.56% to 94.81% on DEAP and a mean accuracy of 79.65% on SEED-IV. Compared to existing SNN methods, EESCN significantly improves EEG emotion recognition performance. In addition, it also has the advantages of faster running speed and less memory footprint.<br><br>CONCLUSIONS: EESCN has shown excellent performance and efficiency in EEG-based emotion recognition with potential for practical applications requiring portability and resource constraints.}, }
@article {pmid37995362, year = {2023}, author = {Ke, Y and Wang, T and He, F and Liu, S and Ming, D}, title = {Enhancing EEG-based cross-day mental workload classification using periodic component of power spectrum.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad0f3d}, pmid = {37995362}, issn = {1741-2552}, abstract = {The day-to-day variability of electroencephalogram (EEG) poses a significant challenge to decode human brain activity in EEG-based passive brain-computer interfaces (pBCIs). Conventionally, a time-consuming calibration process is required to collect data from users on a new day to ensure the performance of the machine learning-based decoding model, which hinders the application of pBCIs to monitor mental workload (MWL) states in real-world settings. This study investigated the day-to-day stability of the raw power spectral density (PSD) and their periodic and aperiodic components decomposed by the Fitting Oscillations and One-Over-F algorithm. In addition, we validated the feasibility of using periodic components to improve cross-day MWL classification performance. Compared to the raw PSD (69.9%±18.5%) and the aperiodic component (69.4%±19.2%), the periodic component had better day-to-day stability and significantly higher cross-day classification accuracy (84.2%±11.0%). This finding not only enhances the practicality of pBCIs for MWL estimation but also unlocks the potential for decoding various brain states in future applications.}, }
@article {pmid37995162, year = {2023}, author = {Chen, S and Zhang, X and Shen, X and Huang, Y and Wang, Y}, title = {Online Estimating Pairwise Neuronal Functional Connectivity in Brain-Machine Interface.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3336362}, pmid = {37995162}, issn = {1558-0210}, abstract = {Neurons respond to external stimuli and form functional networks through pairwise interactions. A neural encoding model can describe a single neuron's behavior, and brain-machine interfaces (BMIs) provide a platform to investigate how neurons adapt, functionally connect, and encode movement. Movement modulation and pairwise functional connectivity are modeled as high-dimensional tuning states, estimated from neural spike train observations. However, accurate estimation of this neural state vector can be challenging as pairwise neural interactions are highly dimensional, change in different temporal scales from movement, and could be non-stationary. We propose an Adam-based gradient descent method to online estimate high-dimensional pairwise neuronal functional connectivity and single neuronal tuning adaptation simultaneously. By minimizing negative log-likelihood based on point process observation, the proposed method adaptively adjusts the learning rate for each dimension of the neural state vectors by employing momentum and regularizer. We test the method on real recordings of two rats performing the brain control mode of a two-lever discrimination task. Our results show that our method outperforms existing methods, especially when the state is sparse. Our method is more stable and faster for an online scenario regardless of the parameter initializations. Our method provides a promising tool to track and build the time-variant functional neural connectivity, which dynamically forms the functional network and results in better brain control.}, }
@article {pmid37995161, year = {2023}, author = {Zhu, L and Liu, Y and Liu, R and Peng, Y and Cao, J and Li, J and Kong, W}, title = {Decoding Multi-Brain Motor Imagery from EEG Using Coupling Feature Extraction and Few-Shot Learning.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3336356}, pmid = {37995161}, issn = {1558-0210}, abstract = {Electroencephalography (EEG)-based motor imagery (MI) is one of brain computer interface (BCI) paradigms, which aims to build a direct communication pathway between human brain and external devices by decoding the brain activities. In a traditional way, MI BCI replies on a single brain, which suffers from the limitations, such as low accuracy and weak stability. To alleviate these limitations, multi-brain BCI has emerged based on the integration of multiple individuals' intelligence. Nevertheless, the existing decoding methods mainly use linear averaging or feature integration learning from multi-brain EEG data, and do not effectively utilize coupling relationship features, resulting in undesired decoding accuracy. To overcome these challenges, we proposed an EEG-based multi-brain MI decoding method, which utilizes coupling feature extraction and few-shot learning to capture coupling relationship features among multi-brains with only limited EEG data. We performed an experiment to collect EEG data from multiple persons who engaged in the same task simultaneously and compared the methods on the collected data. The comparison results showed that our proposed method improved the performance by 14.23% compared to the single-brain mode in the 10-shot three-class decoding task. It demonstrated the effectiveness of the proposed method and usability of the method in the context of only small amount of EEG data available.}, }
@article {pmid37993467, year = {2023}, author = {Mao, C and Gao, M and Zang, SK and Zhu, Y and Shen, DD and Chen, LN and Yang, L and Wang, Z and Zhang, H and Wang, WW and Shen, Q and Lu, Y and Ma, X and Zhang, Y}, title = {Orthosteric and allosteric modulation of human HCAR2 signaling complex.}, journal = {Nature communications}, volume = {14}, number = {1}, pages = {7620}, pmid = {37993467}, issn = {2041-1723}, abstract = {Hydroxycarboxylic acids are crucial metabolic intermediates involved in various physiological and pathological processes, some of which are recognized by specific hydroxycarboxylic acid receptors (HCARs). HCAR2 is one such receptor, activated by endogenous β-hydroxybutyrate (3-HB) and butyrate, and is the target for Niacin. Interest in HCAR2 has been driven by its potential as a therapeutic target in cardiovascular and neuroinflammatory diseases. However, the limited understanding of how ligands bind to this receptor has hindered the development of alternative drugs able to avoid the common flushing side-effects associated with Niacin therapy. Here, we present three high-resolution structures of HCAR2-Gi1 complexes bound to four different ligands, one potent synthetic agonist (MK-6892) bound alone, and the two structures bound to the allosteric agonist compound 9n in conjunction with either the endogenous ligand 3-HB or niacin. These structures coupled with our functional and computational analyses further our understanding of ligand recognition, allosteric modulation, and activation of HCAR2 and pave the way for the development of high-efficiency drugs with reduced side-effects.}, }
@article {pmid37992582, year = {2023}, author = {Yu, C and Lu, Y and Pang, J and Li, L}, title = {A hemostatic sponge derived from chitosan and hydroxypropylmethylcellulose.}, journal = {Journal of the mechanical behavior of biomedical materials}, volume = {150}, number = {}, pages = {106240}, doi = {10.1016/j.jmbbm.2023.106240}, pmid = {37992582}, issn = {1878-0180}, abstract = {Hemostatic materials are of great significance for rapid control of bleeding, especially in military trauma and traffic accidents. Chitosan (CS) hemostatic sponges have been widely concerned and studied due to their excellent biocompatibility. However, the hemostatic performance of pure chitosan sponges is poor due to the shortcoming of strong rigidity. In this study, CS and hydroxypropylmethylcellulose (HPMC) were combined to develop a safe and effective hemostatic composite sponges (CS/HPMC) for hemorrhage control by a simple mixed-lyophilization strategy. The CS/HPMC exhibited excellent flexibility (the flexibility was 74% higher than that of pure CS sponges). Due to the high porosity and procoagulant chemical structure of the CS/HPMC, it exhibited rapid hemostatic ability in vitro (BCI was shortened by 50% than that of pure CS sponges). The good biocompatibility of the obtained CS/HPMC was confirmed via cytotoxicity, hemocompatibility and skin irritation tests. The CS/HPMC can induced the erythrocyte and platelets adhesion, resulting in significant coagulation acceleration. The CS/HPMC had excellent performance in vivo assessments with shortest clotting time (40 s) and minimal blood loss (166 mg). All above results proved that the CS/HPMC had great potential to be a safe and rapid hemostatic material.}, }
@article {pmid37991789, year = {2023}, author = {Khan, S and Anderson, W and Constandinou, T}, title = {Surgical Implantation of Brain Computer Interfaces.}, journal = {JAMA surgery}, volume = {}, number = {}, pages = {}, doi = {10.1001/jamasurg.2023.2399}, pmid = {37991789}, issn = {2168-6262}, }
@article {pmid37991593, year = {2023}, author = {Mizuguchi, N}, title = {Candidate brain regions for motor imagery practice: a commentary on Rieger et al., 2023.}, journal = {Psychological research}, volume = {}, number = {}, pages = {}, pmid = {37991593}, issn = {1430-2772}, support = {23K10625//JSPS KAKENHI/ ; }, abstract = {The mechanism through which motor imagery practice improves motor performance remains unclear. In this special issue, Rieger et al. propose a model to explain why motor imagery practice improves motor performance. According to their model, motor imagery involves a comparison between intended and predicted action effects, allowing for the modification of the internal model upon detecting errors. I believe that the anterior cingulate cortex (ACC) is a candidate as a brain region responsible for comparing intended and predicted action effects. Evidence supports this hypothesis, as a previous study has observed error-related activity in the ACC preceding incorrect responses (i.e., commission errors) in the Go/No-go task (Bediou et al., 2012, Neuroimage). Therefore, the error-related activity can be induced without any feedback. This fact also sheds light on the mechanisms of brain-computer interface. I believe that this additional literature will enhance Rieger's model.}, }
@article {pmid37990998, year = {2023}, author = {Xu, F and Yan, Y and Zhu, J and Chen, X and Gao, L and Liu, Y and Shi, W and Lou, Y and Wang, W and Leng, J and Zhang, Y}, title = {Self-Supervised EEG Representation Learning with Contrastive Predictive Coding for Post-Stroke Patients.}, journal = {International journal of neural systems}, volume = {33}, number = {12}, pages = {2350066}, doi = {10.1142/S0129065723500661}, pmid = {37990998}, issn = {1793-6462}, mesh = {Humans ; *Imagination ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; Cognition ; }, abstract = {Stroke patients are prone to fatigue during the EEG acquisition procedure, and experiments have high requirements on cognition and physical limitations of subjects. Therefore, how to learn effective feature representation is very important. Deep learning networks have been widely used in motor imagery (MI) based brain-computer interface (BCI). This paper proposes a contrast predictive coding (CPC) framework based on the modified s-transform (MST) to generate MST-CPC feature representations. MST is used to acquire the temporal-frequency feature to improve the decoding performance for MI task recognition. EEG2Image is used to convert multi-channel one-dimensional EEG into two-dimensional EEG topography. High-level feature representations are generated by CPC which consists of an encoder and autoregressive model. Finally, the effectiveness of generated features is verified by the k-means clustering algorithm. It can be found that our model generates features with high efficiency and a good clustering effect. After classification performance evaluation, the average classification accuracy of MI tasks is 89% based on 40 subjects. The proposed method can obtain effective feature representations and improve the performance of MI-BCI systems. By comparing several self-supervised methods on the public dataset, it can be concluded that the MST-CPC model has the highest average accuracy. This is a breakthrough in the combination of self-supervised learning and image processing of EEG signals. It is helpful to provide effective rehabilitation training for stroke patients to promote motor function recovery.}, }
@article {pmid37990160, year = {2023}, author = {Colamarino, E and Lorusso, M and Pichiorri, F and Toppi, J and Tamburella, F and Serratore, G and Riccio, A and Tomaiuolo, F and Bigioni, A and Giove, F and Scivoletto, G and Cincotti, F and Mattia, D}, title = {DiSCIoser: unlocking recovery potential of arm sensorimotor functions after spinal cord injury by promoting activity-dependent brain plasticity by means of brain-computer interface technology: a randomized controlled trial to test efficacy.}, journal = {BMC neurology}, volume = {23}, number = {1}, pages = {414}, pmid = {37990160}, issn = {1471-2377}, abstract = {BACKGROUND: Traumatic cervical spinal cord injury (SCI) results in reduced sensorimotor abilities that strongly impact on the achievement of daily living activities involving hand/arm function. Among several technology-based rehabilitative approaches, Brain-Computer Interfaces (BCIs) which enable the modulation of electroencephalographic sensorimotor rhythms, are promising tools to promote the recovery of hand function after SCI. The "DiSCIoser" study proposes a BCI-supported motor imagery (MI) training to engage the sensorimotor system and thus facilitate the neuroplasticity to eventually optimize upper limb sensorimotor functional recovery in patients with SCI during the subacute phase, at the peak of brain and spinal plasticity. To this purpose, we have designed a BCI system fully compatible with a clinical setting whose efficacy in improving hand sensorimotor function outcomes in patients with traumatic cervical SCI will be assessed and compared to the hand MI training not supported by BCI.<br><br>METHODS: This randomized controlled trial will include 30 participants with traumatic cervical SCI in the subacute phase randomly assigned to 2 intervention groups: the BCI-assisted hand MI training and the hand MI training not supported by BCI. Both interventions are delivered (3 weekly sessions; 12 weeks) as add-on to standard rehabilitation care. A multidimensional assessment will be performed at: randomization/pre-intervention and post-intervention. Primary outcome measure is the Graded Redefined Assessment of Strength, Sensibility and Prehension (GRASSP) somatosensory sub-score. Secondary outcome measures include the motor and functional scores of the GRASSP and other clinical, neuropsychological, neurophysiological and neuroimaging measures.<br><br>DISCUSSION: We expect the BCI-based intervention to promote meaningful cortical sensorimotor plasticity and eventually maximize recovery of arm functions in traumatic cervical subacute SCI. This study will generate a body of knowledge that is fundamental to drive optimization of BCI application in SCI as a top-down therapeutic intervention, thus beyond the canonical use of BCI as assistive tool.<br><br>TRIAL REGISTRATION: Name of registry: DiSCIoser: improving arm sensorimotor functions after spinal cord injury via brain-computer interface training (DiSCIoser).<br><br>TRIAL REGISTRATION NUMBER: NCT05637775; registration date on the ClinicalTrial.gov platform: 05-12-2022.}, }
@article {pmid37986895, year = {2023}, author = {Wu, EG and Rudzite, AM and Bohlen, MO and Li, PH and Kling, A and Cooler, S and Rhoades, C and Brackbill, N and Gogliettino, AR and Shah, NP and Madugula, SS and Sher, A and Litke, AM and Field, GD and Chichilnisky, EJ}, title = {Decomposition of retinal ganglion cell electrical images for cell type and functional inference.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.11.06.565889}, pmid = {37986895}, abstract = {Identifying neuronal cell types and their biophysical properties based on their extracellular electrical features is a major challenge for experimental neuroscience and the development of high-resolution brain-machine interfaces. One example is identification of retinal ganglion cell (RGC) types and their visual response properties, which is fundamental for developing future electronic implants that can restore vision. The electrical image (EI) of a RGC, or the mean spatio-temporal voltage footprint of its recorded spikes on a high-density electrode array, contains substantial information about its anatomical, morphological, and functional properties. However, the analysis of these properties is complex because of the high-dimensional nature of the EI. We present a novel optimization-based algorithm to decompose electrical image into a low-dimensional, biophysically-based representation: the temporally-shifted superposition of three learned basis waveforms corresponding to spike waveforms produced in the somatic, dendritic and axonal cellular compartments. Large-scale multi-electrode recordings from the macaque retina were used to test the effectiveness of the decomposition. The decomposition accurately localized the somatic and dendritic compartments of the cell. The imputed dendritic fields of RGCs correctly predicted the location and shape of their visual receptive fields. The inferred waveform amplitudes and shapes accurately identified the four major primate RGC types (ON and OFF midget and parasol cells), a substantial advance. Together, these findings may contribute to more accurate inference of RGC types and their original light responses in the degenerated retina, with possible implications for other electrical imaging applications.}, }
@article {pmid37986883, year = {2023}, author = {Chen, K and Forrest, A and Gonzalez Burgos, G and Kozai, TDY}, title = {Neuronal functional connectivity is impaired in a layer dependent manner near the chronically implanted microelectrodes.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.11.06.565852}, pmid = {37986883}, abstract = {OBJECTIVE: This study aims to reveal longitudinal changes in functional network connectivity within and across different brain structures near the chronically implanted microelectrode. While it is well established that the foreign-body response (FBR) contributes to the gradual decline of the signals recorded from brain implants over time, how does the FBR impact affect the functional stability of neural circuits near implanted Brain-Computer Interfaces (BCIs) remains unknown. This research aims to illuminate how the chronic FBR can alter local neural circuit function and the implications for BCI decoders.<br><br>APPROACH: This study utilized multisite Michigan-style microelectrodes that span all cortical layers and the hippocampal CA1 region to collect spontaneous and visually-evoked electrophysiological activity. Alterations in neuronal activity near the microelectrode were tested assessing cross-frequency synchronization of LFP and spike entrainment to LFP oscillatory activity throughout 16 weeks after microelectrode implantation.<br><br>MAIN RESULTS: The study found that cortical layer 4, the input-receiving layer, maintained activity over the implantation time. However, layers 2/3 rapidly experienced severe impairment, leading to a loss of proper intralaminar connectivity in the downstream output layers 5/6. Furthermore, the impairment of interlaminar connectivity near the microelectrode was unidirectional, showing decreased connectivity from Layers 2/3 to Layers 5/6 but not the reverse direction. In the hippocampus, CA1 neurons gradually became unable to properly entrain to the surrounding LFP oscillations.<br><br>SIGNIFICANCE: This study provides a detailed characterization of network connectivity dysfunction over long-term microelectrode implantation periods. This new knowledge could contribute to the development of targeted therapeutic strategies aimed at improving the health of the tissue surrounding brain implants and potentially inform engineering of adaptive decoders as the FBR progresses. Our study's understanding of the dynamic changes in the functional network over time opens the door to developing interventions for improving the long-term stability and performance of intracortical microelectrodes.}, }
@article {pmid37986728, year = {2023}, author = {Fan, C and Hahn, N and Kamdar, F and Avansino, D and Wilson, GH and Hochberg, L and Shenoy, KV and Henderson, JM and Willett, FR}, title = {Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication.}, journal = {ArXiv}, volume = {}, number = {}, pages = {}, pmid = {37986728}, issn = {2331-8422}, abstract = {Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs.}, }
@article {pmid37984201, year = {2023}, author = {Borgheai, SB and Zisk, AH and McLinden, J and Mcintyre, J and Sadjadi, R and Shahriari, Y}, title = {Multimodal pre-screening can predict BCI performance variability: A novel subject-specific experimental scheme.}, journal = {Computers in biology and medicine}, volume = {168}, number = {}, pages = {107658}, doi = {10.1016/j.compbiomed.2023.107658}, pmid = {37984201}, issn = {1879-0534}, abstract = {BACKGROUND: Brain-computer interface (BCI) systems currently lack the required robustness for long-term daily use due to inter- and intra-subject performance variability. In this study, we propose a novel personalized scheme for a multimodal BCI system, primarily using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), to identify, predict, and compensate for factors affecting competence-related and interfering factors associated with performance.<br><br>METHOD: 11 (out of 13 recruited) participants, including five participants with motor deficits, completed four sessions on average. During the training sessions, the subjects performed a short pre-screening phase, followed by three variations of a novel visou-mental (VM) protocol. Features extracted from the pre-screening phase were used to construct predictive platforms using stepwise multivariate linear regression (MLR) models. In the test sessions, we employed a task-correction phase where our predictive models were used to predict the ideal task variation to maximize performance, followed by an interference-correction phase. We then investigated the associations between predicted and actual performances and evaluated the outcome of correction strategies.<br><br>RESULT: The predictive models resulted in respective adjusted R-squared values of 0.942, 0.724, and 0.939 for the first, second, and third variation of the task, respectively. The statistical analyses showed significant associations between the performances predicted by predictive models and the actual performances for the first two task variations, with rhos of 0.7289 (p-value&#xa0;=&#xa0;0.011) and 0.6970 (p-value&#xa0;=&#xa0;0.017), respectively. For 81.82&#xa0;% of the subjects, the task/workload correction stage correctly determined which task variation provided the highest accuracy, with an average performance gain of 5.18&#xa0;% when applying the correction strategies.<br><br>CONCLUSION: Our proposed method can lead to an integrated multimodal predictive framework to compensate for BCI performance variability, particularly, for people with severe motor deficits.}, }
@article {pmid37982955, year = {2023}, author = {Li, M and Li, J and Song, Z and Deng, H and Xu, J and Xu, G and Liao, W}, title = {EEGNet-based multi-source domain filter for BCI transfer learning.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {37982955}, issn = {1741-0444}, support = {F2021202003//Natural Science Foundation of Hebei Province/ ; EERI_OY2020004//State Key Laboratory of Reliability and Intelligence of Electrical Equipment/ ; EERI_OY202000//State Key Laboratory of Reliability and Intelligence of Electrical Equipment/ ; 19277752D//the Key Research and Development Foundation of Hebei/ ; 21372002D//the Key Research and Development Foundation of Hebei/ ; JBKYXX2007//the Technology Nova of Hebei University of Technology/ ; 51977060//National Natural Science Foundation of China/ ; }, abstract = {Deep learning has great potential on decoding EEG in brain-computer interface. While common deep learning algorithms cannot directly train models with data from multiple individuals because of the inter-individual differences in EEG. Collecting enough data for each subject to satisfy the training of deep learning would result in an increase in training cost. This study proposes a novel transfer learning, EEGNet-based multi-source domain filter for transfer learning (EEGNet-MDFTL), to reduce the amount of training data and improve the performance of BCI. The EEGNet-MDFTL uses bagging ensemble learning to learn domain-invariant features from the multi-source domain and utilizes model loss value to filter the multi-source domain. Compared with baseline methods, the accuracy of the EEGNet-MDFTL reaches 91.96%, higher than two state-of-the-art methods, which demonstrates source domain filter can select similar source domains to improve the accuracy of the model, and remains a high level even when the data amount is reduced to 1/8, proving that ensemble learning learns enough domain invariant features from the multi-source domain to make the model insensitive to data amount. The proposed EEGNet-MDFTL is effective in improving the decoding performance with a small amount of data, which is helpful to save the BCI training cost.}, }
@article {pmid37982637, year = {2023}, author = {Herring, EZ and Graczyk, EL and Memberg, WD and Adams, R and Fernandez Baca-Vaca, G and Hutchison, BC and Krall, JT and Alexander, BJ and Conlan, EC and Alfaro, KE and Bhat, P and Ketting-Olivier, AB and Haddix, CA and Taylor, DM and Tyler, DJ and Sweet, JA and Kirsch, RF and Ajiboye, AB and Miller, JP}, title = {Reconnecting the Hand and Arm to the Brain: Efficacy of Neural Interfaces for Sensorimotor Restoration After Tetraplegia.}, journal = {Neurosurgery}, volume = {}, number = {}, pages = {}, doi = {10.1227/neu.0000000000002769}, pmid = {37982637}, issn = {1524-4040}, support = {Clinical Trial Award SC180308//Congressionally Directed Medical Research Programs/ ; }, abstract = {BACKGROUND AND OBJECTIVES: Paralysis after spinal cord injury involves damage to pathways that connect neurons in the brain to peripheral nerves in the limbs. Re-establishing this communication using neural interfaces has the potential to bridge the gap and restore upper extremity function to people with high tetraplegia. We report a novel approach for restoring upper extremity function using selective peripheral nerve stimulation controlled by intracortical microelectrode recordings from sensorimotor networks, along with restoration of tactile sensation of the hand using intracortical microstimulation.<br><br>METHODS: A 27-year-old right-handed man with AIS-B (motor-complete, sensory-incomplete) C3-C4 tetraplegia was enrolled into the clinical trial. Six 64-channel intracortical microelectrode arrays were implanted into left hemisphere regions involved in upper extremity function, including primary motor and sensory cortices, inferior frontal gyrus, and anterior intraparietal area. Nine 16-channel extraneural peripheral nerve electrodes were implanted to allow targeted stimulation of right median, ulnar (2), radial, axillary, musculocutaneous, suprascapular, lateral pectoral, and long thoracic nerves, to produce selective muscle contractions on demand. Proof-of-concept studies were performed to demonstrate feasibility of using a brain-machine interface to read from and write to the brain for restoring motor and sensory functions of the participant's own arm and hand.<br><br>RESULTS: Multiunit neural activity that correlated with intended motor action was successfully recorded from intracortical arrays. Microstimulation of electrodes in somatosensory cortex produced repeatable sensory percepts of individual fingers for restoration of touch sensation. Selective electrical activation of peripheral nerves produced antigravity muscle contractions, resulting in functional movements that the participant was able to command under brain control to perform virtual and actual arm and hand movements. The system was well tolerated with no operative complications.<br><br>CONCLUSION: The combination of implanted cortical electrodes and nerve cuff electrodes has the potential to create bidirectional restoration of motor and sensory functions of the arm and hand after neurological injury.}, }
@article {pmid37982231, year = {2023}, author = {Quanyu, W and Sheng, D and Weige, T and Lingjiao, P and Xiaojie, L}, title = {Research on MI EEG signal classification algorithm using multi-model fusion strategy coupling.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-10}, doi = {10.1080/10255842.2023.2284091}, pmid = {37982231}, issn = {1476-8259}, abstract = {To enhance the accuracy of motor imagery(MI)EEG signal recognition, two methods, namely power spectral density and wavelet packet decomposition combined with a common spatial pattern, were employed to explore the feature information in depth in MI EEG signals. The extracted MI EEG signal features were subjected to series feature fusion, and the F-test method was used to select features with higher information content. Here regarding the accuracy of MI EEG signal classification, we further proposed the Platt Scaling probability calibration method was used to calibrate the results obtained from six basic classifiers, namely random forest (RF), support vector machines (SVM), Logistic Regression (LR), Gaussian naïve bayes (GNB), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). From these 12 classifiers, three to four with higher accuracy were selected for model fusion. The proposed method was validated on Datasets 2a of the 4th International BCI Competition, achieving an average accuracy of MI EEG data of nine subjects reached 91.46%, which indicates that model fusion was an effective method to improve classification accuracy, and provides some reference value for the research on MI brain-machine interface.}, }
@article {pmid37980798, year = {2023}, author = {Ali, O and Saif-Ur-Rehman, M and Glasmachers, T and Iossifidis, I and Klaes, C}, title = {ConTraNet: A hybrid network for improving the classification of EEG and EMG signals with limited training data.}, journal = {Computers in biology and medicine}, volume = {168}, number = {}, pages = {107649}, doi = {10.1016/j.compbiomed.2023.107649}, pmid = {37980798}, issn = {1879-0534}, abstract = {OBJECTIVE: Bio-Signals such as electroencephalography (EEG) and electromyography (EMG) are widely used for the rehabilitation of physically disabled people and for the characterization of cognitive impairments. Successful decoding of these bio-signals is however non-trivial because of the time-varying and non-stationary characteristics. Furthermore, existence of short- and long-range dependencies in these time-series signal makes the decoding even more challenging. State-of-the-art studies proposed Convolutional Neural Networks (CNNs) based architectures for the classification of these bio-signals, which are proven useful to learn spatial representations. However, CNNs because of the fixed size convolutional kernels and shared weights pay only uniform attention and are also suboptimal in learning short-long term dependencies, simultaneously, which could be pivotal in decoding EEG and EMG signals. Therefore, it is important to address these limitations of CNNs. To learn short- and long-range dependencies simultaneously and to pay more attention to more relevant part of the input signal, Transformer neural network-based architectures can play a significant role. Nonetheless, it requires a large corpus of training data. However, EEG and EMG decoding studies produce limited amount of the data. Therefore, using standalone transformers neural networks produce ordinary results. In this study, we ask a question whether we can fix the limitations of CNN and transformer neural networks and provide a robust and generalized model that can simultaneously learn spatial patterns, long-short term dependencies, pay variable amount of attention to time-varying non-stationary input signal with limited training data.<br><br>APPROACH: In this work, we introduce a novel single hybrid model called ConTraNet, which is based on CNN and Transformer architectures that contains the strengths of both CNN and Transformer neural networks. ConTraNet uses a CNN block to introduce inductive bias in the model and learn local dependencies, whereas the Transformer block uses the self-attention mechanism to learn the short- and long-range or global dependencies in the signal and learn to pay different attention to different parts of the signals.<br><br>MAIN RESULTS: We evaluated and compared the ConTraNet with state-of-the-art methods on four publicly available datasets (BCI Competition IV dataset 2b, Physionet MI-EEG dataset, Mendeley sEMG dataset, Mendeley sEMG V1 dataset) which belong to EEG-HMI and EMG-HMI paradigms. ConTraNet outperformed its counterparts in all the different category tasks (2-class, 3-class, 4-class, 7-class, and 10-class decoding tasks).<br><br>SIGNIFICANCE: With limited training data ConTraNet significantly improves classification performance on four publicly available datasets for 2, 3, 4, 7, and 10-classes compared to its counterparts.}, }
@article {pmid37980536, year = {2023}, author = {Canny, E and Vansteensel, MJ and van der Salm, SMA and Müller-Putz, GR and Berezutskaya, J}, title = {Boosting brain-computer interfaces with functional electrical stimulation: potential applications in people with locked-in syndrome.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {20}, number = {1}, pages = {157}, pmid = {37980536}, issn = {1743-0003}, support = {OCENW.XS22.4.118//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; User-Computer Interface ; *Locked-In Syndrome ; Paralysis ; Electric Stimulation ; Brain/physiology ; }, abstract = {Individuals with a locked-in state live with severe whole-body paralysis that limits their ability to communicate with family and loved ones. Recent advances in brain-computer interface (BCI) technology have presented a potential alternative for these people to communicate by detecting neural activity associated with attempted hand or speech movements and translating the decoded intended movements to a control signal for a computer. A technique that could potentially enrich the communication capacity of BCIs is functional electrical stimulation (FES) of paralyzed limbs and face to restore body and facial movements of paralyzed individuals, allowing to add body language and facial expression to communication BCI utterances. Here, we review the current state of the art of existing BCI and FES work in people with paralysis of body and face and propose that a combined BCI-FES approach, which has already proved successful in several applications in stroke and spinal cord injury, can provide a novel promising mode of communication for locked-in individuals.}, }
@article {pmid37980496, year = {2023}, author = {Tanamachi, K and Kuwahara, W and Okawada, M and Sasaki, S and Kaneko, F}, title = {Relationship between resting-state functional connectivity and change in motor function after motor imagery intervention in patients with stroke: a scoping review.}, journal = {Journal of neuroengineering and rehabilitation}, volume = {20}, number = {1}, pages = {159}, pmid = {37980496}, issn = {1743-0003}, support = {23H00458//JSPS KAKENHI/ ; }, mesh = {Humans ; *Stroke ; Brain ; Imagery, Psychotherapy/methods ; *Stroke Rehabilitation/methods ; Recovery of Function/physiology ; }, abstract = {BACKGROUND: In clinical practice, motor imagery has been proposed as a treatment modality for stroke owing to its feasibility in patients with severe motor impairment. Motor imagery-based interventions can be categorized as open- or closed-loop. Closed-loop intervention is based on voluntary motor imagery and induced peripheral sensory afferent (e.g., Brain Computer Interface (BCI)-based interventions). Meanwhile, open-loop interventions include methods without voluntary motor imagery or sensory afferent. Resting-state functional connectivity (rs-FC) is defined as a significant temporal correlated signal among functionally related brain regions without any stimulus. rs-FC is a powerful tool for exploring the baseline characteristics of brain connectivity. Previous studies reported changes in rs-FC after motor imagery interventions. Systematic reviews also reported the effects of motor imagery-based interventions at the behavioral level. This study aimed to review and describe the relationship between the improvement in motor function and changes in rs-FC after motor imagery in patients with stroke.<br><br>REVIEW PROCESS: The literature review was based on Arksey and O'Malley's framework. PubMed, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, and Web of Science were searched up to September 30, 2023. The included studies covered the following topics: illusion without voluntary action, motor imagery, action imitation, and BCI-based interventions. The correlation between rs-FC and motor function before and after the intervention was analyzed. After screening by two independent researchers, 13 studies on BCI-based intervention, motor imagery intervention, and kinesthetic illusion induced by visual stimulation therapy were included.<br><br>CONCLUSION: All studies relating to motor imagery in this review reported improvement in motor function post-intervention. Furthermore, all those studies demonstrated a significant relationship between the change in motor function and rs-FC (e.g., sensorimotor network and parietal cortex).}, }
@article {pmid37979923, year = {2023}, author = {Sengupta, P and Lakshminarayanan, K}, title = {Cortical Activation and BCI Performance during Brief Vibrotactile Tactile Imagery: A Comparative Study with Motor Imagery.}, journal = {Behavioural brain research}, volume = {}, number = {}, pages = {114760}, doi = {10.1016/j.bbr.2023.114760}, pmid = {37979923}, issn = {1872-7549}, abstract = {Brain-computer interfaces (BCIs) rely heavily on motor imagery (MI) for operation, yet tactile imagery (TI) presents a novel approach that may be advantageous in situations where visual feedback is impractical. The current study aimed to compare the cortical activity and digit classification performance induced by TI and MI to assess the viability of TI for use in BCIs. Twelve right-handed participants engaged in trials of TI and MI, focusing on their left and right index digits. Event-related desynchronization (ERD) in the mu and beta bands was analyzed, and classification accuracy was determined through an artificial neural network (ANN). Comparable ERD patterns were observed in both TI and MI, with significant decreases in ERD during imagery tasks. The ANN demonstrated high classification accuracy, with TI achieving a mean±SD of 79.30±3.91% and MI achieving 81.10±2.96%, with no significant difference between the two (p=0.11). The study found that TI induces substantial ERD comparable to MI and maintains high classification accuracy, supporting its potential as an effective mental strategy for BCIs. This suggests that TI could be a valuable alternative in BCI applications, particularly for individuals unable to rely on visual cues.}, }
@article {pmid37979051, year = {2023}, author = {Ousingsawat, J and Centeio, R and Schreiber, R and Kunzelmann, K}, title = {Niclosamide, but not ivermectin, inhibits anoctamin 1 and 6 and attenuates inflammation of the respiratory tract.}, journal = {Pflugers Archiv : European journal of physiology}, volume = {}, number = {}, pages = {}, pmid = {37979051}, issn = {1432-2013}, abstract = {Inflammatory airway diseases like cystic fibrosis, asthma and COVID-19 are characterized by high levels of pulmonary cytokines. Two well-established antiparasitic drugs, niclosamide and ivermectin, are intensively discussed for the treatment of viral inflammatory airway infections. Here, we examined these repurposed drugs with respect to their anti-inflammatory effects in airways in vivo and in vitro. Niclosamide reduced mucus content, eosinophilic infiltration and cell death in asthmatic mouse lungs in vivo and inhibited release of interleukins in the two differentiated airway epithelial cell lines CFBE and BCi-NS1.1 in vitro. Cytokine release was also inhibited by the knockdown of the Ca[2+]-activated Cl[-] channel anoctamin 1 (ANO1, TMEM16A) and the phospholipid scramblase anoctamin 6 (ANO6, TMEM16F), which have previously been shown to affect intracellular Ca[2+] levels near the plasma membrane and to facilitate exocytosis. At concentrations around 200 nM, niclosamide inhibited inflammation, lowered intracellular Ca[2+], acidified cytosolic pH and blocked activation of ANO1 and ANO6. It is suggested that niclosamide brings about its anti-inflammatory effects at least in part by inhibiting ANO1 and ANO6, and by lowering intracellular Ca[2+] levels. In contrast to niclosamide, 1 µM ivermectin did not exert any of the effects described for niclosamide. The present data suggest niclosamide as an effective anti-inflammatory treatment in CF, asthma, and COVID-19, in addition to its previously reported antiviral effects. It has an advantageous concentration-response relationship and is known to be well tolerated.}, }
@article {pmid37978295, year = {2023}, author = {Wang, DX and Dong, ZJ and Deng, SX and Tian, YM and Xiao, YJ and Li, X and Ma, XR and Li, L and Li, P and Chang, HZ and Liu, L and Wang, F and Wu, Y and Gao, X and Zheng, SS and Gu, HM and Zhang, YN and Wu, JB and Wu, F and Peng, Y and Zhang, XW and Zhan, RY and Gao, LX and Sun, Q and Guo, X and Zhao, XD and Luo, JH and Zhou, R and Han, L and Shu, Y and Zhao, JW}, title = {GDF11 slows excitatory neuronal senescence and brain ageing by repressing p21.}, journal = {Nature communications}, volume = {14}, number = {1}, pages = {7476}, pmid = {37978295}, issn = {2041-1723}, support = {81971144//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {As a major neuron type in the brain, the excitatory neuron (EN) regulates the lifespan in C. elegans. How the EN acquires senescence, however, is unknown. Here, we show that growth differentiation factor 11 (GDF11) is predominantly expressed in the EN in the adult mouse, marmoset and human brain. In mice, selective knock-out of GDF11 in the post-mitotic EN shapes the brain ageing-related transcriptional profile, induces EN senescence and hyperexcitability, prunes their dendrites, impedes their synaptic input, impairs object recognition memory and shortens the lifespan, establishing a functional link between GDF11, brain ageing and cognition. In vitro GDF11 deletion causes cellular senescence in Neuro-2a cells. Mechanistically, GDF11 deletion induces neuronal senescence via Smad2-induced transcription of the pro-senescence factor p21. This work indicates that endogenous GDF11 acts as a brake on EN senescence and brain ageing.}, }
@article {pmid37978205, year = {2023}, author = {Iwane, F and Billard, A and Millán, JDR}, title = {Inferring individual evaluation criteria for reaching trajectories with obstacle avoidance from EEG signals.}, journal = {Scientific reports}, volume = {13}, number = {1}, pages = {20163}, pmid = {37978205}, issn = {2045-2322}, support = {16070//Hasler Stiftung/ ; }, mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Evoked Potentials/physiology ; Brain ; Algorithms ; }, abstract = {During reaching actions, the human central nerve system (CNS) generates the trajectories that optimize effort and time. When there is an obstacle in the path, we make sure that our arm passes the obstacle with a sufficient margin. This comfort margin varies between individuals. When passing a fragile object, risk-averse individuals may adopt a larger margin by following the longer path than risk-prone people do. However, it is not known whether this variation is associated with a personalized cost function used for the individual optimal control policies and how it is represented in our brain activity. This study investigates whether such individual variations in evaluation criteria during reaching results from differentiated weighting given to energy minimization versus comfort, and monitors brain error-related potentials (ErrPs) evoked when subjects observe a robot moving dangerously close to a fragile object. Seventeen healthy participants monitored a robot performing safe, daring and unsafe trajectories around a wine glass. Each participant displayed distinct evaluation criteria on the energy efficiency and comfort of robot trajectories. The ErrP-BCI outputs successfully inferred such individual variation. This study suggests that ErrPs could be used in conjunction with an optimal control approach to identify the personalized cost used by CNS. It further opens new avenues for the use of brain-evoked potential to train assistive robotic devices through the use of neuroprosthetic interfaces.}, }
@article {pmid37974976, year = {2023}, author = {Karikari, E and Koshechkin, KA}, title = {Review on brain-computer interface technologies in healthcare.}, journal = {Biophysical reviews}, volume = {15}, number = {5}, pages = {1351-1358}, pmid = {37974976}, issn = {1867-2450}, abstract = {Brain-computer interface (BCI) technologies have developed as a game changer, altering how humans interact with computers and opening up new avenues for understanding and utilizing the power of the human brain. The goal of this research study is to assess recent breakthroughs in BCI technologies and their future prospects. The paper starts with an outline of the fundamental concepts and principles that underpin BCI technologies. It examines the many forms of BCIs, including as invasive, partially invasive, and non-invasive interfaces, emphasizing their advantages and disadvantages. The progress of BCI hardware and signal processing techniques is investigated, with a focus on the shift from bulky and invasive systems to more portable and user-friendly options. Following that, the article delves into the important advances in BCI applications across several fields. It investigates the use of BCIs in healthcare, particularly in neurorehabilitation, assistive technology, and cognitive enhancement. BCIs' potential for boosting human capacities such as communication, motor control, and sensory perception is being thoroughly researched. Furthermore, the article investigates developing BCI applications in gaming, entertainment, and virtual reality, demonstrating how BCI technologies are growing outside medical and therapeutic settings. The study also gives light on the problems and limits that prevent BCIs from being widely adopted. Ethical concerns about privacy, data security, and informed permission are addressed, highlighting the importance of strong legislative frameworks to enable responsible and ethical usage of BCI technologies. Furthermore, the study delves into technological issues such as increasing signal resolution and precision, increasing system reliability, and enabling smooth connection with existing technology. Finally, this study paper gives an in-depth examination of the advances and future possibilities of BCI technologies. It emphasizes the transformative influence of BCIs on human-computer interaction and their potential to alter healthcare, gaming, and other industries. This research intends to stimulate further innovation and progress in the field of brain-computer interfaces by addressing problems and imagining future possibilities.}, }
@article {pmid37974580, year = {2023}, author = {Choi, YJ and Kwon, OS and Kim, SP}, title = {Design of auditory P300-based brain-computer interfaces with a single auditory channel and no visual support.}, journal = {Cognitive neurodynamics}, volume = {17}, number = {6}, pages = {1401-1416}, pmid = {37974580}, issn = {1871-4080}, abstract = {UNLABELLED: Non-invasive brain-computer interfaces (BCIs) based on an event-related potential (ERP) component, P300, elicited via the oddball paradigm, have been extensively developed to enable device control and communication. While most P300-based BCIs employ visual stimuli in the oddball paradigm, auditory P300-based BCIs also need to be developed for users with unreliable gaze control or limited visual processing. Specifically, auditory BCIs without additional visual support or multi-channel sound sources can broaden the application areas of BCIs. This study aimed to design optimal stimuli for auditory BCIs among artificial (e.g., beep) and natural (e.g., human voice and animal sounds) sounds in such circumstances. In addition, it aimed to investigate differences between auditory and visual stimulations for online P300-based BCIs. As a result, natural sounds led to both higher online BCI performance and larger differences in ERP amplitudes between the target and non-target compared to artificial sounds. However, no single type of sound offered the best performance for all subjects; rather, each subject indicated different preferences between the human voice and animal sound. In line with previous reports, visual stimuli yielded higher BCI performance (average 77.56%) than auditory counterparts (average 54.67%). In addition, spatiotemporal patterns of the differences in ERP amplitudes between target and non-target were more dynamic with visual stimuli than with auditory stimuli. The results suggest that selecting a natural auditory stimulus optimal for individual users as well as making differences in ERP amplitudes between target and non-target stimuli more dynamic may further improve auditory P300-based BCIs.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-022-09901-3.}, }
@article {pmid37974284, year = {2023}, author = {Cipriani, M and Pichiorri, F and Colamarino, E and Toppi, J and Tamburella, F and Lorusso, M and Bigioni, A and Morone, G and Tomaiuolo, F and Santoro, F and Cordella, D and Molinari, M and Cincotti, F and Mattia, D and Puopolo, M}, title = {The Promotoer, a brain-computer interface-assisted intervention to promote upper limb functional motor recovery after stroke: a statistical analysis plan for a randomized controlled trial.}, journal = {Trials}, volume = {24}, number = {1}, pages = {736}, pmid = {37974284}, issn = {1745-6215}, support = {RF-2018-12365210//Ministero della Salute/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Recovery of Function/physiology ; *Stroke Rehabilitation/methods ; Pilot Projects ; *Stroke/diagnosis/therapy/complications ; Upper Extremity ; }, abstract = {BACKGROUND: Electroencephalography (EEG)-based brain-computer interfaces (BCIs) allow to modulate the sensorimotor rhythms and are emerging technologies for promoting post-stroke motor function recovery. The Promotoer study aims to assess the short and long-term efficacy of the Promotoer system, an EEG-based BCI assisting motor imagery (MI) practice, in enhancing post-stroke functional hand motor recovery. This paper details the statistical analysis plan of the Promotoer study.<br><br>METHODS: The Promotoer study is a randomized, controlled, assessor-blinded, single-centre, superiority trial, with two parallel groups and a 1:1 allocation ratio. Subacute stroke patients are randomized to EEG-based BCI-assisted MI training or to MI training alone (i.e. no BCI). An internal pilot study for sample size re-assessment is planned. The primary outcome is the effectiveness of the Upper Extremity Fugl-Meyer Assessment (UE-FMA) score. Secondary outcomes include clinical, functional, and user experience scores assessed at the end of intervention and at follow-up. Neurophysiological assessments are also planned. Effectiveness formulas have been specified, and intention-to-treat and per-protocol populations have been defined. Statistical methods for comparisons of groups and for development of a predictive score of significant improvement are described. Explorative subgroup analyses and methodology to handle missing data are considered.<br><br>DISCUSSION: The Promotoer study will provide robust evidence for the short/long-term efficacy of the Promotoer system in subacute stroke patients undergoing a rehabilitation program. Moreover, the development of a predictive score of response will allow transferring of the Promotoer system to optimal clinical practice. By carefully describing the statistical principles and procedures, the statistical analysis plan provides transparency in the analysis of data.<br><br>TRIAL REGISTRATION: ClinicalTrials.gov NCT04353297 . Registered on April 15, 2020.}, }
@article {pmid37972395, year = {2023}, author = {Wu, Y and Li, BZ and Wang, L and Fan, S and Chen, C and Li, A and Lin, Q and Wang, P}, title = {An unsupervised real-time spike sorting system based on optimized OSort.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad0d15}, pmid = {37972395}, issn = {1741-2552}, abstract = {OBJECTIVE: The OSort algorithm, a pivotal unsupervised spike sorting method, has been implemented in dedicated hardware devices for real-time spike sorting. However, due to the inherent complexity of neural recording environments, OSort still grapples with numerous transient cluster occurrences during the practical sorting process. This leads to substantial memory usage, heavy computational load, and complex hardware architectures, especially in noisy recordings and multi-channel systems.<br><br>APPROACH: This study introduces an optimized OSort algorithm (opt-OSort) which utilizes correlation coefficient (CC), instead of Euclidean distance as classification criterion. The CC method not only bolsters the robustness of spike classification amidst the diverse and ever-changing conditions of physiological and recording noise environments, but also can finish the entire sorting procedure within a fixed number of cluster slots, thus preventing a large number of transient clusters. Moreover, the opt-OSort incorporates two configurable validation loops to efficiently reject cluster outliers and track recording variations caused by electrode drifting in real-time.<br><br>MAIN RESULTS: The opt-OSort significantly reduces transient cluster occurrences by two orders of magnitude and decreases memory usage by 2.5 to 80 times in the number of pre-allocated transient clusters compared with other hardware implementations of OSort. The opt-OSort maintains an accuracy comparable to offline OSort and other commonly-used algorithms, with a sorting time of 0.68 &#xb5;s as measured by the hardware-implemented system in both simulated datasets and experimental data. The opt-OSort's ability to handle variations in neural activity caused by electrode drifting is also demonstrated.<br><br>SIGNIFICANCE: These results present a rapid, precise, and robust spike sorting solution suitable for integration into low-power, portable, closed-loop neural control systems and brain-computer interfaces.}, }
@article {pmid37971908, year = {2023}, author = {Jiang, X and Meng, L and Wang, Z and Wu, D}, title = {Deep Source Semi-Supervised Transfer Learning (DS3TL) for Cross-Subject EEG Classification.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2023.3333327}, pmid = {37971908}, issn = {1558-2531}, abstract = {OBJECTIVE: An electroencephalogram (EEG) based brain-computer interface (BCI) maps the user's EEG signals into commands for external device control. Usually a large amount of labeled EEG trials are required to train a reliable EEG recognition model. However, acquiring labeled EEG data is time-consuming and user-unfriendly. Semi-supervised learning (SSL) and transfer learning can be used to exploit the unlabeled data and the auxiliary data, respectively, to reduce the amount of labeled data for a new subject.<br><br>METHODS: This paper proposes deep source semi-supervised transfer learning (DS3TL) for EEG-based BCIs, which assumes the source subject has a small number of labeled EEG trials and a large number of unlabeled ones, whereas all EEG trials from the target subject are unlabeled. DS3TL mainly includes a hybrid SSL module, a weakly-supervised contrastive module, and a domain adaptation module. The hybrid SSL module integrates pseudo-labeling and consistency regularization for SSL. The weakly-supervised contrastive module performs contrastive learning by using the true labels of the labeled data and the pseudo-labels of the unlabeled data. The domain adaptation module reduces the individual differences by uncertainty reduction.<br><br>RESULTS: Experiments on three EEG datasets from different tasks demonstrated that DS3TL outperformed a supervised learning baseline with many more labeled training data, and multiple state-of-the-art SSL approaches with the same number of labeled data.<br><br>SIGNIFICANCE: To our knowledge, this is the first approach in EEG-based BCIs that exploits the unlabeled source data for more accurate target classifier training.}, }
@article {pmid37968802, year = {2023}, author = {Biffl, WL and Fawley, JA and Mohan, RC}, title = {Diagnosis and Management of Blunt Cardiac Injury: What You Need to Know.}, journal = {The journal of trauma and acute care surgery}, volume = {}, number = {}, pages = {}, doi = {10.1097/TA.0000000000004216}, pmid = {37968802}, issn = {2163-0763}, abstract = {Blunt cardiac injury (BCI) encompasses a wide spectrum, from occult and inconsequential contusion to rapidly fatal cardiac rupture. A small percentage of patients present with abnormal electrocardiogram (ECG) or shock, but most are initially asymptomatic. The potential for sudden dysrhythmia or cardiac pump failure mandates consideration of the presence of BCI, including appropriate monitoring and management. In this review we will present what you need to know to diagnose and manage BCI.}, }
@article {pmid37965214, year = {2023}, author = {Xu, F and Ming, D and Jung, TP and Xu, P and Xu, M}, title = {Editorial: The application of artificial intelligence in brain-computer interface and neural system rehabilitation.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1290961}, doi = {10.3389/fnins.2023.1290961}, pmid = {37965214}, issn = {1662-4548}, }
@article {pmid37678222, year = {2023}, author = {Agarwal, AK and Roy-Chaudhury, P and Mounts, P and Hurlburt, E and Pfaffle, A and Poggio, EC}, title = {Taurolidine/Heparin Lock Solution and Catheter-Related Bloodstream Infection in Hemodialysis: A Randomized, Double-Blind, Active-Control, Phase 3 Study.}, journal = {Clinical journal of the American Society of Nephrology : CJASN}, volume = {18}, number = {11}, pages = {1446-1455}, pmid = {37678222}, issn = {1555-905X}, mesh = {Adult ; Humans ; *Catheter-Related Infections/etiology ; Heparin/adverse effects ; *Central Venous Catheters/adverse effects ; Renal Dialysis/adverse effects ; *Sepsis/etiology ; *Catheterization, Central Venous/adverse effects ; }, abstract = {BACKGROUND: Catheter-related bloodstream infections (CRBSIs) are one of the most prevalent, fatal, and costly complications of hemodialysis with a central venous catheter (CVC). The LOCK IT-100 trial compared the efficacy and safety of a taurolidine/heparin catheter lock solution that combines taurolidine 13.5 mg/ml and heparin (1000 units/ml) versus heparin in preventing CRBSIs in participants receiving hemodialysis via CVC.<br><br>METHODS: LOCK IT-100 was a randomized, double-blind, active-control, multicenter, phase 3 study that enrolled adults with kidney failure undergoing maintenance hemodialysis via CVC from 70 US sites. Participants were randomized 1:1 to taurolidine/heparin catheter lock solution or heparin control catheter lock solution (1000 units/ml). The primary end point was time to CRBSI as assessed by a blinded Clinical Adjudication Committee. Secondary end points were catheter removal for any reason and loss of catheter patency. On the basis of a prespecified interim analysis, the Data and Safety Monitoring Board recommended terminating the trial early for efficacy with no safety concerns.<br><br>RESULTS: In the full analysis population (N =795), nine participants in the taurolidine/heparin arm (n =397; 2%) and 32 participants in the heparin arm (n =398; 8%) had a CRBSI. Event rates per 1000 catheter days were 0.13 and 0.46, respectively, with the difference in time to CRBSI being statistically significant, favoring taurolidine/heparin (P < 0.001). The hazard ratio was 0.29 (95% confidence interval, 0.14 to 0.62), corresponding to a 71% reduction in risk of CRBSIs with taurolidine/heparin versus heparin. There were no significant differences between study arms in time to catheter removal for any reason or loss of catheter patency. The safety of taurolidine/heparin was comparable with that of heparin, and most treatment-emergent adverse events were mild or moderate.<br><br>CONCLUSIONS: Taurolidine/heparin reduced the risk of developing a CRBSI in study participants receiving hemodialysis via CVC compared with heparin with a comparable safety profile.<br><br>Study Assessing Safety &amp; Effectiveness of a Catheter Lock Solution in Dialysis Patients to Prevent Bloodstream Infection, NCT02651428 .}, }
@article {pmid37964432, year = {2023}, author = {Okatan, M and Kocatürk, M}, title = {Decoding the Spike-Band Subthreshold Motor Cortical Activity.}, journal = {Journal of motor behavior}, volume = {}, number = {}, pages = {1-23}, doi = {10.1080/00222895.2023.2280263}, pmid = {37964432}, issn = {1940-1027}, abstract = {Intracortical Brain-Computer Interfaces (iBCI) use single-unit activity (SUA), multiunit activity (MUA) and local field potentials (LFP) to control neuroprosthetic devices. SUA and MUA are usually extracted from the bandpassed recording through amplitude thresholding, while subthreshold data are ignored. Here, we show that subthreshold data can actually be decoded to determine behavioral variables with test set accuracy of up to 100%. Although the utility of SUA, MUA and LFP for decoding behavioral variables has been explored previously, this study investigates the utility of spike-band subthreshold activity exclusively. We provide evidence suggesting that this activity can be used to keep decoding performance at acceptable levels even when SUA quality is reduced over time. To the best of our knowledge, the signals that we derive from the subthreshold activity may be the weakest neural signals that have ever been extracted from extracellular neural recordings, while still being decodable with test set accuracy of up to 100%. These results are relevant for the development of fully data-driven and automated methods for amplitude thresholding spike-band extracellular neural recordings in iBCIs containing thousands of electrodes.}, }
@article {pmid37963394, year = {2023}, author = {Miao, M and Yang, Z and Zeng, H and Zhang, W and Xu, B and Hu, W}, title = {Explainable cross-task adaptive transfer learning for motor imagery EEG classification.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad0c61}, pmid = {37963394}, issn = {1741-2552}, abstract = {OBJECTIVE: In the field of motor imagery (MI) electroencephalography (EEG) based brain-computer interfaces (BCIs), deep transfer learning (TL) has proven to be an effective tool for solving the problem of limited availability of subject-specific data for training of robust deep learning (DL) models. Despite considerable progress has been made in cross-subject/session and cross-device scenarios, the more challenging problem of cross-task deep TL still remains largely unexplored.<br><br>APPROACH: We propose a novel explainable cross-task adaptive TL method for MI EEG decoding. Firstly, similarity analysis and data alignment are performed for EEG data of motor execution (ME) and MI tasks. Afterward, the MI EEG decoding model is obtained via pre-training with extensive ME EEG data and fine-tuning with partial MI EEG data. Finally, expected gradients based post-hoc explainability analysis is conducted for visualization of important temporal-spatial features.<br><br>MAIN RESULTS: Extensive experiments are conducted on one large ME EEG dataset (HGD) and two large MI EEG datasets (openBMI and GIST). The best average classification accuracy of our method reaches 80.00% and 72.73% for openBMI and GIST respectively, which outperforms several state-of-the-art algorithms. Besides, the results of explainability analysis further validate the correlation between ME and MI EEG data and effectiveness of ME/MI cross-task adaptation.<br><br>SIGNIFICANCE: This paper confirms that decoding of MI EEG can be well facilitated by pre-existing ME EEG data, which largely relaxes the constraint of training samples for MI EEG decoding and has important practical sense.}, }
@article {pmid37961808, year = {2023}, author = {Liu, M and Jiang, N and Shi, Y and Wang, P and Zhuang, L}, title = {Spatiotemporal coding of natural odors in the olfactory bulb.}, journal = {Journal of Zhejiang University. Science. B}, volume = {24}, number = {11}, pages = {1057-1061}, pmid = {37961808}, issn = {1862-1783}, support = {LY21C100001 and LBY21H180001//the Zhejiang Provincial Natural Science Foundation of China/ ; 62271443 and 32250008//the National Natural Science Foundation of China/ ; }, mesh = {*Olfactory Bulb ; *Odorants ; Smell ; }, abstract = {气味是评价食品新鲜度最重要的参数之一。当气味以其自然浓度存在时，会在嗅觉系统中引发不同的神经活动模式。本研究提出了一种通过检测食物气味进行食物检测与评价的在体生物传感系统。我们通过将多通道微电极植入在清醒大鼠嗅球的僧帽/丛状细胞层上，进而对神经信号进行实时检测。结果表明，不同的气味可以引起不同的神经振荡活动，每个僧帽/丛状细胞会表现出特定气味的锋电位发放模式。单个大鼠的少量细胞携带足够的信息，可以根据锋电位发放频率变化率的极坐标图来区分不同储存天数的食物。此外，研究表明气味刺激后，β振荡比γ振荡表现出更特异的气味响应模式，这表明β振荡在气味识别中起着更重要的作用。综上，本研究提出的在体神经接口为评估食品新鲜度提供了一种可行性方法。}, }
@article {pmid37961227, year = {2023}, author = {Francioni, V and Tang, VD and Brown, NJ and Toloza, EHS and Harnett, M}, title = {Vectorized instructive signals in cortical dendrites during a brain-computer interface task.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {37961227}, abstract = {Backpropagation of error is the most widely used learning algorithm in artificial neural networks, forming the backbone of modern machine learning and artificial intelligence[1,2]. Backpropagation provides a solution to the credit assignment problem by vectorizing an error signal tailored to individual neurons. Recent theoretical models have suggested that neural circuits could implement backpropagation-like learning by semi-independently processing feedforward and feedback information streams in separate dendritic compartments[3-7]. This presents a compelling, but untested, hypothesis for how cortical circuits could solve credit assignment in the brain. We designed a neurofeedback brain-computer interface (BCI) task with an experimenter-defined reward function to evaluate the key requirements for dendrites to implement backpropagation-like learning. We trained mice to modulate the activity of two spatially intermingled populations (4 or 5 neurons each) of layer 5 pyramidal neurons in the retrosplenial cortex to rotate a visual grating towards a target orientation while we recorded GCaMP activity from somas and corresponding distal apical dendrites. We observed that the relative magnitudes of somatic versus dendritic signals could be predicted using the activity of the surrounding network and contained information about task-related variables that could serve as instructive signals, including reward and error. The signs of these putative teaching signals both depended on the causal role of individual neurons in the task and predicted changes in overall activity over the course of learning. These results provide the first biological evidence of a backpropagation-like solution to the credit assignment problem in the brain.}, }
@article {pmid37960675, year = {2023}, author = {Lyu, S and Cheung, RCC}, title = {Efficient Multiple Channels EEG Signal Classification Based on Hierarchical Extreme Learning Machine.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {21}, pages = {}, pmid = {37960675}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; Algorithms ; Software ; Machine Learning ; Electroencephalography/methods ; }, abstract = {The human brain can be seen as one of the most powerful processors in the world, and it has a very complex structure with different kinds of signals for monitoring organics, communicating to neurons, and reacting to different information, which allows large developments in observing human sleeping, revealing diseases, reflecting certain motivations of limbs, and other applications. Relative theory, algorithms, and applications also help us to build brain-computer interface (BCI) systems for different powerful functions. Therefore, we present a fast-reaction framework based on an extreme learning machine (ELM) with multiple layers for the ElectroEncephaloGram (EEG) signals classification in motor imagery, showing the advantages in both accuracy of classification and training speed compared with conventional machine learning methods. The experiments are performed on software with the dataset of BCI Competition II with fast training time and high accuracy. The final average results show an accuracy of 93.90% as well as a reduction of 75% of the training time as compared to conventional deep learning and machine learning algorithms for EEG signal classification, also showing its prospects of the improvement of the performance of the BCI system.}, }
@article {pmid37960592, year = {2023}, author = {Lun, X and Zhang, Y and Zhu, M and Lian, Y and Hou, Y}, title = {A Combined Virtual Electrode-Based ESA and CNN Method for MI-EEG Signal Feature Extraction and Classification.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {21}, pages = {}, pmid = {37960592}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; Electroencephalography/methods ; Neural Networks, Computer ; Imagery, Psychotherapy ; Electrodes ; Algorithms ; }, abstract = {A Brain-Computer Interface (BCI) is a medium for communication between the human brain and computers, which does not rely on other human neural tissues, but only decodes Electroencephalography (EEG) signals and converts them into commands to control external devices. Motor Imagery (MI) is an important BCI paradigm that generates a spontaneous EEG signal without external stimulation by imagining limb movements to strengthen the brain's compensatory function, and it has a promising future in the field of computer-aided diagnosis and rehabilitation technology for brain diseases. However, there are a series of technical difficulties in the research of motor imagery-based brain-computer interface (MI-BCI) systems, such as: large individual differences in subjects and poor performance of the cross-subject classification model; a low signal-to-noise ratio of EEG signals and poor classification accuracy; and the poor online performance of the MI-BCI system. To address the above problems, this paper proposed a combined virtual electrode-based EEG Source Analysis (ESA) and Convolutional Neural Network (CNN) method for MI-EEG signal feature extraction and classification. The outcomes reveal that the online MI-BCI system developed based on this method can improve the decoding ability of multi-task MI-EEG after training, it can learn generalized features from multiple subjects in cross-subject experiments and has some adaptability to the individual differences of new subjects, and it can decode the EEG intent online and realize the brain control function of the intelligent cart, which provides a new idea for the research of an online MI-BCI system.}, }
@article {pmid37959200, year = {2023}, author = {Gunduz, ME and Bucak, B and Keser, Z}, title = {Advances in Stroke Neurorehabilitation.}, journal = {Journal of clinical medicine}, volume = {12}, number = {21}, pages = {}, pmid = {37959200}, issn = {2077-0383}, abstract = {Stroke is one of the leading causes of disability worldwide despite recent advances in hyperacute interventions to lessen the initial impact of stroke. Stroke recovery therapies are crucial in reducing the long-term disability burden after stroke. Stroke recovery treatment options have rapidly expanded within the last decade, and we are in the dawn of an exciting era of multimodal therapeutic approaches to improve post-stroke recovery. In this narrative review, we highlighted various promising advances in treatment and technologies targeting stroke rehabilitation, including activity-based therapies, non-invasive and minimally invasive brain stimulation techniques, robotics-assisted therapies, brain-computer interfaces, pharmacological treatments, and cognitive therapies. These new therapies are targeted to enhance neural plasticity as well as provide an adequate dose of rehabilitation and improve adherence and participation. Novel activity-based therapies and telerehabilitation are promising tools to improve accessibility and provide adequate dosing. Multidisciplinary treatment models are crucial for post-stroke neurorehabilitation, and further adjuvant treatments with brain stimulation techniques and pharmacological agents should be considered to maximize the recovery. Among many challenges in the field, the heterogeneity of patients included in the study and the mixed methodologies and results across small-scale studies are the cardinal ones. Biomarker-driven individualized approaches will move the field forward, and so will large-scale clinical trials with a well-targeted patient population.}, }
@article {pmid37956749, year = {2023}, author = {Qin, Y and Li, B and Wang, W and Shi, X and Wang, H and Wang, X}, title = {ETCNet: An EEG-based motor imagery classification model combining efficient channel attention and temporal convolutional network.}, journal = {Brain research}, volume = {}, number = {}, pages = {148673}, doi = {10.1016/j.brainres.2023.148673}, pmid = {37956749}, issn = {1872-6240}, abstract = {Brain-computer interface (BCI) enables the control of external devices using signals from the brain, offering immense potential in assisting individuals with neuromuscular disabilities. Among the different paradigms of BCI systems, the motor imagery (MI) based electroencephalogram (EEG) signal is widely recognized as exceptionally promising. Deep learning (DL) has found extensive applications in the processing of MI signals, wherein convolutional neural networks (CNNs) have demonstrated superior performance compared to conventional machine learning approaches. Nevertheless, challenges related to subject independence and subject dependence persist, while the inherent low signal-to-noise ratio of EEG signals remains a critical aspect that demands attention. Accurately deciphering intentions from EEG signals continues to present a formidable challenge. This paper introduces an advanced end-to-end network that effectively combines the efficient channel attention (ECA) and temporal convolutional network (TCN) components for the classification of motor imagination signals. We incorporated an ECA module prior to feature extraction in order to enhance the extraction of channel-specific features. A compact convolutional network model uses for feature extraction in the middle part. Finally, the time characteristic information is obtained by using TCN. The results show that our network is a lightweight network that is characterized by few parameters and fast speed. Our network achieves an average accuracy of 80.71% on the BCI Competition IV-2a dataset.}, }
@article {pmid37955998, year = {2023}, author = {Wang, Y and Zhang, Y and Zhang, Y and Wang, Z and Guo, W and Zhang, Y and Wang, Y and Ge, Q and Wang, D}, title = {Voluntary Respiration Control: Signature Analysis by EEG.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3332458}, pmid = {37955998}, issn = {1558-0210}, abstract = {OBJECTIVE: The perception of voluntary respiratory consciousness is quite important in some situations, such as respiratory assistance and respiratory rehabilitation training, and the key signatures about voluntary respiration control may lie in the neural signals from brain manifested as electroencephalography (EEG). The present work aims to explore whether there exists correlation between voluntary respiration and scalp EEG.<br><br>METHODS: Evoke voluntary respiration of different intensities, while collecting EEG and respiration signal synchronously. Data from 11 participants were analyzed. Spectrum characteristics at low-frequency band were studied. Computation of EEG-respiration phase lock value (PLV) and EEG sample entropy were conducted as well.<br><br>RESULT: When breathing voluntarily, the 0-2 Hz band EEG power is significantly enhanced in frontal and right-parietal area. The distance between main peaks belonging to the two signals in 0-2 Hz spectrum graph tends to get smaller, while EEG-respiration PLV increases in frontal area. Besides, the sample entropy of EEG shows a trend of decreasing during voluntary respiration in both areas.<br><br>CONCLUSION: There's a strong correlation between voluntary respiration and scalp EEG.<br><br>SIGNIFICANCE: The discoveries will provide guidelines for developing a voluntary respiratory consciousness identifying method and make it possible to monitor people's intention of respiration by noninvasive BCI.}, }
@article {pmid37949256, year = {2023}, author = {Cabrera Castillos, K and Ladouce, S and Darmet, L and Dehais, F}, title = {Burst c-VEP Based BCI: Optimizing stimulus design for enhanced classification with minimal calibration data and improved user experience.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {120446}, doi = {10.1016/j.neuroimage.2023.120446}, pmid = {37949256}, issn = {1095-9572}, abstract = {The utilization of aperiodic flickering visual stimuli under the form of code-modulated Visual Evoked Potentials (c-VEP) represents a pivotal advancement in the field of reactive Brain-Computer Interface (rBCI). A major advantage of the c-VEP approach is that the training of the model is independent of the number and complexity of targets, which helps reduce calibration time. Nevertheless, the existing designs of c-VEP stimuli can be further improved in terms of visual user experience but also to achieve a higher signal-to-noise ratio, while shortening the selection time and calibration process. In this study, we introduce an innovative variant of code-VEP, referred to as "Burst c-VEP". This original approach involves the presentation of short bursts of aperiodic visual flashes at a deliberately slow rate, typically ranging from two to four flashes per second. The rationale behind this design is to leverage the sensitivity of the primary visual cortex to transient changes in low-level stimuli features to reliably elicit distinctive series of visual evoked potentials. In comparison to other types of faster-paced code sequences, burst c-VEP exhibit favorable properties to achieve high bitwise decoding performance using convolutional neural networks (CNN), which yields potential to attain faster selection time with the need for less calibration data. Furthermore, our investigation focuses on reducing the perceptual saliency of c-VEP through the attenuation of visual stimuli contrast and intensity to significantly improve users' visual comfort. The proposed solutions were tested through an offline 4-classes c-VEP protocol involving 12 participants. Following a factorial design, participants were instructed to focus on c-VEP targets whose pattern (burst and maximum-length sequences) and amplitude (100% or 40% amplitude depth modulations) were manipulated across experimental conditions. Firstly, the full amplitude burst c-VEP sequences exhibited higher accuracy, ranging from 90.5% (with 17.6s of calibration data) to 95.6% (with 52.8s of calibration data), compared to its m-sequence counterpart (71.4% to 85.0%). The mean selection time for both types of codes (1.5s) compared favorably to reports from previous studies. Secondly, our findings revealed that lowering the intensity of the stimuli only slightly decreased the accuracy of the burst code sequences to 94.2% while leading to substantial improvements in terms of user experience. Taken together, these results demonstrate the high potential of the proposed burst codes to advance reactive BCI both in terms of performance and usability. The collected dataset, along with the proposed CNN architecture implementation, are shared through open-access repositories.}, }
@article {pmid37948768, year = {2023}, author = {Luo, R and Xiao, X and Chen, E and Meng, L and Jung, TP and Xu, M and Ming, D}, title = {Almost free of calibration for SSVEP-based brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad0b8f}, pmid = {37948768}, issn = {1741-2552}, abstract = {OBJECTIVE: Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) is a promising technology that can achieve high information transfer rate (ITR) with supervised algorithms such as ensemble task-related component analysis (eTRCA) and task-discriminant component analysis (TDCA). However, training individual models requires a tedious and time-consuming calibration process, which hinders the real-life use of SSVEP-BCIs. A recent data augmentation method, called source aliasing matrix estimation (SAME), can generate new EEG samples from a few calibration trials. But SAME does not exploit the information across stimuli as well as only reduces the number of calibration trials per command, so it still has some limitations.<br><br>APPROACH: This study proposes an extended version of SAME, called multi-stimulus SAME (msSAME), which exploits the similarity of the aliasing matrix across frequencies to enhance the performance of SSVEP-BCI with insufficient calibration trials. We also propose a semi-supervised approach based on msSAME that can further reduce the number of SSVEP frequencies needed for calibration. We evaluate our method on two public datasets, Benchmark and BETA, and an online experiment.<br><br>MAIN RESULTS: The results show that msSAME outperforms SAME for both eTRCA and TDCA on the public datasets. Moreover, the semi-supervised msSAME-based method achieves comparable performance to the fully calibrated methods and outperforms the conventional free-calibrated methods. Remarkably, our method only needs 24 seconds to calibrate 40 targets in the online experiment and achieves an average ITR of 213.8 bits/min with a peak of 242.6 bits/min.<br><br>SIGNIFICANCE: This study significantly reduces the calibration effort for individual SSVEP-BCIs, which is beneficial for developing practical plug-and-play SSVEP-BCIs.}, }
@article {pmid37948668, year = {2023}, author = {Das, A and Nandi, N and Ray, S}, title = {Alpha and SSVEP power outperform gamma power in capturing attentional modulation in human EEG.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {}, number = {}, pages = {}, doi = {10.1093/cercor/bhad412}, pmid = {37948668}, issn = {1460-2199}, support = {//Ministry of Education, Government of India/ ; IA/S/18/2/504003//Wellcome Trust/DBT India Alliance/ ; //Tata Trusts/ ; //Department of Biotechnology-Indian Institute of Science/ ; }, abstract = {Attention typically reduces power in the alpha (8-12 Hz) band and increases power in gamma (>30 Hz) band in brain signals, as reported in macaque local field potential (LFP) and human electro/magneto-encephalogram (EEG/MEG) studies. In addition, EEG studies often use flickering stimuli that produce a specific measure called steady-state-visually-evoked-potential (SSVEP), whose power also increases with attention. However, effectiveness of these neural measures in capturing attentional modulation is unknown since stimuli and task paradigms vary widely across studies. In a recent macaque study, attentional modulation was more salient in the gamma band of the LFP, compared to alpha or SSVEP. To compare this with human EEG, we designed an orientation change detection task where we presented both static and counterphasing stimuli of matched difficulty levels to 26 subjects and compared attentional modulation of various measures under similar conditions. We report two main results. First, attentional modulation was comparable for SSVEP and alpha. Second, non-foveal stimuli produced weak gamma despite various stimulus optimizations and showed negligible attentional modulation although full-screen gratings showed robust gamma activity. Our results are useful for brain-machine-interfacing studies where suitable features are used for decoding attention, and also provide clues about spatial scales of neural mechanisms underlying attention.}, }
@article {pmid37947903, year = {2023}, author = {Wu, L and Wang, J and Lu, Y and Huang, Y and Zhang, X and Ma, D and Xiao, Y and Cao, F}, title = {Association of intimate partner violence with offspring growth in 32 low- and middle-income countries: a population-based cross-sectional study.}, journal = {Archives of women's mental health}, volume = {}, number = {}, pages = {}, pmid = {37947903}, issn = {1435-1102}, support = {32071084//National Natural Science Foundation of China/ ; }, abstract = {Intimate partner violence (IPV) against women presents a major public health challenge, especially in low-income and middle-income countries (LMICs), and its relationship with poor offspring growth is emerging but remains understudied. This study aimed to explore the impact of maternal exposure to IPV on offspring growth based on different approaches in LMICs. We conducted a population-based cross-sectional study using the most recent Demographic and Health Surveys from 32 LMICs; 81,652 mother-child dyads comprising women aged from 15 to 49 years with children aged 0 to 59 months were included. We applied logistic regression models to explore the independent and cumulative relationship between IPV, including emotional, physical, and sexual IPV, with poor child growth status, including stunting and wasting; 52.6% of mothers were under the age of 30 years with a 36% prevalence of any lifetime exposure to IPV. Maternal exposure to any IPV increased the odds of stunting, but only physical and sexual IPV were independently associated with an increased risk of stunting. Three different types of IPV exhibited a cumulative effect on stunting. Maternal exposure to physical IPV was significantly associated with an increased risk of wasting. Significant associations between maternal exposure to emotional IPV with offspring stunting and physical IPV with wasting were only observed in children aged 0 to 36 months. IPV against women remains high in LMICs and has adverse effects on offspring growth. Policy and program efforts are needed to prioritize the reduction of widespread physical and sexual IPV and to mitigate the impact of such violence.}, }
@article {pmid37943244, year = {2023}, author = {Levett, JJ and Elkaim, LM and Niazi, F and Weber, MH and Iorio-Morin, C and Bonizzato, M and Weil, AG}, title = {Invasive Brain Computer Interface for Motor Restoration in Spinal Cord Injury: A Systematic Review.}, journal = {Neuromodulation : journal of the International Neuromodulation Society}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neurom.2023.10.006}, pmid = {37943244}, issn = {1525-1403}, abstract = {STUDY DESIGN: Systematic review of the literature.<br><br>OBJECTIVES: In recent years, brain-computer interface (BCI) has emerged as a potential treatment for patients with spinal cord injury (SCI). This is the first systematic review of the literature on invasive closed-loop BCI technologies for the treatment of SCI in humans.<br><br>MATERIALS AND METHODS: A comprehensive search of PubMed MEDLINE, Web of Science, and Ovid EMBASE was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.<br><br>RESULTS: Of 8316 articles collected, 19 studies met&#xa0;all the inclusion criteria. Data from 21 patients were extracted from these studies. All patients sustained a cervical SCI and were treated using either a BCI with intracortical microelectrode arrays (n&#xa0;= 18, 85.7%) or electrocorticography (n&#xa0;= 3, 14.3%). To decode these neural signals, machine learning and statistical models were used: support vector machine in eight patients (38.1%), linear estimator in seven patients (33.3%), Hidden Markov Model in three patients (14.3%), and other in three patients (14.3%). As the outputs, ten patients (47.6%) underwent noninvasive functional electrical stimulation (FES) with a cuff; one (4.8%) had an invasive FES with percutaneous stimulation, and ten (47.6%) used an external device (neuroprosthesis or virtual avatar). Motor function was restored in all patients for each assigned task. Clinical outcome measures were heterogeneous across all studies.<br><br>CONCLUSIONS: Invasive techniques of BCI show promise for the treatment of SCI, but there is currently no technology that can restore complete functional autonomy in patients with SCI. The current techniques and outcomes of BCI vary greatly. Because invasive BCIs are still in the early stages of development, further clinical studies should be conducted to optimize the prognosis for patients with SCI.}, }
@article {pmid37941569, year = {2023}, author = {Duan, D and Wu, Z and Zhou, Y and Wan, X and Wen, D}, title = {Working memory training and evaluation based on brain-computer interface and virtual reality: our opinion.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1291983}, pmid = {37941569}, issn = {1662-5161}, }
@article {pmid37939416, year = {2023}, author = {Qin, C and Yuan, Q and Liu, M and Zhuang, L and Xu, L and Wang, P}, title = {Biohybrid tongue based on hypothalamic neuronal network-on-a-chip for real-time blood glucose sensing and assessment.}, journal = {Biosensors &amp; bioelectronics}, volume = {244}, number = {}, pages = {115784}, doi = {10.1016/j.bios.2023.115784}, pmid = {37939416}, issn = {1873-4235}, abstract = {The expression of sweet receptors in the hypothalamus has been implicated in energy homeostasis control and the pathogenesis of obesity and diabetes. However, the exact mechanism by which hypothalamic glucose-sensing neurons function remains unclear. Conventional detection methods, such as fiber photometry, optogenetics, brain-machine interfaces, patch clamp and calcium imaging, pose limitations for real-time glucose perception due to their complexity, cytotoxicity and so on. Therefore, this study proposes a biohybrid tongue based on hypothalamic neuronal network (HNN)-on-a-chip coupling with microelectrode array (MEA) for real-time glucose perception. Hypothalamic neuronal cultures were cultivated on a two-dimensional "brain-on-chip" device, enabling the formation of neuronal networks and electrophysiological signal detection. Additionally, we investigated the endogenous expression of sweet taste receptors (T1R2/T1R3) in hypothalamic neuronal cells, providing the basis for the biohybrid tongue based on HNN-on-a-chip's sweetness detection capabilities. The spike signal response to sucrose and glucose stimulation was detected, and concentration-dependent responses were explored with glucose concentrations ranging from 0.01 mM to 8 mM. MEAs allow for real-time recordings, enabling the observation of dynamic changes in neuronal responses to glucose fluctuations over time. The biohybrid tongue based on HNN-on-a-chip can measure various parameters, including spike frequency and amplitude, providing insights into neuronal firing patterns and excitability. Moreover, hypothalamic glucoregulatory neurons that sense and respond to changes in blood glucose was identified, including glucose-excited neurons (GE-Neurons) and glucose-inhibited neurons (GI-Neurons). The detection range for GE-Neurons spans from 0.4 to 6 mM, while GI-Neurons demonstrate sensitivity within the range of 1-8 mM. And the glucose detection limit was firmly established at 0.01 mM. Through non-linear regression analysis, the IC50 for GI-Neurons' spike firing was determined to be 4.18 mM. In conclusion, the biohybrid tongue based on HNN-on-a-chip offers a valuable in vitro tool for studying hypothalamic neurons, elucidating glucose sensing mechanisms, and understanding hypothalamic neuronal function.}, }
@article {pmid37938944, year = {2023}, author = {Eskandari, R and Sawan, M}, title = {Challenges and Perspectives on Impulse Radio-Ultra-Wideband Transceivers for Neural Recording Applications.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBCAS.2023.3331049}, pmid = {37938944}, issn = {1940-9990}, abstract = {Brain-machine interfaces (BMI) are widely adopted in neuroscience investigations and neural prosthetics, with sensing channel counts constantly increasing. These Investigations place increasing demands for high data rates and low-power implantable devices despite high tissue losses. The Impulse radio ultra- wideband (IR-UWB), a revived wireless technology for short-range radios, has been widely used in various applications. Since the requirements and solutions are application-oriented, in this review paper we focus on neural recording implants with high-data rates and ultra-low power requirements. We examine in detail the working principle, design methodology, performance, and implementations of different architectures of IR-UWB transceivers in a quantitative manner to draw a deep comparison and extract the bottlenecks and possible solutions concerning the dedicated application. Our analysis shows that current solutions rely on enhanced or combined modulation techniques to improve link margin. An in-depth study of prior-art publications that achieved Gbps data rates concludes that edge-combination architecture and non-coherent detectors are remarkable for transmitter and receiver, respectively. Although the aim to minimize power and improve data rate - defined as energy efficiency (pJ/b) - extending communication distance despite high tissue losses and limited power budget, good narrow-band interference (NBI) tolerance coexisted in the same frequency band of UWB systems, and compatibility with energy harvesting designs are among the critical challenges remained unsolved. Furthermore, we expect that the combination of artificial intelligence (AI) and the inherent advantages of UWB radios will pave the way for future improvements in BMIs.}, }
@article {pmid37938700, year = {2023}, author = {Drew, L}, title = {The rise of brain-reading technology: what you need to know.}, journal = {Nature}, volume = {623}, number = {7986}, pages = {241-243}, pmid = {37938700}, issn = {1476-4687}, mesh = {*Brain/physiology ; Monitoring, Physiologic/instrumentation/methods/trends ; *Thinking/physiology ; Prostheses and Implants ; Humans ; *Brain-Computer Interfaces/trends ; }, }
@article {pmid37936533, year = {2023}, author = {Liu, M and Li, T and Zhang, X and Yang, Y and Zhou, Z and Fu, T}, title = {IMH-Net: a convolutional neural network for end-to-end EEG motor imagery classification.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-14}, doi = {10.1080/10255842.2023.2275244}, pmid = {37936533}, issn = {1476-8259}, abstract = {As the main component of Brain-computer interface (BCI) technology, the classification algorithm based on EEG has developed rapidly. The previous algorithms were often based on subject-dependent settings, resulting in BCI needing to be calibrated for new users. In this work, we propose IMH-Net, an end-to-end subject-independent model. The model first uses Inception blocks extracts the frequency domain features of the data, then further compresses the feature vectors to extract the spatial domain features, and finally learns the global information and classification through Multi-Head Attention mechanism. On the OpenBMI dataset, IMH-Net obtained 73.90 ± 13.10% accuracy and 73.09 ± 14.99% F1-score in subject-independent manner, which improved the accuracy by 1.96% compared with the comparison model. On the BCI competition IV dataset 2a, this model also achieved the highest accuracy and F1-score in subject-dependent manner. The IMH-Net model we proposed can improve the accuracy of subject-independent Motor Imagery (MI), and the robustness of the algorithm is high, which has strong practical value in the field of BCI.}, }
@article {pmid37936521, year = {2023}, author = {Pastötter, B and Frings, C}, title = {Prestimulus alpha power signals attention to retrieval.}, journal = {The European journal of neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1111/ejn.16181}, pmid = {37936521}, issn = {1460-9568}, abstract = {The human brain is in distinct processing modes at different times. Specifically, a distinction can be made between encoding and retrieval modes, which refer to the brain's state when it is storing new information or searching for old information, respectively. Recent research proposed the idea of a "ready-to-encode" mode, which describes a prestimulus effect in brain activity that signals (external) attention to encoding and predicts subsequent memory performance. Whether there is also a corresponding "ready-to-retrieve" mode in human brain activity is currently unclear. In this study, we examined whether prestimulus oscillations can be linked to (internal) attention to retrieval. We show that task cues to prepare for retrieval (or testing) in comparison with restudy of previously studied vocabulary word pairs led to a significant decrease of prestimulus alpha power just before the onset of word stimuli. Beamformer analysis localized this effect in the right secondary visual cortex (Brodmann area 18). Correlation analysis showed that the task cue-induced, prestimulus alpha power effect is positively related to stimulus-induced alpha/beta power, which in turn predicted participants' memory performance. The results are consistent with the idea that prestimulus alpha power signals internal attention to retrieval, which promotes the elaborative processing of episodic memories. Future research on brain-computer interfaces may find the findings interesting regarding the potential of using online measures of fluctuating alpha oscillations to trigger the presentation and sequencing of restudy and testing trials, ultimately enhancing instructional learning strategies.}, }
@article {pmid37935744, year = {2023}, author = {Yu, H and Ni, P and Tian, Y and Zhao, L and Li, M and Li, X and Wei, W and Wei, J and Wang, Q and Guo, W and Deng, W and Ma, X and Coid, J and Li, T}, title = {Association of elevated levels of peripheral complement components with cortical thinning and impaired logical memory in drug-naïve patients with first-episode schizophrenia.}, journal = {Schizophrenia (Heidelberg, Germany)}, volume = {9}, number = {1}, pages = {79}, pmid = {37935744}, issn = {2754-6993}, support = {81630030 and 81920108018//National Natural Science Foundation of China (National Science Foundation of China)/ ; 82101598//National Natural Science Foundation of China (National Science Foundation of China)/ ; 81871054 and 81501159//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Schizophrenia has been linked to polymorphism in genes encoding components of the complement system, and hyperactive complement activity has been linked to immune dysfunction in schizophrenia patients. Whether and how specific complement components influence brain structure and cognition in the disease is unclear. Here we compared 52 drug-naïve patients with first-episode schizophrenia and 52 healthy controls in terms of levels of peripheral complement factors, cortical thickness (CT), logical memory and psychotic symptoms. We also explored the relationship between complement factors with CT, cognition and psychotic symptoms. Patients showed significantly higher levels of C1q, C4, factor B, factor H, and properdin in plasma. Among patients, higher levels of C3 in plasma were associated with worse memory recall, while higher levels of C4, factor B and factor H were associated with thinner sensory cortex. These findings link dysregulation of specific complement components to abnormal brain structure and cognition in schizophrenia.}, }
@article {pmid37934650, year = {2023}, author = {Park, D and Park, H and Kim, S and Choo, S and Lee, S and Nam, CS and Jung, JY}, title = {Spatio-temporal explanation of 3D-EEGNet for motor imagery EEG classification using permutation and saliency.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3330922}, pmid = {37934650}, issn = {1558-0210}, abstract = {Recently, convolutional neural network (CNN)-based classification models have shown good performance for motor imagery (MI) brain-computer interfaces (BCI) using electroencephalogram (EEG) in end-to-end learning. Although a few explainable artificial intelligence (XAI) techniques have been developed, it is still challenging to interpret the CNN models for EEG-based BCI classification effectively. In this research, we propose 3D-EEGNet as a 3D CNN model to improve both the explainability and performance of MI EEG classification. The proposed approach exhibited better performances on two MI EEG datasets than the existing EEGNet, which uses a 2D input shape. The MI classification accuracies are improved around 1.8% and 6.1% point in average on the datasets, respectively. The permutation-based XAI method is first applied for the reliable explanation of the 3D-EEGNet. Next, to find a faster XAI method for spatio-temporal explanation, we design a novel technique based on the normalized discounted cumulative gain (NDCG) for selecting the best among a few saliency-based methods due to their higher time complexity than the permutation-based method. Among the saliency-based methods, DeepLIFT was selected because the NDCG scores indicated its results are the most similar to the permutation-based results. Finally, the fast spatio-temporal explanation using DeepLIFT provides deeper understanding for the classification results of the 3D-EEGNet and the important properties in the MI EEG experiments.}, }
@article {pmid37934649, year = {2023}, author = {Wang, Z and Fang, J and Zhang, J}, title = {Rethinking Delayed Hemodynamic Responses for fNIRS Classification.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3330911}, pmid = {37934649}, issn = {1558-0210}, abstract = {Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technology for monitoring cerebral hemodynamic responses. Enhancing fNIRS classification can improve the performance of brain-computer interfaces (BCIs). Currently, deep neural networks (DNNs) do not consider the inherent delayed hemodynamic responses of fNIRS signals, which causes many optimization and application problems. Considering the kernel size and receptive field of convolutions, delayed hemodynamic responses as domain knowledge are introduced into fNIRS classification, and a concise and efficient model named fNIRSNet is proposed. We empirically summarize three design guidelines for fNIRSNet. In subject-specific and subject-independent experiments, fNIRSNet outperforms other DNNs on open-access datasets. Specifically, fNIRSNet with only 498 parameters is 6.58% higher than convolutional neural network (CNN) with millions of parameters on mental arithmetic tasks and the floating-point operations (FLOPs) of fNIRSNet are much lower than CNN. Therefore, fNIRSNet is friendly to practical applications and reduces the hardware cost of BCI systems. It may inspire more research on knowledge-driven models for fNIRS BCIs. Code is available at https://github.com/wzhlearning/fNIRSNet.}, }
@article {pmid37932497, year = {2023}, author = {Ma, S and Chen, M and Jiang, Y and Xiang, X and Wang, S and Wu, Z and Li, S and Cui, Y and Wang, J and Zhu, Y and Zhang, Y and Ma, H and Duan, S and Li, H and Yang, Y and Lingle, CJ and Hu, H}, title = {Author Correction: Sustained antidepressant effect of ketamine through NMDAR trapping in the LHb.}, journal = {Nature}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41586-023-06814-x}, pmid = {37932497}, issn = {1476-4687}, }
@article {pmid37932250, year = {2023}, author = {Duraivel, S and Rahimpour, S and Chiang, CH and Trumpis, M and Wang, C and Barth, K and Harward, SC and Lad, SP and Friedman, AH and Southwell, DG and Sinha, SR and Viventi, J and Cogan, GB}, title = {High-resolution neural recordings improve the accuracy of speech decoding.}, journal = {Nature communications}, volume = {14}, number = {1}, pages = {6938}, pmid = {37932250}, issn = {2041-1723}, support = {R01 DC019498/DC/NIDCD NIH HHS/United States ; UG3 NS120172/NS/NINDS NIH HHS/United States ; R01 NS129703/NS/NINDS NIH HHS/United States ; UL1 TR002553/TR/NCATS NIH HHS/United States ; }, mesh = {Humans ; *Speech ; Quality of Life ; Electrocorticography/methods ; Communication ; Brain ; *Brain-Computer Interfaces ; }, abstract = {Patients suffering from debilitating neurodegenerative diseases often lose the ability to communicate, detrimentally affecting their quality of life. One solution to restore communication is to decode signals directly from the brain to enable neural speech prostheses. However, decoding has been limited by coarse neural recordings which inadequately capture the rich spatio-temporal structure of human brain signals. To resolve this limitation, we performed high-resolution, micro-electrocorticographic (µECoG) neural recordings during intra-operative speech production. We obtained neural signals with 57× higher spatial resolution and 48% higher signal-to-noise ratio compared to macro-ECoG and SEEG. This increased signal quality improved decoding by 35% compared to standard intracranial signals. Accurate decoding was dependent on the high-spatial resolution of the neural interface. Non-linear decoding models designed to utilize enhanced spatio-temporal neural information produced better results than linear techniques. We show that high-density µECoG can enable high-quality speech decoding for future neural speech prostheses.}, }
@article {pmid37931308, year = {2023}, author = {Han, J and Wei, X and Faisal, AA}, title = {EEG decoding for datasets with heterogenous electrode configurations using transfer learning graph neural networks.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad09ff}, pmid = {37931308}, issn = {1741-2552}, abstract = {Objective Brain-Machine Interfacing (BMI) has greatly benefited from adopting machine learning methods for feature learning that require extensive data for training, which are often unavailable from a single dataset. Yet, it is difficult to combine data across labs or even data within the same lab collected over the years due to the variation in recording equipment and electrode layouts resulting in shifts in data distribution, changes in data dimensionality, and altered identity of data dimensions. Our objective is to overcome this limitation and learn from many different and diverse datasets across labs with different experimental protocols. Approach To tackle the domain adaptation problem, we developed a novel machine learning framework combining graph neural networks (GNNs) and transfer learning methodologies for non-invasive Motor Imagery (MI) EEG decoding, as an example of BMI. Empirically, we focus on the challenges of learning from EEG data with different electrode layouts and varying numbers of electrodes. We utilise three MI EEG databases collected using very different numbers of EEG sensors (from 22 channels to 64) and layouts (from custom layouts to 10-20). Main Results Our model achieved the highest accuracy with lower standard deviations on the testing datasets. This indicates that the GNN-based transfer learning framework can effectively aggregate knowledge from multiple datasets with different electrode layouts, leading to improved generalization in subject-independent MI EEG classification. Significance The findings of this study have important implications for Brain-Computer-Interface (BCI) research, as they highlight a promising method for overcoming the limitations posed by non-unified experimental setups. By enabling the integration of diverse datasets with varying electrode layouts, our proposed approach can help advance the development and application of BMI technologies.}, }
@article {pmid37931299, year = {2023}, author = {Pan, LC and Wang, K and Xu, L and Sun, X and Yi, W and Xu, M and Ming, D}, title = {Riemannian geometric and ensemble learning for decoding cross-session motor imagery electroencephalography signals.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad0a01}, pmid = {37931299}, issn = {1741-2552}, abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) enable a direct communication pathway between the human brain and external devices, without relying on the traditional peripheral nervous and musculoskeletal systems. Motor imagery (MI)-based BCIs have attracted significant interest for their potential in motor rehabilitation. However, current algorithms fail to account for the cross-session variability of electroencephalography (EEG) signals, limiting their practical application.<br><br>APPROACH: We proposed a Riemannian geometry-based adaptive boosting and voting ensemble (RAVE) algorithm to address this issue. Our approach segmented the MI period into multiple sub-datasets using a sliding window approach and extracted features from each sub-dataset using Riemannian geometry. We then trained adaptive boosting (AdaBoost) ensemble learning classifiers for each sub-dataset, with the final BCI output determined by majority voting of all classifiers. We tested our proposed RAVE algorithm and eight other competing algorithms on four datasets (Pan2023, BNCI001-2014, BNCI001-2015, BNCI004-2015).<br><br>MAIN RESULTS: Our results showed that, in the cross-session scenario, the RAVE algorithm outperformed the eight other competing algorithms significantly under different within-session training sample sizes. On most datasets (Pan2023, BNCI001-2014, BNCI001-2015), the RAVE algorithm could achieve or even exceed the classification performance of traditional algorithms that used a large amount of training samples, even when it did not require or only required a small amount of within-session training samples.<br><br>SIGNIFICANCE: These findings indicate that our cross-session decoding strategy could enable MI-BCI applications that require no or minimal training process.}, }
@article {pmid37917520, year = {2023}, author = {Xu, G and Wang, Z and Zhao, X and Li, R and Zhou, T and Xu, T and Hu, H}, title = {Attentional State Classification Using Amplitude and Phase Feature Extraction Method Based on Filter Bank and Riemannian Manifold.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {31}, number = {}, pages = {4402-4412}, doi = {10.1109/TNSRE.2023.3329482}, pmid = {37917520}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; Algorithms ; Electroencephalography/methods ; Signal Processing, Computer-Assisted ; Brain ; }, abstract = {As a significant aspect of cognition, attention has been extensively studied and numerous measurements have been developed based on brain signal processing. Although existing attentional state classification methods have achieved good accuracy by extracting a variety of handcrafted features, spatial features have not been fully explored. This paper proposes an attentional state classification method based on Riemannian manifold to utilize spatial information. Based on the concept of Riemannian manifold of symmetric positive definite (SPD) matrix, the proposed method exploits the structure of covariance matrix to extract spatial features instead of using spatial filters. Specifically, Riemannian distances from intra-class Riemannian means are extracted as features for their robustness. To fully extend the potential of electroencephalograph (EEG) signal, both amplitude and phase information is utilized. In addition, to solve the variance of frequency bands, a filter bank is employed to process the signal of different frequency bands separately. Finally, features are fed into a support vector machine with a polynomial kernel to obtain classification results. The proposed attentional state classification using amplitude and phase feature extraction method based on filter bank and Riemannian manifold (AP-FBRM) method is evaluated on two open datasets including EEG data of 29 and 26 subjects. According to the experimental results, the optimal set of filter bank and the optimal technique to extract features containing both amplitude and phase information are determined. The proposed method respectively achieves accuracies of 88.06% and 80.00% and outperforms 8 baseline methods, which manifests that the proposed method creates an efficient way to recognize attentional state.}, }
@article {pmid37903724, year = {2023}, author = {Yan, Y and Zhou, P and Ding, L and Hu, W and Chen, W and Su, B}, title = {T Cell Antigen Recognition and Discrimination by Electrochemiluminescence Imaging.}, journal = {Angewandte Chemie (International ed. in English)}, volume = {}, number = {}, pages = {e202314588}, doi = {10.1002/anie.202314588}, pmid = {37903724}, issn = {1521-3773}, support = {22125405//National Natural Science Foundation of China/ ; 22074131//National Natural Science Foundation of China/ ; }, abstract = {Adoptive T lymphocyte (T cell) transfer and tumour-specific peptide vaccines are innovative cancer therapies. An accurate assessment of the specific reactivity of T cell receptors (TCRs) to tumour antigens is required because of the high heterogeneity of tumour cells and the immunosuppressive tumour microenvironment. In this study, we report a label-free electrochemiluminescence (ECL) imaging approach for recognising and discriminating between TCRs and tumour-specific antigens by imaging the immune synapses of T cells. Various T cell stimuli, including agonistic antibodies, auxiliary molecules, and tumour-specific antigens, were modified on the electrode's surface to allow for their interaction with T cells bearing different TCRs. The formation of immune synapses activated by specific stimuli produced a negative (shadow) ECL image, from which T cell antigen recognition and discrimination were evaluated by analysing the spreading area and the recognition intensity of T cells. This approach provides an easy way to assess TCR-antigen specificity and screen both of them for immunotherapies.}, }
@article {pmid37930905, year = {2023}, author = {Wang, J and Bi, L and Fei, W}, title = {EEG-Based Motor BCIs for Upper Limb Movement: Current Techniques and Future Insights.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3330500}, pmid = {37930905}, issn = {1558-0210}, abstract = {Motor brain-computer interface (BCI) refers to the BCI that decodes voluntary motion intentions from brain signals directly and outputs corresponding control commands without activating peripheral nerves and muscles. Motor BCIs can be used for the restoration, compensation, and augmentation of motor function by activating the neuromuscular circuit and facilitating neural plasticity. The essential applications of motor BCIs include neurorehabilitation and daily-life assistance for motor-impaired patients. In recent years, studies on motor BCIs mainly concentrate on neural signatures, movement decoding, and its applications. In this review, we aim to provide a comprehensive review of the state-of-the-art research of electroencephalography (EEG) signals-based motor BCIs for the first time. We also aim to give some insights into advancing motor BCIs to a more natural and practical application scenario. In particular, we focus on the motor BCIs for the movements of the upper limbs. Specifically, the experimental paradigms, techniques, and application systems of upper-limb BCIs are reviewed. Several vital issues in developing more natural and practical upper-limb motor BCIs, including developing target-users-oriented, distraction-robust, and multi-limbs motor BCIs, and applying fusion techniques to promote the natural and practical motor BCIs, are discussed.}, }
@article {pmid37928726, year = {2023}, author = {Tong, L and Qian, Y and Peng, L and Wang, C and Hou, ZG}, title = {A learnable EEG channel selection method for MI-BCI using efficient channel attention.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1276067}, pmid = {37928726}, issn = {1662-4548}, abstract = {INTRODUCTION: During electroencephalography (EEG)-based motor imagery-brain-computer interfaces (MI-BCIs) task, a large number of electrodes are commonly used, and consume much computational resources. Therefore, channel selection is crucial while ensuring classification accuracy.<br><br>METHODS: This paper proposes a channel selection method by integrating the efficient channel attention (ECA) module with a convolutional neural network (CNN). During model training process, the ECA module automatically assigns the channel weights by evaluating the relative importance for BCI classification accuracy of every channel. Then a ranking of EEG channel importance can be established so as to select an appropriate number of channels to form a channel subset from the ranking. In this paper, the ECA module is embedded into a commonly used network for MI, and comparative experiments are conducted on the BCI Competition IV dataset 2a.<br><br>RESULTS AND DISCUSSION: The proposed method achieved an average accuracy of 75.76% with all 22 channels and 69.52% with eight channels in a four-class classification task, outperforming other state-of-the-art EEG channel selection methods. The result demonstrates that the proposed method provides an effective channel selection approach for EEG-based MI-BCI.}, }
@article {pmid37928600, year = {2023}, author = {Vorreuther, A and Bastian, L and Benitez Andonegui, A and Evenblij, D and Riecke, L and Lührs, M and Sorger, B}, title = {It takes two (seconds): decreasing encoding time for two-choice functional near-infrared spectroscopy brain-computer interface communication.}, journal = {Neurophotonics}, volume = {10}, number = {4}, pages = {045005}, pmid = {37928600}, issn = {2329-423X}, abstract = {SIGNIFICANCE: Brain-computer interfaces (BCIs) can provide severely motor-impaired patients with a motor-independent communication channel. Functional near-infrared spectroscopy (fNIRS) constitutes a promising BCI-input modality given its high mobility, safety, user comfort, cost-efficiency, and relatively low motion sensitivity.<br><br>AIM: The present study aimed at developing an efficient and convenient two-choice fNIRS communication BCI by implementing a relatively short encoding time (2&#xa0;s), considerably increasing communication speed, and decreasing the cognitive load of BCI users.<br><br>APPROACH: To encode binary answers to 10 biographical questions, 10 healthy adults repeatedly performed a combined motor-speech imagery task within 2 different time windows guided by auditory instructions. Each answer-encoding run consisted of 10 trials. Answers were decoded during the ongoing experiment from the time course of the individually identified most-informative fNIRS channel-by-chromophore combination.<br><br>RESULTS: The answers of participants were decoded online with an accuracy of 85.8% (run-based group mean). Post-hoc analysis yielded an average single-trial accuracy of 68.1%. Analysis of the effect of number of trial repetitions showed that the best information-transfer rate could be obtained by combining four encoding trials.<br><br>CONCLUSIONS: The study demonstrates that an encoding time as short as 2&#xa0;s can enable immediate, efficient, and convenient fNIRS-BCI communication.}, }
@article {pmid37918024, year = {2023}, author = {Zhang, H and Zhang, Y and Wang, X and Chen, G and Jian, X and Xu, M and Ming, D}, title = {Transcranial dipole localization and decoding study based on ultrasonic phased array for acoustoelectric brain imaging.}, journal = {Journal of neural engineering}, volume = {20}, number = {6}, pages = {}, doi = {10.1088/1741-2552/ad08f5}, pmid = {37918024}, issn = {1741-2552}, abstract = {Objective. Neuroimaging is one of the effective tools to understand the functional activities of the brain, but traditional non-invasive neuroimaging techniques are difficult to combine both high temporal and spatial resolution to satisfy clinical needs. Acoustoelectric brain imaging (ABI) can combine the millimeter spatial resolution advantage of focused ultrasound with the millisecond temporal resolution advantage of electroencephalogram signals.Approach. In this study, we first explored the transcranial modulated acoustic field distribution based on ABI, and further localized and decoded single and double dipoles signals.Main results. The results show that the simulation-guided acoustic field modulation results are significantly better than those of self-focusing, which can realize precise modulation focusing of intracranial target focusing. The single dipole transcranial localization error is less than 0.4 mm and the decoding accuracy is greater than 0.93. The double dipoles transcranial localization error is less than 0.2 mm and the decoding accuracy is greater than 0.89.Significance. This study enables precise focusing of transcranial acoustic field modulation, high-precision localization of source signals and decoding of their waveforms, which provides a technical method for ABI in localizing evoked excitatory neuron areas and epileptic focus.}, }
@article {pmid37927423, year = {2023}, author = {Graham, B and Ehlers, A}, title = {Development and Validation of the Bullied Cognitions Inventory (BCI).}, journal = {Cognitive therapy and research}, volume = {47}, number = {6}, pages = {1033-1045}, pmid = {37927423}, issn = {0147-5916}, abstract = {BACKGROUND: Bullying increases risk of social anxiety and can produce symptoms of posttraumatic stress disorder (PTSD). According to cognitive models, these are maintained by unhelpful beliefs, which are therefore assessed and targeted in cognitive therapy. This paper describes psychometric validation of a new measure of beliefs related to bullying experiences.<br><br>METHODS: In an online survey of 1879 young people before starting university or college in the UK, 1279 reported a history of bullying (N&#x2009;=&#x2009;1279), and 854 rated their agreement with beliefs about self and others related to bullying experiences and completed symptom measures of social anxiety and PTSD related to bullying. An empirical structure for a Bullied Cognitions Inventory was established using exploratory and confirmatory factor analyses and assessed using model fit statistics and tests of reliability and validity.<br><br>RESULTS: Fifteen items clustered into four themes: "degraded in the eyes of others", "negative interpretations of reactions to bullying", "recognisable as a bullying victim" and "social defeat". The measure has acceptable reliability and validity and, accounting for existing cognitive measures, explained additional variance in symptoms of PTSD but not social anxiety.<br><br>CONCLUSIONS: The Bullied Cognitions Inventory&#xa0;(BCI) is a valid and reliable tool for measuring cognitions related to bullying. It may be useful in therapy for identifying and monitoring unhelpful cognitions in those who were bullied.<br><br>SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10608-023-10412-6.}, }
@article {pmid37926843, year = {2023}, author = {Zhang, X and Wang, W and Bai, X and Zhang, X and Yuan, Z and Jiao, B and Zhang, Y and Li, Z and Zhang, P and Tang, H and Zhang, Y and Yu, X and Bai, R and Wang, Y and Sui, B}, title = {Increased glymphatic system activity in migraine chronification by diffusion tensor image analysis along the perivascular space.}, journal = {The journal of headache and pain}, volume = {24}, number = {1}, pages = {147}, pmid = {37926843}, issn = {1129-2377}, support = {Z200024//National Natural Science Foundation of Beijing/ ; 32170752, 91849104, and 31770800//National Natural Science Foundation of China/ ; 62271061//National Natural Science Foundation of China/ ; 7212028//Beijing Municipal Natural Science Foundation/ ; }, mesh = {Humans ; *Glymphatic System/diagnostic imaging ; Cross-Sectional Studies ; *Migraine Disorders/diagnostic imaging ; Headache ; *Headache Disorders ; }, abstract = {BACKGROUND: Preliminary evidence suggests that several headache disorders may be associated with glymphatic dysfunction. However, no studies have been conducted to examine the glymphatic activity in migraine chronification.<br><br>PURPOSES: To investigate the glymphatic activity of migraine chronification in patients with episodic migraine (EM) and chronic migraine (CM) using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) method.<br><br>METHODS: In this cross-sectional study, patients with EM, CM, and healthy controls (HCs) were included. All participants underwent a standard brain magnetic resonance imaging (MRI) examination. Bilateral DTI-ALPS indexes were calculated for all participants and compared among EM, CM, and HC groups. Correlations between the DTI-ALPS index and clinical characteristics were analyzed.<br><br>RESULTS: A total of 32 patients with EM, 24 patients with CM, and 41 age- and sex-matched HCs were included in the analysis. Significant differences were found in the right DTI-ALPS index among the three groups (p&#x2009;=&#x2009;0.011), with CM showing significantly higher values than EM (p&#x2009;=&#x2009;0.033) and HCs (p&#x2009;=&#x2009;0.015). The right DTI-ALPS index of CM group was significantly higher than the left DTI-ALPS index (p&#x2009;=&#x2009;0.005). And the headache intensity was correlated to DTI-ALPS index both in the left hemisphere (r&#x2009;=&#x2009;0.371, p&#x2009;=&#x2009;0.011) and in the right hemisphere (r&#x2009;=&#x2009;0.307, p&#x2009;=&#x2009;0.038), but there were no correlations after Bonferroni correction.<br><br>CONCLUSIONS: Glymphatic system activity is shown to be increased instead of impaired during migraine chronification. The mechanism behind this observation suggests that increased glymphatic activity is more likely to be a concomitant phenomenon of altered vascular reactivity associated with migraine pathophysiology rather than a risk factor of migraine chronification.}, }
@article {pmid37925576, year = {2023}, author = {Comandini, G and Ouisse, M and Ting, VP and Scarpa, F}, title = {Acoustic transmission loss in Hilbert fractal metamaterials.}, journal = {Scientific reports}, volume = {13}, number = {1}, pages = {19058}, pmid = {37925576}, issn = {2045-2322}, support = {EP/L016028/1//EPSRC Centre for Doctoral Training in Advanced Composites for Innovation and Science/ ; EP/R01650X/1//EPSRC/ ; No. 101020715//ERC-2020-AdG-NEUROMETA/ ; }, abstract = {Acoustic metamaterials are increasingly being considered as a viable technology for sound insulation. Fractal patterns constitute a potentially groundbreaking architecture for acoustic metamaterials. We describe in this work the behaviour of the transmission loss of Hilbert fractal metamaterials used for sound control purposes. The transmission loss of 3D printed metamaterials with Hilbert fractal patterns related to configurations from the zeroth to the fourth order is investigated here using impedance tube tests and Finite Element models. We evaluate, in particular, the impact of the equivalent porosity and the relative size of the cavity of the fractal pattern versus the overall dimensions of the metamaterial unit. We also provide an analytical formulation that relates the acoustic cavity resonances in the fractal patterns and the frequencies associated with the maxima of the transmission losses, providing opportunities to tune the sound insulation properties through control of the fractal architecture.}, }
@article {pmid37924858, year = {2023}, author = {Pak, S and Lee, M and Lee, S and Zhao, H and Baeg, E and Yang, S and Yang, S}, title = {Cortical surface plasticity promotes map remodeling and alleviates tinnitus in adult mice.}, journal = {Progress in neurobiology}, volume = {}, number = {}, pages = {102543}, doi = {10.1016/j.pneurobio.2023.102543}, pmid = {37924858}, issn = {1873-5118}, abstract = {Tinnitus induced by hearing loss is caused primarily by irreversible damage to the peripheral auditory system, which results in abnormal neural responses and frequency map disruption in the central auditory system. It remains unclear whether and how electrical rehabilitation of the auditory cortex can alleviate tinnitus. We hypothesize that stimulation of the cortical surface can alleviate tinnitus by enhancing neural responses and promoting frequency map reorganization. To test this hypothesis, we assessed and activated cortical maps using our newly designed graphene-based electrode array with a noise-induced tinnitus animal model. We found that cortical surface stimulation increased cortical activity, reshaped sensory maps, and alleviated hearing loss-induced tinnitus behavior in adult mice. These effects were likely due to retained long-term synaptic potentiation capabilities, as shown in cortical slices from the mice model. These findings suggest that cortical surface activation can be used to facilitate practical functional recovery from phantom percepts induced by sensory deprivation. They also provide a working principle for various treatment methods that involve electrical rehabilitation of the cortex.}, }
@article {pmid37924204, year = {2023}, author = {Ng, TTW and Davel, S and O'Connor, KD}, title = {Sulfasalazine-Induced Delayed Hypersensitivity Reaction Presenting as Fever, Aseptic Meningitis, and Mesenteric Panniculitis in a Patient with Seronegative Arthritis.}, journal = {The American journal of case reports}, volume = {24}, number = {}, pages = {e941623}, pmid = {37924204}, issn = {1941-5923}, mesh = {Female ; Humans ; Aged, 80 and over ; *Meningitis, Aseptic/chemically induced/diagnosis ; Sulfasalazine/adverse effects ; *Panniculitis, Peritoneal/complications ; *Arthritis ; Fever/chemically induced/complications ; *Sepsis/complications ; *Neoplasms/complications ; Fatigue ; *Hypersensitivity, Delayed/complications ; Steroids ; }, abstract = {BACKGROUND An 82-year-old woman presented with acute pyrexial illness and mesenteric panniculitis and developed biochemical aseptic meningitis (cerebrospinal fluid pleocytosis with no identifiable pathogen). Investigation determined her illness was likely a delayed hypersensitivity reaction caused by sulfasalazine. Sulfasalazine-induced aseptic meningitis is a rare condition often diagnosed late in a patient's admission owing to initial non-specific illness symptomatology requiring the exclusion of more common "red flag" etiologies, such as infection and malignancy. CASE REPORT An 82-year-old woman with a history of recurrent urinary tract infections and seronegative arthritis presented with a 3-day history of fatigue, headache, dyspnea, and lassitude. On admission, she was treated as presumed sepsis of uncertain source owing to pyrexia and tachycardia. Brain computer tomography (CT) revealed no acute intracranial abnormality. Furthermore, CT of the chest, abdomen, and pelvis did not reveal any source of sepsis or features of malignancy. After excluding infective etiologies with serological and cerebrospinal fluid testing, sulfasalazine-induced aseptic meningitis (SIAM) was diagnosed. The patient was then commenced on intravenous steroids, resulting in immediate defervescence and symptom resolution. CONCLUSIONS SIAM remains a diagnostic challenge since patients present with non-specific signs and symptoms, such as pyrexia, headaches, and lassitude. These patients require a thorough investigative battery starting with anamnesis, physical examination, biochemical testing, and radiologic imaging. This case illustrates the need for a high suspicion index of drug-induced hypersensitivity reaction in a rheumatological patient with pyrexial illness where infective etiologies have been confidently excluded. Prompt initiation of intravenous steroids in SIAM provides a dramatic recovery and resolution of symptoms.}, }
@article {pmid37922809, year = {2023}, author = {Gao, K and Hu, M and Li, J and Li, Z and Xu, W and Qian, Z and Gao, F and Ma, T}, title = {Drug-detecting bioelectronic nose based on odor cue memory combined with a brain computer interface.}, journal = {Biosensors &amp; bioelectronics}, volume = {244}, number = {}, pages = {115797}, doi = {10.1016/j.bios.2023.115797}, pmid = {37922809}, issn = {1873-4235}, abstract = {The international drug situation is increasingly, various new drugs are hidden in public places through changing forms and packaging, which brings new challenges to drug enforcement. This study proposes a drug-detecting bioelectronic nose based on odor cue memory combined with brain-computer interface and optogenetic regulation technologies. First, the rats were trained to generate positive memories of drug odors through food reward training, and multichannel microelectrodes were implanted into the DG region of the hippocampus for responsible memory retrieval, the spike signals of individual neurons and the local field potential signals of population neurons in the brain region were collected for pattern recognition and analysis. Preliminary experimental results have shown that when low-dose drugs are buried in a hidden area, rats can find the location of the drugs in a very short time, and when close to the relevant area, there is a significant change in the energy value and time-frequency spectrum signal coupling of the returned data, which can be extracted to indicate that the rats have found the drugs. Second, we labled the neuronal activity marker c-fos and revealed more robust activation in the DG region following odor detection. We modulated these neurons through neuroregulatory technology, so that the rats could recognize drugs by retrieving memories more quickly. We conceive that the drug-detecting rat robot can detect trace amounts of various drugs in complex terrain and multiple scenes, which is of great significance for anti-drug work in the future.}, }
@article {pmid37920562, year = {2023}, author = {Sankaran, N and Moses, D and Chiong, W and Chang, EF}, title = {Recommendations for promoting user agency in the design of speech neuroprostheses.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1298129}, pmid = {37920562}, issn = {1662-5161}, abstract = {Brain-computer interfaces (BCI) that directly decode speech from brain activity aim to restore communication in people with paralysis who cannot speak. Despite recent advances, neural inference of speech remains imperfect, limiting the ability for speech BCIs to enable experiences such as fluent conversation that promote agency - that is, the ability for users to author and transmit messages enacting their intentions. Here, we make recommendations for promoting agency based on existing and emerging strategies in neural engineering. The focus is on achieving fast, accurate, and reliable performance while ensuring volitional control over when a decoder is engaged, what exactly is decoded, and how messages are expressed. Additionally, alongside neuroscientific progress within controlled experimental settings, we argue that a parallel line of research must consider how to translate experimental successes into real-world environments. While such research will ultimately require input from prospective users, here we identify and describe design choices inspired by human-factors work conducted in existing fields of assistive technology, which address practical issues likely to emerge in future real-world speech BCI applications.}, }
@article {pmid37920561, year = {2023}, author = {Schmoigl-Tonis, M and Schranz, C and Müller-Putz, GR}, title = {Methods for motion artifact reduction in online brain-computer interface experiments: a systematic review.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1251690}, pmid = {37920561}, issn = {1662-5161}, abstract = {Brain-computer interfaces (BCIs) have emerged as a promising technology for enhancing communication between the human brain and external devices. Electroencephalography (EEG) is particularly promising in this regard because it has high temporal resolution and can be easily worn on the head in everyday life. However, motion artifacts caused by muscle activity, fasciculation, cable swings, or magnetic induction pose significant challenges in real-world BCI applications. In this paper, we present a systematic review of methods for motion artifact reduction in online BCI experiments. Using the PRISMA filter method, we conducted a comprehensive literature search on PubMed, focusing on open access publications from 1966 to 2022. We evaluated 2,333 publications based on predefined filtering rules to identify existing methods and pipelines for motion artifact reduction in EEG data. We present a lookup table of all papers that passed the defined filters, all used methods, and pipelines and compare their overall performance and suitability for online BCI experiments. We summarize suitable methods, algorithms, and concepts for motion artifact reduction in online BCI applications, highlight potential research gaps, and discuss existing community consensus. This review aims to provide a comprehensive overview of the current state of the field and guide researchers in selecting appropriate methods for motion artifact reduction in online BCI experiments.}, }
@article {pmid37920297, year = {2023}, author = {Sebastián-Romagosa, M and Cho, W and Ortner, R and Sieghartsleitner, S and Von Oertzen, TJ and Kamada, K and Laureys, S and Allison, BZ and Guger, C}, title = {Brain-computer interface treatment for gait rehabilitation in stroke patients.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1256077}, pmid = {37920297}, issn = {1662-4548}, abstract = {The use of Brain-Computer Interfaces (BCI) as rehabilitation tools for chronically ill neurological patients has become more widespread. BCIs combined with other techniques allow the user to restore neurological function by inducing neuroplasticity through real-time detection of motor-imagery (MI) as patients perform therapy tasks. Twenty-five stroke patients with gait disability were recruited for this study. Participants performed 25 sessions with the MI-BCI and assessment visits to track functional changes during the therapy. The results of this study demonstrated a clinically significant increase in walking speed of 0.19 m/s, 95%CI [0.13-0.25], p < 0.001. Patients also reduced spasticity and improved their range of motion and muscle contraction. The BCI treatment was effective in promoting long-lasting functional improvements in the gait speed of chronic stroke survivors. Patients have more movements in the lower limb; therefore, they can walk better and safer. This functional improvement can be explained by improved neuroplasticity in the central nervous system.}, }
@article {pmid37919371, year = {2023}, author = {Ke, Y and Liu, S and Chen, L and Wang, X and Ming, D}, title = {Lasting enhancements in neural efficiency by multi-session transcranial direct current stimulation during working memory training.}, journal = {NPJ science of learning}, volume = {8}, number = {1}, pages = {48}, pmid = {37919371}, issn = {2056-7936}, abstract = {The neural basis for long-term behavioral improvements resulting from multi-session transcranial direct current stimulation (tDCS) combined with working memory training (WMT) remains unclear. In this study, we used task-related electroencephalography (EEG) measures to investigate the lasting neurophysiological effects of anodal high-definition (HD)-tDCS applied over the left dorsolateral prefrontal cortex (dlPFC) during a challenging WMT. Thirty-four healthy young adults were randomized to sham or active tDCS groups and underwent ten 30-minute training sessions over ten consecutive days, preceded by a pre-test and followed by post-tests performed one day and three weeks after the last session, respectively, by performing high-load WM tasks along with EEG recording. Multi-session HD-tDCS significantly enhanced the behavioral benefits of WMT. Compared to the sham group, the active group showed facilitated increases in theta, alpha, beta, and gamma task-related oscillations at the end of training and significantly increased P300 response 3 weeks post-training. Our findings suggest that applying anodal tDCS over the left dlPFC during multi-session WMT can enhance the behavioral benefits of WMT and facilitate sustained improvements in WM-related neural efficiency.}, }
@article {pmid37918367, year = {2023}, author = {Myhrum, M and Heldahl, MG and Rødvik, AK and Tvete, OE and Jablonski, GE}, title = {Validation of the Norwegian Version of the Speech, Spatial and Qualities of Hearing Scale (SSQ).}, journal = {Audiology &amp; neuro-otology}, volume = {}, number = {}, pages = {1-12}, doi = {10.1159/000534197}, pmid = {37918367}, issn = {1421-9700}, abstract = {INTRODUCTION: The main objective of the study was to validate the Norwegian translation of the Speech, Spatial and Qualities of Hearing Scale (SSQ) and investigate the SSQ disability profiles in a cochlear implant (CI) user population.<br><br>METHODS: The study involved 152 adult CI users. The mean age at implantation was 55 (standard deviation [SD] = 16), and the mean CI experience was 5 years (SD = 4.8). The cohort was split into three groups depending on the hearing modality: bilateral CIs (BCIs), a unilateral CI (UCI), and bimodal (CI plus contralateral hearing aid; HCI). The SSQ disability profiles of each group were compared with those observed in similar studies using the English version and other translations of the SSQ. Standard values, internal consistency, sensitivity, and floor and ceiling effects were investigated, and the missing-response rates to specific questions were calculated. Relationships to speech perception were measured using monosyllabic word scores and the Norwegian Hearing in Noise Test scores.<br><br>RESULTS: In the BCI group, the average scores were around 5.0 for the speech and spatial sections and 7.0 for the qualities section (SD &#x223c;2). The average scores of the UCI and HCI groups were about one point lower than those of the BCI group. The SSQ disability profiles were comparable to the profiles in similar studies. The slopes of the linear regression lines measuring the relationships between the SSQ speech and monosyllabic word scores were 0.8 per 10% increase in the monosyllabic word score for the BCI group (explaining 35% of the variation) and 0.4 for the UCI and HCI groups (explaining 22-23% of the variation).<br><br>CONCLUSION: The Norwegian version of the SSQ measures hearing disability similar to the original English version, and the internal consistency is good. Differences in the recipients' pre-implantation variables could explain some variations we observed in the SSQ responses, and such predictors should be investigated. Data aggregation will be possible using the SSQ as a routine clinical assessment in global CI populations. Moreover, pre-implantation variables should be systematically registered so that they can be used in mixed-effects models.}, }
@article {pmid37917713, year = {2023}, author = {Lai, C and Tanaka, S and Harris, TD and Lee, AK}, title = {Volitional activation of remote place representations with a hippocampal brain-machine interface.}, journal = {Science (New York, N.Y.)}, volume = {382}, number = {6670}, pages = {566-573}, doi = {10.1126/science.adh5206}, pmid = {37917713}, issn = {1095-9203}, mesh = {Humans ; Rats ; Animals ; *Brain-Computer Interfaces ; Hippocampus/physiology ; Mental Recall/physiology ; *Memory, Episodic ; Imagination/physiology ; }, abstract = {The hippocampus is critical for recollecting and imagining experiences. This is believed to involve voluntarily drawing from hippocampal memory representations of people, events, and places, including maplike representations of familiar environments. However, whether representations in such "cognitive maps" can be volitionally accessed is unknown. We developed a brain-machine interface to test whether rats can do so by controlling their hippocampal activity in a flexible, goal-directed, and model-based manner. We found that rats can efficiently navigate or direct objects to arbitrary goal locations within a virtual reality arena solely by activating and sustaining appropriate hippocampal representations of remote places. This provides insight into the mechanisms underlying episodic memory recall, mental simulation and planning, and imagination and opens up possibilities for high-level neural prosthetics that use hippocampal representations.}, }
@article {pmid37917674, year = {2023}, author = {Coulter, ME and Kemere, C}, title = {The neural basis of mental navigation in rats.}, journal = {Science (New York, N.Y.)}, volume = {382}, number = {6670}, pages = {517-518}, doi = {10.1126/science.adl0806}, pmid = {37917674}, issn = {1095-9203}, mesh = {Rats ; Animals ; *Brain-Computer Interfaces ; Hippocampus ; *Spatial Navigation ; }, abstract = {A brain-machine interface demonstrates volitional control of hippocampal activity.}, }
@article {pmid37915755, year = {2023}, author = {Rainey, S}, title = {A gap between reasons for skilled use of BCI speech devices and reasons for utterances, with implications for speech ownership.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1248806}, pmid = {37915755}, issn = {1662-5161}, abstract = {The skilled use of a speech BCI device will draw upon practical experience gained through the use of that very device. The reasons a user may have for using a device in a particular way, reflecting that skill gained via familiarity with the device, may differ significantly from the reasons that a speaker might have for their utterances. The potential divergence between reasons constituting skilled use and BCI-mediated speech output may serve to make clear an instrumental relationship between speaker and BCI speech device. This will affect the way in which the device and the speech it produces for the user can be thought of as being "reasons responsive", hence the way in which the user can be said to be in control of their device. Ultimately, this divergence will come down to how ownership of produced speech can be considered. The upshot will be that skillful use of a synthetic speech device might include practices that diverge from standard speech in significant ways. This might further indicate that synthetic speech devices ought to be considered as different from, not continuous with, standard speech.}, }
@article {pmid37915592, year = {2023}, author = {Wu, GK and Ardeshirpour, Y and Mastracchio, C and Kent, J and Caiola, M and Ye, M}, title = {Amplitude- and frequency-dependent activation of layer II/III neurons by intracortical microstimulation.}, journal = {iScience}, volume = {26}, number = {11}, pages = {108140}, pmid = {37915592}, issn = {2589-0042}, abstract = {Intracortical microstimulation (ICMS) has been used for the development of brain machine interfaces. However, further understanding about the spatiotemporal responses of neurons to different electrical stimulation parameters is necessary to inform the design of optimal therapies. In this study, we employed in vivo electrophysiological recording, two-photon calcium imaging, and electric field simulation to evaluate the acute effect of ICMS on layer II/III neurons. Our results show that stimulation frequency non-linearly modulates neuronal responses, whereas the magnitude of responses is linearly correlated to the electric field strength and stimulation amplitude before reaching a steady state. Temporal dynamics of neurons' responses depends more on stimulation frequency and their distance to the stimulation electrode. In addition, amplitude-dependent post-stimulation suppression was observed within ∼500 μm of the stimulation electrode, as evidenced by both calcium imaging and local field potentials. These findings provide insights for selecting stimulation parameters to achieve desirable spatiotemporal specificity of ICMS.}, }
@article {pmid37915185, year = {2023}, author = {Lee, HG and Jung, IH and Park, BS and Yang, HR and Kim, KK and Tu, TH and Yeh, JY and Lee, S and Yang, S and Lee, BJ and Kim, JG and Nam-Goong, IS}, title = {Altered Metabolic Phenotypes and Hypothalamic Neuronal Activity Triggered by Sodium-Glucose Cotransporter 2 Inhibition.}, journal = {Diabetes &amp; metabolism journal}, volume = {}, number = {}, pages = {}, doi = {10.4093/dmj.2022.0261}, pmid = {37915185}, issn = {2233-6087}, abstract = {BACKGROUND: Sodium-glucose cotransporter 2 (SGLT-2) inhibitors are currently used to treat patients with diabetes. Previous studies have demonstrated that treatment with SGLT-2 inhibitors is accompanied by altered metabolic phenotypes. However, it has not been investigated whether the hypothalamic circuit participates in the development of the compensatory metabolic phenotypes triggered by the treatment with SGLT-2 inhibitors.<br><br>METHODS: Mice were fed a standard diet or high-fat diet and treated with dapagliflozin, an SGLT-2 inhibitor. Food intake and energy expenditure were observed using indirect calorimetry system. The activity of hypothalamic neurons in response to dapagliflozin treatment was evaluated by immunohistochemistry with c-Fos antibody. Quantitative real-time polymerase chain reaction was performed to determine gene expression patterns in the hypothalamus of dapagliflozin-treated mice.<br><br>RESULTS: Dapagliflozin-treated mice displayed enhanced food intake and reduced energy expenditure. Altered neuronal activities were observed in multiple hypothalamic nuclei in association with appetite regulation. Additionally, we found elevated immunosignals of agouti-related peptide neurons in the paraventricular nucleus of the hypothalamus.<br><br>CONCLUSION: This study suggests the functional involvement of the hypothalamus in the development of the compensatory metabolic phenotypes induced by SGLT-2 inhibitor treatment.}, }
@article {pmid37914959, year = {2023}, author = {Zhu, L and Yu, F and Huang, A and Ying, N and Zhang, J}, title = {Instance-representation transfer method based on joint distribution and deep adaptation for EEG emotion recognition.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {37914959}, issn = {1741-0444}, support = {2020C04009//Key Research and Development Project of Zhejiang Province/ ; 2020E10010//Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province/ ; }, abstract = {Electroencephalogram (EEG) emotion recognition technology is essential for improving human-computer interaction. However, the practical application of emotion recognition technology is limited due to the variety of subjects and sessions. Transfer learning has been applied to address this issue and has received extensive research and application. Studies mainly concentrate on either instance transfer or representation transfer methods. This paper proposes an emotion recognition method called Joint Distributed Instances Represent Transfer (JD-IRT), which includes two core components: Joint Distribution Deep Adaptation (JDDA) and Instance-Representation Transfer (I-RT). Specifically, JDDA is different from common representation transfer methods in transfer learning. It bridges the discrepancies of marginal and conditional distributions simultaneously and combines multiple adaptive layers and kernels for deep domain adaptation. On the other hand, I-RT utilizes instance transfer to select source domain data for better representation transfer. We performed experiments and compared them with other representative methods in the SEED, SEED-IV, and SEED-V datasets. In cross-subject experiments, our approach achieved an average accuracy of 83.21% in SEED, 52.12% in SEED-IV, and 60.17% in SEED-V. Similarly, in cross-session experiments, the accuracy was 91.29% in SEED, 59.02% in SEED-IV, and 65.91% in SEED-V. These results demonstrate the improvement in the accuracy of EEG emotion recognition using the proposed approach.}, }
@article {pmid37914729, year = {2023}, author = {Sharma, N and Upadhyay, A and Sharma, M and Singhal, A}, title = {Deep temporal networks for EEG-based motor imagery recognition.}, journal = {Scientific reports}, volume = {13}, number = {1}, pages = {18813}, pmid = {37914729}, issn = {2045-2322}, mesh = {*Movement ; *Brain-Computer Interfaces ; Neural Networks, Computer ; Imagery, Psychotherapy/methods ; Algorithms ; Electroencephalography/methods ; Imagination ; }, abstract = {The electroencephalogram (EEG) based motor imagery (MI) signal classification, also known as motion recognition, is a highly popular area of research due to its applications in robotics, gaming, and medical fields. However, the problem is ill-posed as these signals are non-stationary and noisy. Recently, a lot of efforts have been made to improve MI signal classification using a combination of signal decomposition and machine learning techniques but they fail to perform adequately on large multi-class datasets. Previously, researchers have implemented long short-term memory (LSTM), which is capable of learning the time-series information, on the MI-EEG dataset for motion recognition. However, it can not model very long-term dependencies present in the motion recognition data. With the advent of transformer networks in natural language processing (NLP), the long-term dependency issue has been widely addressed. Motivated by the success of transformer algorithms, in this article, we propose a transformer-based deep learning neural network architecture that performs motion recognition on the raw BCI competition III IVa and IV 2a datasets. The validation results show that the proposed method achieves superior performance than the existing state-of-the-art methods. The proposed method produces classification accuracy of 99.7% and 84% on the binary class and the multi-class datasets, respectively. Further, the performance of the proposed transformer-based model is also compared with LSTM.}, }
@article {pmid37910621, year = {2023}, author = {Wang, X and Wu, X and Wu, H and Xiao, H and Hao, S and Wang, B and Li, C and Bleymehl, K and Kauschke, SG and Mack, V and Ferger, B and Klein, H and Zheng, R and Duan, S and Wang, H}, title = {Neural adaption in midbrain GABAergic cells contributes to high-fat diet-induced obesity.}, journal = {Science advances}, volume = {9}, number = {44}, pages = {eadh2884}, pmid = {37910621}, issn = {2375-2548}, mesh = {Mice ; Animals ; *Diet, High-Fat/adverse effects ; *Calcium/metabolism ; Obesity/etiology/metabolism ; Adipose Tissue, White/metabolism ; Mesencephalon ; Mice, Inbred C57BL ; }, abstract = {Overeating disorders largely contribute to worldwide incidences of obesity. Available treatments are limited. Here, we discovered that long-term chemogenetic activation of ventrolateral periaqueductal gray (vlPAG) GABAergic cells rescue obesity of high-fat diet-induced obesity (DIO) mice. This was associated with the recovery of enhanced mIPSCs, decreased food intake, increased energy expenditure, and inguinal white adipose tissue (iWAT) browning. In vivo calcium imaging confirmed vlPAG GABAergic suppression for DIO mice, with corresponding reduction in intrinsic excitability. Single-nucleus RNA sequencing identified transcriptional expression changes in GABAergic cell subtypes in DIO mice, highlighting Cacna2d1 as of potential importance. Overexpressing CACNA2D1 in vlPAG GABAergic cells of DIO mice rescued enhanced mIPSCs and calcium response, reversed obesity, and therefore presented here as a potential target for obesity treatment.}, }
@article {pmid37910541, year = {2023}, author = {Ortiz, O and Kuruganti, U and Chester, V and Wilson, A and Blustein, DH}, title = {Changes in EEG alpha-band power during prehension indicates neural motor drive inhibition.}, journal = {Journal of neurophysiology}, volume = {}, number = {}, pages = {}, doi = {10.1152/jn.00506.2022}, pmid = {37910541}, issn = {1522-1598}, support = {RGPIN_2021-02638//Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (NSERC)/ ; }, abstract = {Changes in alpha band activity (8-12 Hz) indicate the inhibition of brain regions during cognitive tasks, reflecting real-time cognitive load. Despite this, its feasibility to be used in a more dynamic environment with ongoing motor corrections has not been studied. This research used electroencephalography (EEG) to explore how different brain regions are engaged during a simple grasp and lift task where unexpected changes to the object's weight or surface friction are introduced. The results suggest that alpha activity changes related to motor error correction occur only in motor-related areas (i.e. central areas), but not in error processing areas (ie. fronto-parietal network) during unexpected weight changes. This suggests that oscillations over motor areas reflect reduction of motor drive related to motor error correction, thus being a potential cortical electrophysiological biomarker for the process, and not solely as a proxy for cognitive demands. This observation is particularly relevant in scenarios where these signals are used to evaluate high cognitive demands co-occurring with high levels of motor errors and corrections, such as prosthesis use. The establishment of electrophysiological biomarkers of mental resource allocation during movement and cognition can help identify indicators of mental workload and motor drive, which may be useful for improving brain-machine interfaces.}, }
@article {pmid37910412, year = {2023}, author = {Tang, Z and Wang, H and Cui, Z and Jin, X and Zhang, L and Peng, Y and Xing, B}, title = {An upper-limb rehabilitation exoskeleton system controlled by MI recognition model with deep emphasized informative features in a VR scene.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3329059}, pmid = {37910412}, issn = {1558-0210}, abstract = {The prevalence of stroke continues to increase with the global aging. Based on the motor imagery (MI) brain-computer interface (BCI) paradigm and virtual reality (VR) technology, we designed and developed an upper-limb rehabilitation exoskeleton system (VR-ULE) in the VR scenes for stroke patients. The VR-ULE system makes use of the MI electroencephalogram (EEG) recognition model with a convolutional neural network and squeeze-and-excitation (SE) blocks to obtain the patient's motion intentions and control the exoskeleton to move during rehabilitation training movement. Due to the individual differences in EEG, the frequency bands with optimal MI EEG features for each patient are different. Therefore, the weight of different feature channels is learned by combining SE blocks to emphasize the useful information frequency band features. The MI cues in the VR-based virtual scenes can improve the interhemispheric balance and the neuroplasticity of patients. It also makes up for the disadvantages of the current MI-BCIs, such as single usage scenarios, poor individual adaptability, and many interfering factors. We designed the offline training experiment to evaluate the feasibility of the EEG recognition strategy, and designed the online control experiment to verify the effectiveness of the VR-ULE system. The results showed that the MI classification method with MI cues in the VR scenes improved the accuracy of MI classification (86.49%±3.02%); all subjects performed two types of rehabilitation training tasks under their own models trained in the offline training experiment, with the highest average completion rates of 86.82%±4.66% and 88.48%±5.84%. The VR-ULE system can efficiently help stroke patients with hemiplegia complete upper-limb rehabilitation training tasks, and provide the new methods and strategies for BCI-based rehabilitation devices.}, }
@article {pmid37909251, year = {2023}, author = {Wang, S and Jiang, C and Cao, K and Li, R and Gao, Z and Wang, Y}, title = {HK2 in microglia and macrophages contribute to the development of neuropathic pain.}, journal = {Glia}, volume = {}, number = {}, pages = {}, doi = {10.1002/glia.24482}, pmid = {37909251}, issn = {1098-1136}, support = {81772382//National Natural Science Foundation of China/ ; 2020C03042//Science Technology Department of Zhejiang Province/ ; }, abstract = {Neuropathic pain is a complex pain condition accompanied by prominent neuroinflammation involving activation of both central and peripheral immune cells. Metabolic switch to glycolysis is an important feature of activated immune cells. Hexokinase 2 (HK2), a key glycolytic enzyme enriched in microglia, has recently been shown important in regulating microglial functions. Whether and how HK2 is involved in neuropathic pain-related neuroinflammation remains unknown. Using a HK2-tdTomato reporter line, we found that HK2 was prominently elevated in spinal microglia. Pharmacological inhibition of HK2 effectively alleviated nerve injury-induced acute mechanical pain. However, selective ablation of Hk2 in microglia reduced microgliosis in the spinal dorsal horn (SDH) with little analgesic effects. Further analyses showed that nerve injury also significantly induced HK2 expression in dorsal root ganglion (DRG) macrophages. Deletion of Hk2 in myeloid cells, including both DRG macrophages and spinal microglia, led to the alleviation of mechanical pain during the first week after injury, along with attenuated microgliosis in the ipsilateral SDH, macrophage proliferation in DRGs, and suppressed inflammatory responses in DRGs. These data suggest that HK2 plays an important role in regulating neuropathic pain-related immune cell responses at acute phase and that HK2 contributes to neuropathic pain onset primarily through peripheral monocytes and DRG macrophages rather than spinal microglia.}, }
@article {pmid37907477, year = {2023}, author = {Zhong, C and Liao, K and Dai, T and Wei, M and Ma, H and Wu, J and Zhang, Z and Ye, Y and Luo, Y and Chen, Z and Jian, J and Sun, C and Tang, B and Zhang, P and Liu, R and Li, J and Yang, J and Li, L and Liu, K and Hu, X and Lin, H}, title = {Graphene/silicon heterojunction for reconfigurable phase-relevant activation function in coherent optical neural networks.}, journal = {Nature communications}, volume = {14}, number = {1}, pages = {6939}, pmid = {37907477}, issn = {2041-1723}, support = {61975179//National Natural Science Foundation of China (National Science Foundation of China)/ ; 91950204//National Natural Science Foundation of China (National Science Foundation of China)/ ; 92150302//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62105287//National Natural Science Foundation of China (National Science Foundation of China)/ ; 12104375//National Natural Science Foundation of China (National Science Foundation of China)/ ; 91950204//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Optical neural networks (ONNs) herald a new era in information and communication technologies and have implemented various intelligent applications. In an ONN, the activation function (AF) is a crucial component determining the network performances and on-chip AF devices are still in development. Here, we first demonstrate on-chip reconfigurable AF devices with phase activation fulfilled by dual-functional graphene/silicon (Gra/Si) heterojunctions. With optical modulation and detection in one device, time delays are shorter, energy consumption is lower, reconfigurability is higher and the device footprint is smaller than other on-chip AF strategies. The experimental modulation voltage (power) of our Gra/Si heterojunction achieves as low as 1 V (0.5 mW), superior to many pure silicon counterparts. In the photodetection aspect, a high responsivity of over 200 mA/W is realized. Special nonlinear functions generated are fed into a complex-valued ONN to challenge handwritten letters and image recognition tasks, showing improved accuracy and potential of high-efficient, all-component-integration on-chip ONN. Our results offer new insights for on-chip ONN devices and pave the way to high-performance integrated optoelectronic computing circuits.}, }
@article {pmid37907096, year = {2023}, author = {Sun, S and Li, J and Wang, S and Li, J and Ren, J and Bao, Z and Sun, L and Ma, X and Zheng, F and Ma, S and Sun, L and Wang, M and Yu, Y and Ma, M and Wang, Q and Chen, Z and Ma, H and Wang, X and Wu, Z and Zhang, H and Yan, K and Yang, Y and Zhang, Y and Zhang, S and Lei, J and Teng, ZQ and Liu, CM and Bai, G and Wang, YJ and Li, J and Wang, X and Zhao, G and Jiang, T and Belmonte, JCI and Qu, J and Zhang, W and Liu, GH}, title = {CHIT1-positive microglia drive motor neuron aging in the primate spinal cord.}, journal = {Nature}, volume = {}, number = {}, pages = {}, doi = {10.1038/s41586-023-06783-1}, pmid = {37907096}, issn = {1476-4687}, abstract = {Aging is a critical factor in spinal cord-associated disorders[1], yet aging-specific mechanisms underlying this relationship remain poorly understood. To address this knowledge gap, we combined single-nucleus RNA sequencing with behavioral and neurophysiological analysis in non-human primates (NHPs). We identified motor neuron senescence and neuroinflammation with microglial hyperactivation as intertwined hallmarks of spinal cord aging. As an underlying mechanism, we identified a previously unreported neurotoxic microglial state demarcated by elevated expression of CHIT1 (a secreted mammalian chitinase) specific to the aged spinal cords in NHP and human biopsies. In the aged spinal cord, CHIT1-positive microglia preferentially localize around motor neurons (MNs), and they are capable of triggering senescence, partly by activating SMAD signaling. We further validated the driving role of secreted CHIT1 on MN senescence by multi-modal experiments both in vivo, utilizing the NHP spinal cord as a model and in vitro, employing a sophisticated human MN-and-microenvironment interplay modeling system. Moreover, we demonstrated that ascorbic acid, a geroprotective compound, counteracted the pro-senescent effect of CHIT1 and mitigated motor neuron senescence in aged monkeys. Our findings provide the first single-cell resolution cellular and molecular landscape of the aged primate spinal cord and identify a new biomarker and intervention target for spinal cord degeneration.}, }
@article {pmid37906969, year = {2023}, author = {Peng, D and Zheng, WL and Liu, L and Jiang, WB and Li, Z and Lu, Y and Lu, BL}, title = {Identifying sex differences in EEG-based emotion recognition using graph convolutional network with attention mechanism.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad085a}, pmid = {37906969}, issn = {1741-2552}, abstract = {OBJECTIVE: Sex differences in emotions have been widely perceived via self-reports, peripheral physiological signals and brain imaging techniques. However, how sex differences are reflected in the EEG neural patterns of emotions remains unresolved. In this paper, we detect sex differences in emotional EEG patterns, investigate the consistency of such differences in various emotion datasets across cultures, and study how sex as a factor affects the performance of EEG-based emotion recognition models.<br><br>APPROACH: We thoroughly assess sex differences in emotional EEG patterns on five public datasets, including SEED, SEED-IV, SEED-V, DEAP and DREAMER, systematically examine the sex-specific EEG patterns for happy, sad, fearful, disgusted and neutral emotions, and implement deep learning models for sex-specific emotion recognition.<br><br>MAIN RESULTS: (1) Sex differences exist in various emotion types and both Western and Eastern cultures; (2) The emotion patterns of females are more stable than those of males, and the patterns of happiness from females are in sharp contrast with the patterns of sadness, fear and disgust, while the energy levels are more balanced for males; (3) The key features for emotion recognition are mainly located at the frontal and temporal sites for females and distributed more evenly over the whole brain for males, and (4) The same-sex emotion recognition models outperform the corresponding cross-sex models.<br><br>SIGNIFICANCE: These findings extend efforts to characterize sex differences in emotional brain activation, provide new physiological evidence for sex-specific emotion processing, and reinforce the message that sex differences should be carefully considered in affective research and precision medicine.}, }
@article {pmid37906489, year = {2023}, author = {Meng, J and Liu, H and Wu, Q and Zhou, H and Shi, W and Meng, L and Xu, M and Ming, D}, title = {A SSVEP-based brain-computer interface with low-pixel density of stimuli.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3328917}, pmid = {37906489}, issn = {1558-0210}, abstract = {The brain-computer interface (BCI) based on the steady-state visual evoked potential (SSVEP) has drawn widespread attention due to its high communication speed and low individual variability. However, there is still a need to enhance the comfort of SSVEP-BCI, especially considering the assurance of its effectiveness. This study aims to achieve a perfect balance between comfort and effectiveness by reducing the pixel density of SSVEP stimuli. Three experiments were conducted to determine the most suitable presentation form (flickering square vs. flickering checkerboard), pixel distribution pattern (random vs. uniform), and pixel density value (100%, 90%, 80%, 70%, 60%, 40%, 20%). Subjects' electroencephalogram (EEG) and fatigue scores were recorded, while comfort and effectiveness were measured by fatigue score and classification accuracy, respectively. The results showed that the flickering square with random pixel distribution achieved a lower fatigue score and higher accuracy. EEG responses induced by stimuli with a square-random presentation mode were then compared across various pixel densities. In both offline and online tests, the fatigue score decreased as the pixel density decreased. Strikingly, when the pixel density was above 60%, the accuracies of low-pixel density SSVEP were all satisfactory (>90%) and showed no significant difference with that of the conventional 100%-pixel density. These results support the feasibility of using 60%-pixel density with a square-random presentation mode to improve the comfort of SSVEP-BCI, thereby promoting its practical applications in communication and control.}, }
@article {pmid37905887, year = {2024}, author = {Lian, Y and Wu, C and Liu, L and Li, X}, title = {Prediction of cell-cell communication patterns of dorsal root ganglion cells: single-cell RNA sequencing data analysis.}, journal = {Neural regeneration research}, volume = {19}, number = {6}, pages = {1367-1374}, doi = {10.4103/1673-5374.384067}, pmid = {37905887}, issn = {1673-5374}, }
@article {pmid37905305, year = {2023}, author = {Mao, T and Fang, Z and Chai, Y and Deng, Y and Rao, J and Quan, P and Goel, N and Basner, M and Guo, B and Dinges, DF and Liu, J and Detre, JA and Rao, H}, title = {Sleep deprivation attenuates neural responses to outcomes from risky decision-making.}, journal = {Psychophysiology}, volume = {}, number = {}, pages = {e14465}, doi = {10.1111/psyp.14465}, pmid = {37905305}, issn = {1540-5958}, support = {2021M692150//China Postdoctoral Science Foundation/ ; R01-HL102119//Foundation for the National Institutes of Health/ ; R01-MH107571//Foundation for the National Institutes of Health/ ; 32200889//National Natural Science Foundation of China/ ; 2021KFKT012//Shanghai International Studies University Research Projects/ ; 2020367//Shanghai Post-doctoral Excellence Program/ ; }, abstract = {Sleep loss impacts a broad range of brain and cognitive functions. However, how sleep deprivation affects risky decision-making remains inconclusive. This study used functional MRI to examine the impact of one night of total sleep deprivation (TSD) on risky decision-making behavior and the underlying brain responses in healthy adults. In this study, we analyzed data from N = 56 participants in a strictly controlled 5-day and 4-night in-laboratory study using a modified Balloon Analogue Risk Task. Participants completed two scan sessions in counter-balanced order, including one scan during rested wakefulness (RW) and another scan after one night of TSD. Results showed no differences in participants' risk-taking propensity and risk-induced activation between RW and TSD. However, participants showed significantly reduced neural activity in the anterior cingulate cortex and bilateral insula for loss outcomes, and in bilateral putamen for win outcomes during TSD compared with RW. Moreover, risk-induced activation in the insula negatively correlated with participants' risk-taking propensity during RW, while no such correlations were observed after TSD. These findings suggest that sleep loss may impact risky decision-making by attenuating neural responses to decision outcomes and impairing brain-behavior associations.}, }
@article {pmid37905046, year = {2023}, author = {Forenzo, D and Zhu, H and Shanahan, J and Lim, J and He, B}, title = {Continuous Tracking using Deep Learning-based Decoding for Non-invasive Brain-Computer Interface.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.10.12.562084}, pmid = {37905046}, abstract = {Brain-computer interfaces (BCI) using electroencephalography (EEG) provide a non-invasive method for users to interact with external devices without the need for muscle activation. While noninvasive BCIs have the potential to improve the lives of both healthy and motor impaired individuals, they currently have limited applications due to inconsistent performance and low degrees of freedom. In this study, we use deep-learning (DL)-based decoders for online Continuous Pursuit (CP), a complex BCI task requiring the user to track an object in 2D space. We developed a new labelling system to use CP data for supervised learning, trained DL-based decoders based on two architectures, including a newly proposed adaptation of the PointNet architecture, and evaluated the performance over several online sessions. We rigorously evaluated the DL-based decoders in a total of 28 human subjects, and found that the DL-based models improved throughout the sessions as more training data became available and significantly outperformed a traditional BCI decoder by the last session. We also performed additional experiments to test an implementation of transfer learning by pre-training models on data from other subjects, and mid-session training to reduce inter-session variability. The results from these experiments show that pre-training did not significantly improve performance, but updating the models mid-session may have some benefit. Overall, these findings support the use of DL-based decoders for improving BCI performance in complex tasks like CP, which can expand the potential applications of BCI devices and help improve the lives of both healthy individuals and motor-impaired patients.}, }
@article {pmid37904095, year = {2023}, author = {Mohammad, A and Siddiqui, F and Alam, MA and Idrees, SM}, title = {Tri-model classifiers for EEG based mental task classification: hybrid optimization assisted framework.}, journal = {BMC bioinformatics}, volume = {24}, number = {1}, pages = {406}, pmid = {37904095}, issn = {1471-2105}, mesh = {Humans ; *Algorithms ; *Electroencephalography/methods ; Neural Networks, Computer ; Emotions/physiology ; }, abstract = {The commercial adoption of BCI technologies for both clinical and non-clinical applications is drawing scientists to the creation of wearable devices for daily living. Emotions are essential to human existence and have a significant impact on thinking. Emotion is frequently linked to rational decision-making, perception, interpersonal interaction, and even basic human intellect. The requirement for trustworthy and implementable methods for the detection of individual emotional responses is needed with rising attention of the scientific community towards the establishment of some significant emotional connections among people and computers. This work introduces EEG recognition model, where the input signal is pre-processed using band pass filter. Then, the features like discrete wavelet transform (DWT), band power, spectral flatness, and improved Entropy are extracted. Further, for recognition, tri-classifiers like long short term memory (LSTM), improved deep belief network (DBN) and recurrent neural network (RNN) are used. Also to enhance tri-model classifier performance, the weights of LSTM, improved DBN, and RNN are tuned by model named as shark smell updated BES optimization (SSU-BES). Finally, the perfection of SSU-BES is demonstrated over diverse metrics.}, }
@article {pmid37903108, year = {2023}, author = {Wise, DH and Mores, RM and M Pajda-De La O, J and McCary, MA}, title = {Pattern of seasonal variation in rates of predation between spider families is temporally stable in a food web with widespread intraguild predation.}, journal = {PloS one}, volume = {18}, number = {10}, pages = {e0293176}, pmid = {37903108}, issn = {1932-6203}, mesh = {Humans ; Animals ; Food Chain ; Seasons ; *Spiders ; Predatory Behavior ; *Arthropods ; }, abstract = {Intraguild predation (IGP)-predation between generalist predators (IGPredator and IGPrey) that potentially compete for a shared prey resource-is a common interaction module in terrestrial food webs. Understanding temporal variation in webs with widespread IGP is relevant to testing food web theory. We investigated temporal constancy in the structure of such a system: the spider-focused food web of the forest floor. Multiplex PCR was used to detect prey DNA in 3,300 adult spiders collected from the floor of a deciduous forest during spring, summer, and fall over four years. Because only spiders were defined as consumers, the web was tripartite, with 11 consumer nodes (spider families) and 22 resource nodes: 11 non-spider arthropod taxa (order- or family-level) and the 11 spider families. Most (99%) spider-spider predation was on spider IGPrey, and ~90% of these interactions were restricted to spider families within the same broadly defined foraging mode (cursorial or web-spinning spiders). Bootstrapped-derived confidence intervals (BCI's) for two indices of web structure, restricted connectance and interaction evenness, overlapped broadly across years and seasons. A third index, % IGPrey (% IGPrey among all prey of spiders), was similar across years (~50%) but varied seasonally, with a summer rate (65%) ~1.8x higher than spring and fall. This seasonal pattern was consistent across years. Our results suggest that extensive spider predation on spider IGPrey that exhibits consistent seasonal variation in frequency, and that occurs primarily within two broadly defined spider-spider interaction pathways, must be incorporated into models of the dynamics of forest-floor food webs.}, }
@article {pmid37901886, year = {2023}, author = {O'Shaughnessy, MR and Johnson, WG and Tournas, LN and Rozell, CJ and Rommelfanger, KS}, title = {Neuroethics guidance documents: principles, analysis, and implementation strategies.}, journal = {Journal of law and the biosciences}, volume = {10}, number = {2}, pages = {lsad025}, doi = {10.1093/jlb/lsad025}, pmid = {37901886}, issn = {2053-9711}, abstract = {Innovations in neurotechnologies have ignited conversations about ethics around the world, with implications for researchers, policymakers, and the private sector. The human rights impacts of neurotechnologies have drawn the attention of United Nations bodies; nearly 40 states are tasked with implementing the Organization for Economic Co-operation and Development's principles for responsible innovation in neurotechnology; and the United States is considering placing export controls on brain-computer interfaces. Against this backdrop, we offer the first review and analysis of neuroethics guidance documents recently issued by prominent government, private, and academic groups, focusing on commonalities and divergences in articulated goals; envisioned roles and responsibilities of different stakeholder groups; and the suggested role of the public. Drawing on lessons from the governance of other emerging technologies, we suggest implementation and evaluation strategies to guide practitioners and policymakers in operationalizing these ethical norms in research, business, and policy settings.}, }
@article {pmid37896483, year = {2023}, author = {Di Flumeri, G and Giorgi, A and Germano, D and Ronca, V and Vozzi, A and Borghini, G and Tamborra, L and Simonetti, I and Capotorto, R and Ferrara, S and Sciaraffa, N and Babiloni, F and Aricò, P}, title = {A Neuroergonomic Approach Fostered by Wearable EEG for the Multimodal Assessment of Drivers Trainees.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {20}, pages = {}, pmid = {37896483}, issn = {1424-8220}, mesh = {Humans ; Adolescent ; Reaction Time ; Electroencephalography/methods ; *Wearable Electronic Devices ; *Automobile Driving ; Accidents, Traffic ; }, abstract = {When assessing trainees' progresses during a driving training program, instructors can only rely on the evaluation of a trainee's explicit behavior and their performance, without having any insight about the training effects at a cognitive level. However, being able to drive does not imply knowing how to drive safely in a complex scenario such as the road traffic. Indeed, the latter point involves mental aspects, such as the ability to manage and allocate one's mental effort appropriately, which are difficult to assess objectively. In this scenario, this study investigates the validity of deploying an electroencephalographic neurometric of mental effort, obtained through a wearable electroencephalographic device, to improve the assessment of the trainee. The study engaged 22 young people, without or with limited driving experience. They were asked to drive along five different but similar urban routes, while their brain activity was recorded through electroencephalography. Moreover, driving performance, subjective and reaction times measures were collected for a multimodal analysis. In terms of subjective and performance measures, no driving improvement could be detected either through the driver's subjective measures or through their driving performance. On the other side, through the electroencephalographic neurometric of mental effort, it was possible to catch their improvement in terms of mental performance, with a decrease in experienced mental demand after three repetitions of the driving training tasks. These results were confirmed by the analysis of reaction times, that significantly improved from the third repetition as well. Therefore, being able to measure when a task is less mentally demanding, and so more automatic, allows to deduce the degree of users training, becoming capable of handling additional tasks and reacting to unexpected events.}, }
@article {pmid37894754, year = {2023}, author = {La, VNT and Minh, DDL}, title = {Bayesian Regression Quantifies Uncertainty of Binding Parameters from Isothermal Titration Calorimetry More Accurately Than Error Propagation.}, journal = {International journal of molecular sciences}, volume = {24}, number = {20}, pages = {}, pmid = {37894754}, issn = {1422-0067}, support = {1905324//National Science Foundation/ ; }, mesh = {*Uncertainty ; Bayes Theorem ; Calorimetry/methods ; Thermodynamics ; Protein Binding ; }, abstract = {We compare several different methods to quantify the uncertainty of binding parameters estimated from isothermal titration calorimetry data: the asymptotic standard error from maximum likelihood estimation, error propagation based on a first-order Taylor series expansion, and the Bayesian credible interval. When the methods are applied to simulated experiments and to measurements of Mg(II) binding to EDTA, the asymptotic standard error underestimates the uncertainty in the free energy and enthalpy of binding. Error propagation overestimates the uncertainty for both quantities, except in the simulations, where it underestimates the uncertainty of enthalpy for confidence intervals less than 70%. In both datasets, Bayesian credible intervals are much closer to observed confidence intervals.}, }
@article {pmid37893339, year = {2023}, author = {Hoeferlin, GF and Bajwa, T and Olivares, H and Zhang, J and Druschel, LN and Sturgill, BS and Sobota, M and Boucher, P and Duncan, J and Hernandez-Reynoso, AG and Cogan, SF and Pancrazio, JJ and Capadona, JR}, title = {Antioxidant Dimethyl Fumarate Temporarily but Not Chronically Improves Intracortical Microelectrode Performance.}, journal = {Micromachines}, volume = {14}, number = {10}, pages = {}, pmid = {37893339}, issn = {2072-666X}, support = {R01NS11082//National Institute for Neurological Disorders and Stroke/ ; T32EB004314//National Institute for Biomedical Imaging and Bioengineering/ ; I01RX002611//Department of Veterans Affairs Rehabilitation Research and Development Service/ ; I02RX003077//Department of Veterans Affairs Rehabilitation Research and Development Service/ ; IK6RX003077//Department of Veterans Affairs Rehabilitation Research and Development Service/ ; }, abstract = {Intracortical microelectrode arrays (MEAs) can be used in a range of applications, from basic neuroscience research to providing an intimate interface with the brain as part of a brain-computer interface (BCI) system aimed at restoring function for people living with neurological disorders or injuries. Unfortunately, MEAs tend to fail prematurely, leading to a loss in functionality for many applications. An important contributing factor in MEA failure is oxidative stress resulting from chronically inflammatory-activated microglia and macrophages releasing reactive oxygen species (ROS) around the implant site. Antioxidants offer a means for mitigating oxidative stress and improving tissue health and MEA performance. Here, we investigate using the clinically available antioxidant dimethyl fumarate (DMF) to reduce the neuroinflammatory response and improve MEA performance in a rat MEA model. Daily treatment of DMF for 16 weeks resulted in a significant improvement in the recording capabilities of MEA devices during the sub-chronic (Weeks 5-11) phase (42% active electrode yield vs. 35% for control). However, these sub-chronic improvements were lost in the chronic implantation phase, as a more exacerbated neuroinflammatory response occurs in DMF-treated animals by 16 weeks post-implantation. Yet, neuroinflammation was indiscriminate between treatment and control groups during the sub-chronic phase. Although worse for chronic use, a temporary improvement (<12 weeks) in MEA performance is meaningful. Providing short-term improvement to MEA devices using DMF can allow for improved use for limited-duration studies. Further efforts should be taken to explore the mechanism behind a worsened neuroinflammatory response at the 16-week time point for DMF-treated animals and assess its usefulness for specific applications.}, }
@article {pmid37891775, year = {2023}, author = {Liu, T and Li, B and Zhang, C and Chen, P and Zhao, W and Yan, B}, title = {Real-Time Classification of Motor Imagery Using Dynamic Window-Level Granger Causality Analysis of fMRI Data.}, journal = {Brain sciences}, volume = {13}, number = {10}, pages = {}, pmid = {37891775}, issn = {2076-3425}, support = {62106285//the National Natural Science Foundation of China/ ; }, abstract = {This article presents a method for extracting neural signal features to identify the imagination of left- and right-hand grasping movements. A functional magnetic resonance imaging (fMRI) experiment is employed to identify four brain regions with significant activations during motor imagery (MI) and the effective connections between these regions of interest (ROIs) were calculated using Dynamic Window-level Granger Causality (DWGC). Then, a real-time fMRI (rt-fMRI) classification system for left- and right-hand MI is developed using the Open-NFT platform. We conducted data acquisition and processing on three subjects, and all of whom were recruited from a local college. As a result, the maximum accuracy of using Support Vector Machine (SVM) classifier on real-time three-class classification (rest, left hand, and right hand) with effective connections is 69.3%. And it is 3% higher than that of traditional multivoxel pattern classification analysis on average. Moreover, it significantly improves classification accuracy during the initial stage of MI tasks while reducing the latency effects in real-time decoding. The study suggests that the effective connections obtained through the DWGC method serve as valuable features for real-time decoding of MI using fMRI. Moreover, they exhibit higher sensitivity to changes in brain states. This research offers theoretical support and technical guidance for extracting neural signal features in the context of fMRI-based studies.}, }
@article {pmid37890180, year = {2023}, author = {Rocca, A and Lehner, C and Wafula-Wekesa, E and Luna, E and Fernández-Cornejo, V and Abarca-Olivas, J and Soto-Sánchez, C and Fernández-Jover, E and González-López, P}, title = {Robot-assisted implantation of a microelectrode array in the occipital lobe as a visual prosthesis: technical note.}, journal = {Journal of neurosurgery}, volume = {}, number = {}, pages = {1-8}, doi = {10.3171/2023.8.JNS23772}, pmid = {37890180}, issn = {1933-0693}, abstract = {The prospect of direct interaction between the brain and computers has been investigated in recent decades, revealing several potential applications. One of these is sight restoration in profoundly blind people, which is based on the ability to elicit visual perceptions while directly stimulating the occipital cortex. Technological innovation has led to the development of microelectrodes implantable on the brain surface. The feasibility of implanting a microelectrode on the visual cortex has already been shown in animals, with promising results. Current research has focused on the implantation of microelectrodes into the occipital brain of blind volunteers. The technique raises several technical challenges. In this technical note, the authors suggest a safe and effective approach for robot-assisted implantation of microelectrodes in the occipital lobe for sight restoration.}, }
@article {pmid37887123, year = {2023}, author = {Qin, Y and Zhang, Y and Zhang, Y and Liu, S and Guo, X}, title = {Application and Development of EEG Acquisition and Feedback Technology: A Review.}, journal = {Biosensors}, volume = {13}, number = {10}, pages = {}, pmid = {37887123}, issn = {2079-6374}, support = {12072030//National Natural Science Foundation of China/ ; }, mesh = {Humans ; Feedback ; Electroencephalography ; *Epilepsy/diagnosis ; *Brain-Computer Interfaces ; Emotions ; Signal Processing, Computer-Assisted ; Algorithms ; }, abstract = {This review focuses on electroencephalogram (EEG) acquisition and feedback technology and its core elements, including the composition and principles of the acquisition devices, a wide range of applications, and commonly used EEG signal classification algorithms. First, we describe the construction of EEG acquisition and feedback devices encompassing EEG electrodes, signal processing, and control and feedback systems, which collaborate to measure faint EEG signals from the scalp, convert them into interpretable data, and accomplish practical applications using control feedback systems. Subsequently, we examine the diverse applications of EEG acquisition and feedback across various domains. In the medical field, EEG signals are employed for epilepsy diagnosis, brain injury monitoring, and sleep disorder research. EEG acquisition has revealed associations between brain functionality, cognition, and emotions, providing essential insights for psychologists and neuroscientists. Brain-computer interface technology utilizes EEG signals for human-computer interaction, driving innovation in the medical, engineering, and rehabilitation domains. Finally, we introduce commonly used EEG signal classification algorithms. These classification tasks can identify different cognitive states, emotional states, brain disorders, and brain-computer interface control and promote further development and application of EEG technology. In conclusion, EEG acquisition technology can deepen the understanding of EEG signals while simultaneously promoting developments across multiple domains, such as medicine, science, and engineering.}, }
@article {pmid37885532, year = {2023}, author = {Zotey, V and Andhale, A and Shegekar, T and Juganavar, A}, title = {Adaptive Neuroplasticity in Brain Injury Recovery: Strategies and Insights.}, journal = {Cureus}, volume = {15}, number = {9}, pages = {e45873}, pmid = {37885532}, issn = {2168-8184}, abstract = {This review addresses the relationship between neuroplasticity and recovery from brain damage. Neuroplasticity's ability to adapt becomes crucial since brain injuries frequently result in severe impairments. We begin by describing the fundamentals of neuroplasticity and how it relates to rehabilitation. Examining different forms of brain injuries and their neurological effects highlights the complex difficulties in rehabilitation. By revealing cellular processes, we shed light on synaptic adaptability following damage. Our study of synaptic plasticity digs into axonal sprouting, dendritic remodeling, and the balance of long-term potentiation. These processes depict neural resilience amid change. Then, after damage, we investigate immediate and slow neuroplastic alterations, separating reorganizations that are adaptive from those that are maladaptive. As we go on to rehabilitation, we evaluate techniques that use neuroplasticity's potential. These methods take advantage of the brain's plasticity for healing, from virtual reality and brain-computer interfaces to constraint-induced movement therapy. Ethics and individualized neurorehabilitation are explored. We scrutinize the promise of combination therapy and the difficulties in putting new knowledge into clinical practice. In conclusion, this analysis highlights neuroplasticity's critical role in brain injury recovery, providing sophisticated approaches to improve life after damage.}, }
@article {pmid37883851, year = {2023}, author = {Jiang, X and Fan, J and Zhu, Z and Wang, Z and Guo, Y and Liu, X and Jia, F and Dai, C}, title = {Cybersecurity in neural interfaces: Survey and future trends.}, journal = {Computers in biology and medicine}, volume = {167}, number = {}, pages = {107604}, doi = {10.1016/j.compbiomed.2023.107604}, pmid = {37883851}, issn = {1879-0534}, abstract = {With the joint advancement in areas such as pervasive neural data sensing, neural computing, neuromodulation and artificial intelligence, neural interface has become a promising technology facilitating both the closed-loop neurorehabilitation for neurologically impaired patients and the intelligent man-machine interactions for general application purposes. However, although neural interface has been widely studied, few previous studies focused on the cybersecurity issues in related applications. In this survey, we systematically investigated possible cybersecurity risks in neural interfaces, together with potential solutions to these problems. Importantly, our survey considers interfacing techniques on both central nervous systems (i.e., brain-computer interfaces) and peripheral nervous systems (i.e., general neural interfaces), covering diverse neural modalities such as electroencephalography, electromyography and more. Moreover, our survey is organized on three different levels: (1) the data level, which mainly focuses on the privacy leakage issue via attacking and analyzing neural database of users; (2) the permission level, which mainly focuses on the prospects and risks to directly use real time neural signals as biometrics for continuous and unobtrusive user identity verification; and (3) the model level, which mainly focuses on adversarial attacks and defenses on both the forward neural decoding models (e.g. via machine learning) and the backward feedback implementation models (e.g. via neuromodulation and stimulation). This is the first study to systematically investigate cybersecurity risks and possible solutions in neural interfaces which covers both central and peripheral nervous systems, and considers multiple different levels to provide a complete picture of this issue.}, }
@article {pmid37883287, year = {2023}, author = {Zhong, Y and Yao, L and Wang, Y}, title = {Enhanced Motor Imagery Decoding by Calibration Model Assisted with Tactile ERD.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3327788}, pmid = {37883287}, issn = {1558-0210}, abstract = {OBJECTIVE: In this study, we propose a tactile-assisted calibration method for a motor imagery (MI) based Brain-Computer Interface (BCI) system.<br><br>METHOD: In the proposed calibration, tactile stimulation was applied to the hand wrist to assist the subjects in the MI task, which is named SA-MI task. Then, classifier training in the SA-MI Calibration was performed using the SA-MI data, while the Conventional Calibration employed the MI data. After the classifiers were trained, the performance was evaluated on a common MI dataset.<br><br>RESULTS: Our study demonstrated that the SA-MI Calibration significantly improved the performance as compared with the Conventional Calibration, with a decoding accuracy of (78.3% vs. 71.3%). Moreover, the average calibration time could be reduced by 40%. This benefit of the SA-MI Calibration effect was further validated by an independent control group, which showed no improvement when tactile stimulation was not applied during the calibration phase. Further analysis showed that when compared with MI, greater motor-related cortical activation and higher R[2] value in the alpha-beta frequency band were induced in SA-MI.<br><br>CONCLUSION: Indeed, the SA-MI Calibration could significantly improve the performance and reduce the calibration time as compared with the Conventional Calibration.<br><br>SIGNIFICANCE: The proposed tactile stimulation-assisted MI Calibration method holds great potential for a faster and more accurate system setup at the beginning of BCI usage.}, }
@article {pmid37883285, year = {2023}, author = {Pousson, JE and Shen, YW and Lin, YP and Voicikas, A and Pipinis, E and Bernhofs, V and Burmistrova, L and Griskova-Bulanova, I}, title = {Exploring Spatio-spectral Electroencephalogram Modulations of Imbuing Emotional Intent during Active Piano Playing.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3327740}, pmid = {37883285}, issn = {1558-0210}, abstract = {Imbuing emotional intent serves as a crucial modulator of music improvisation during active musical instrument playing. However, most improvisation-related neural endeavors have been gained without considering the emotional context. This study attempted to exploit reproducible spatio-spectral electroencephalogram (EEG) oscillations of emotional intent using a data-driven independent component analysis framework in an ecological multiday piano playing experiment. Through the four-day 32-ch EEG dataset of 10 professional players, we showed that EEG patterns were substantially affected by both intra- and inter-individual variability underlying the emotional intent of the dichotomized valence (positive vs. negative) and arousal (high vs. low) categories. Less than half (3-4) of the 10 participants analogously exhibited day-reproducible (≥ three days) spectral modulations at the right frontal beta in response to the valence contrast as well as the frontal central gamma and the superior parietal alpha to the arousal counterpart. In particular, frontal engagement facilitated a better understanding of the frontal cortex (e.g., dorsolateral prefrontal cortex and anterior cingulate cortex) and its role in intervening emotional processes and expressing spectral signatures that are relatively resistant to natural EEG variability. Such ecologically vivid EEG findings may lead to better understanding of the development of a brain-computer music interface infrastructure capable of guiding the training, performance, and appreciation for emotional improvisatory status or actuating music interaction via emotional context.}, }
@article {pmid37882881, year = {2023}, author = {Milford, SR and Shaw, D and Starke, G}, title = {Playing Brains: The Ethical Challenges Posed by Silicon Sentience and Hybrid Intelligence in DishBrain.}, journal = {Science and engineering ethics}, volume = {29}, number = {6}, pages = {38}, pmid = {37882881}, issn = {1471-5546}, mesh = {Humans ; *Artificial Intelligence ; *Silicon ; Brain ; Intelligence ; Learning ; }, abstract = {The convergence of human and artificial intelligence is currently receiving considerable scholarly attention. Much debate about the resulting Hybrid Minds focuses on the integration of artificial intelligence into the human brain through intelligent brain-computer interfaces as they enter clinical use. In this contribution we discuss a complementary development: the integration of a functional in vitro network of human neurons into an in silico computing environment.To do so, we draw on a recent experiment reporting the creation of silico-biological intelligence as a case study (Kagan et al., 2022b). In this experiment, multielectrode arrays were plated with stem cell-derived human neurons, creating a system which the authors call DishBrain. By embedding the system into a virtual game-world, neural clusters were able to receive electrical input signals from the game-world and to respond appropriately with output signals from pre-assigned motor regions. Using this design, the authors demonstrate how the DishBrain self-organises and successfully learns to play the computer game 'Pong', exhibiting 'sentient' and intelligent behaviour in its virtual environment.The creation of such hybrid, silico-biological intelligence raises numerous ethical challenges. Following the neuroscientific framework embraced by the authors themselves, we discuss the arising ethical challenges in the context of Karl Friston's Free Energy Principle, focusing on the risk of creating synthetic phenomenology. Following the DishBrain's creator's neuroscientific assumptions, we highlight how DishBrain's design may risk bringing about artificial suffering and argue for a congruently cautious approach to such synthetic biological intelligence.}, }
@article {pmid37881517, year = {2023}, author = {Mang, J and Xu, Z and Qi, Y and Zhang, T}, title = {Favoring the cognitive-motor process in the closed-loop of BCI mediated post stroke motor function recovery: challenges and approaches.}, journal = {Frontiers in neurorobotics}, volume = {17}, number = {}, pages = {1271967}, pmid = {37881517}, issn = {1662-5218}, abstract = {The brain-computer interface (BCI)-mediated rehabilitation is emerging as a solution to restore motor skills in paretic patients after stroke. In the human brain, cortical motor neurons not only fire when actions are carried out but are also activated in a wired manner through many cognitive processes related to movement such as imagining, perceiving, and observing the actions. Moreover, the recruitment of motor cortexes can usually be regulated by environmental conditions, forming a closed-loop through neurofeedback. However, this cognitive-motor control loop is often interrupted by the impairment of stroke. The requirement to bridge the stroke-induced gap in the motor control loop is promoting the evolution of the BCI-based motor rehabilitation system and, notably posing many challenges regarding the disease-specific process of post stroke motor function recovery. This review aimed to map the current literature surrounding the new progress in BCI-mediated post stroke motor function recovery involved with cognitive aspect, particularly in how it refired and rewired the neural circuit of motor control through motor learning along with the BCI-centric closed-loop.}, }
@article {pmid37881515, year = {2023}, author = {Staffa, M and D'Errico, L and Sansalone, S and Alimardani, M}, title = {Classifying human emotions in HRI: applying global optimization model to EEG brain signals.}, journal = {Frontiers in neurorobotics}, volume = {17}, number = {}, pages = {1191127}, pmid = {37881515}, issn = {1662-5218}, abstract = {Significant efforts have been made in the past decade to humanize both the form and function of social robots to increase their acceptance among humans. To this end, social robots have recently been combined with brain-computer interface (BCI) systems in an attempt to give them an understanding of human mental states, particularly emotions. However, emotion recognition using BCIs poses several challenges, such as subjectivity of emotions, contextual dependency, and a lack of reliable neuro-metrics for real-time processing of emotions. Furthermore, the use of BCI systems introduces its own set of limitations, such as the bias-variance trade-off, dimensionality, and noise in the input data space. In this study, we sought to address some of these challenges by detecting human emotional states from EEG brain activity during human-robot interaction (HRI). EEG signals were collected from 10 participants who interacted with a Pepper robot that demonstrated either a positive or negative personality. Using emotion valence and arousal measures derived from frontal brain asymmetry (FBA), several machine learning models were trained to classify human's mental states in response to the robot personality. To improve classification accuracy, all proposed classifiers were subjected to a Global Optimization Model (GOM) based on feature selection and hyperparameter optimization techniques. The results showed that it is possible to classify a user's emotional responses to the robot's behavior from the EEG signals with an accuracy of up to 92%. The outcome of the current study contributes to the first level of the Theory of Mind (ToM) in Human-Robot Interaction, enabling robots to comprehend users' emotional responses and attribute mental states to them. Our work advances the field of social and assistive robotics by paving the way for the development of more empathetic and responsive HRI in the future.}, }
@article {pmid37879343, year = {2023}, author = {Fleury, M and Figueiredo, P and Vourvopoulos, A and Lecuyer, A}, title = {Two is better? Combining EEG and fMRI for BCI and neurofeedback: a systematic review.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad06e1}, pmid = {37879343}, issn = {1741-2552}, abstract = {Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are two commonly used non-invasive techniques for measuring brain activity in neuroscience and brain- computer interfaces (BCI). While EEG has high temporal resolution and low spatial resolution, fMRI has high spatial resolution and low temporal resolution. In this review, we focus on the use of EEG and fMRI in neurofeedback (NF) and discuss the challenges of combining the two modalities in order to improve understanding of brain activity and achieve more effective clinical outcomes. Advanced technologies have been developed to simultaneously record EEG and fMRI signals in order to provide a better understanding of the relationship between the two modalities. However, the complexity of brain processes and the heterogeneous nature of EEG and fMRI present challenges in extracting useful information from the combined data. We will survey existing EEG-fMRI combinations and recent studies that exploit EEG-fMRI in NF, highlighting the experimental and technical challenges. We will also identify some of the remaining challenges in this field.}, }
@article {pmid37878151, year = {2023}, author = {Woolpert, KM and Ahern, TP and Lash, TL and O'Malley, DL and Stokes, AM and Cronin-Fenton, DP}, title = {Biomarkers predictive of a response to extended endocrine therapy in breast cancer: a systematic review and meta-analysis.}, journal = {Breast cancer research and treatment}, volume = {}, number = {}, pages = {}, pmid = {37878151}, issn = {1573-7217}, support = {R01 CA166825/CA/NCI NIH HHS/United States ; }, abstract = {PURPOSE: Extension of adjuvant endocrine therapy beyond five years confers only modest survival benefit in breast cancer patients and carries risk of toxicities. This systematic review investigates the role of biomarker tests in predicting the clinical response to an extension of endocrine therapy.<br><br>METHODS: We searched Ovid MEDLINE, Ovid Embase, Global Index Medicus, and the Cochrane Central Register of Controlled Trials using an iterative approach to identify full-text articles related to breast cancer, endocrine therapy, and biomarkers.<br><br>RESULTS: Of the 1,217 unique reports identified, five studies were deemed eligible. Four investigated the Breast Cancer Index (BCI) assay in three distinct study populations. These studies consistently showed that BCI score was predictive of response to extended endocrine therapy among 1,946 combined patients, who were predominately non-Hispanic white and postmenopausal.<br><br>CONCLUSIONS: Evidence in the setting of predictive tests for extended endocrine therapy is sparse. Most relevant studies investigated the use of BCI, but these study populations were largely restricted to a single age, race, and ethnicity group. Future studies should evaluate a variety of biomarkers in diverse populations. Without sufficient evidence, physicians and patients face a difficult decision in balancing the benefits and risks of endocrine therapy extension.}, }
@article {pmid37876899, year = {2023}, author = {Ma, G and Yan, R and Tang, H}, title = {Exploiting noise as a resource for computation and learning in spiking neural networks.}, journal = {Patterns (New York, N.Y.)}, volume = {4}, number = {10}, pages = {100831}, pmid = {37876899}, issn = {2666-3899}, abstract = {Networks of spiking neurons underpin the extraordinary information-processing capabilities of the brain and have become pillar models in neuromorphic artificial intelligence. Despite extensive research on spiking neural networks (SNNs), most studies are established on deterministic models, overlooking the inherent non-deterministic, noisy nature of neural computations. This study introduces the noisy SNN (NSNN) and the noise-driven learning (NDL) rule by incorporating noisy neuronal dynamics to exploit the computational advantages of noisy neural processing. The NSNN provides a theoretical framework that yields scalable, flexible, and reliable computation and learning. We demonstrate that this framework leads to spiking neural models with competitive performance, improved robustness against challenging perturbations compared with deterministic SNNs, and better reproducing probabilistic computation in neural coding. Generally, this study offers a powerful and easy-to-use tool for machine learning, neuromorphic intelligence practitioners, and computational neuroscience researchers.}, }
@article {pmid37876845, year = {2023}, author = {Wang, P and Liu, J and Wang, L and Ma, H and Mei, X and Zhang, A}, title = {Effects of brain-Computer interface combined with mindfulness therapy on rehabilitation of hemiplegic patients with stroke: a randomized controlled trial.}, journal = {Frontiers in psychology}, volume = {14}, number = {}, pages = {1241081}, pmid = {37876845}, issn = {1664-1078}, abstract = {AIM: To explore the effects of brain-computer interface training combined with mindfulness therapy on Hemiplegic Patients with Stroke.<br><br>BACKGROUND: The prevention and treatment of stroke still faces great challenges. Maximizing the improvement of patients' ability to perform activities of daily living, limb motor function, and reducing anxiety, depression, and other social and psychological problems to improve patients' overall quality of life is the focus and difficulty of clinical rehabilitation work.<br><br>METHODS: Patients were recruited from December 2021 to November 2022, and assigned to either the intervention or control group following a simple randomization procedure (computer-generated random numbers). Both groups received conventional rehabilitation treatment, while patients in the intervention group additionally received brain-computer interface training and mindfulness therapy. The continuous treatment duration was 5 days per week for 8 weeks. Limb motor function, activities of daily living, mindfulness attention awareness level, sleep quality, and quality of life of the patients were measured (in T0, T1, and T2). Generalized estimated equation (GEE) were used to evaluate the effects. The trial was registered with the Chinese Clinical Trial Registry (ChiCTR2300070382).<br><br>RESULTS: A total of 128 participants were randomized and 64 each were assigned to the intervention and control groups (of these, eight patients were lost to follow-up). At 6 months, compared with the control group, intervention group showed statistically significant improvements in limb motor function, mindful attention awareness, activities of daily living, sleep quality, and quality of life.<br><br>CONCLUSION: Brain-computer interface combined with mindfulness therapy training can improve limb motor function, activities of daily living, mindful attention awareness, sleep quality, and quality of life in hemiplegic patients with stroke.<br><br>IMPACT: This study provides valuable insights into post-stroke care. It may help improve the effect of rehabilitation nursing to improve the comprehensive ability and quality of life of patients after stroke.<br><br>CLINICAL REVIEW REGISTRATION: https://www.chictr.org.cn/, identifier ChiCTR2300070382.}, }
@article {pmid37875937, year = {2023}, author = {Obukhov, NV and Naish, PLN and Solnyshkina, IE and Siourdaki, TG and Martynov, IA}, title = {Real-time assessment of hypnotic depth, using an EEG-based brain-computer interface: a preliminary study.}, journal = {BMC research notes}, volume = {16}, number = {1}, pages = {288}, pmid = {37875937}, issn = {1756-0500}, mesh = {Humans ; Hypnotics and Sedatives ; *Brain-Computer Interfaces ; Electroencephalography/methods ; *Hypnosis ; }, abstract = {OBJECTIVE: Hypnosis can be an effective treatment for many conditions, and there have been attempts to develop instrumental approaches to continuously monitor hypnotic state level ("depth"). However, there is no method that addresses the individual variability of electrophysiological hypnotic correlates. We explore the possibility of using an EEG-based passive brain-computer interface (pBCI) for real-time, individualised estimation of the hypnosis deepening process.<br><br>RESULTS: The wakefulness and deep hypnosis intervals were manually defined and labelled in 27 electroencephalographic (EEG) recordings obtained from eight outpatients after hypnosis sessions. Spectral analysis showed that EEG correlates of deep hypnosis were relatively stable in each patient throughout the treatment but varied between patients. Data from each first session was used to train classification models to continuously assess deep hypnosis probability in subsequent sessions. Models trained using four frequency bands (1.5-45, 1.5-8, 1.5-14, and 4-15&#xa0;Hz) showed accuracy mostly exceeding 85% in a 10-fold cross-validation. Real-time classification accuracy was also acceptable, so at least one of the four bands yielded results exceeding 74% in any session. The best results averaged across all sessions were obtained using 1.5-14 and 4-15&#xa0;Hz, with an accuracy of 82%. The revealed issues are also discussed.}, }
@article {pmid37875484, year = {2023}, author = {Wang, M and Lin, T and Wang, L and Lin, A and Zou, K and Xu, X and Zhou, Y and Peng, Y and Meng, Q and Qian, Y and Deng, G and Wu, Z and Chen, J and Lin, J and Zhang, M and Zhu, W and Zhang, C and Zhang, D and Goh, RSM and Liu, Y and Pang, CP and Chen, X and Chen, H and Fu, H}, title = {Uncertainty-inspired open set learning for retinal anomaly identification.}, journal = {Nature communications}, volume = {14}, number = {1}, pages = {6757}, pmid = {37875484}, issn = {2041-1723}, support = {C222812010//Agency for Science, Technology and Research (A*STAR)/ ; }, abstract = {Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We establish an uncertainty-inspired open set (UIOS) model, which is trained with fundus images of 9 retinal conditions. Besides assessing the probability of each category, UIOS also calculates an uncertainty score to express its confidence. Our UIOS model with thresholding strategy achieves an F1 score of 99.55%, 97.01% and 91.91% for the internal testing set, external target categories (TC)-JSIEC dataset and TC-unseen testing set, respectively, compared to the F1 score of 92.20%, 80.69% and 64.74% by the standard AI model. Furthermore, UIOS correctly predicts high uncertainty scores, which would prompt the need for a manual check in the datasets of non-target categories retinal diseases, low-quality fundus images, and non-fundus images. UIOS provides a robust method for real-world screening of retinal anomalies.}, }
@article {pmid37875422, year = {2023}, author = {,  and , }, title = {[Chinese expert consensus on multigene testing for adjuvant treatment of HR-positive, HER-2 negative early breast cancer(2023 edition)].}, journal = {Zhonghua zhong liu za zhi [Chinese journal of oncology]}, volume = {45}, number = {10}, pages = {863-870}, doi = {10.3760/cma.j.cn112152-20230627-00266}, pmid = {37875422}, issn = {0253-3766}, support = {2022-I2M-C&amp;T-A-014//Chinese Academy of Medical Sciences Clinical Transformation and Medical Research Fund Rolling Project/ ; 7222150//Beijing Natural Science Foundation/ ; }, mesh = {Humans ; Female ; *Breast Neoplasms/drug therapy/genetics ; Consensus ; East Asian People ; Prognosis ; Chemotherapy, Adjuvant ; Receptor, ErbB-2/genetics ; }, abstract = {Breast cancer is the most common malignant tumor in women, of which the majority is early breast cancer (EBC). The strategy of postoperative adjuvant treatment relies mainly on the clinicopathologic characteristics of patients, but there are certain deficiencies if only depending on it to assess treatment benefits and disease prognosis. Multigene testing tools can evaluate the prognosis and predict therapeutic effects of breast cancer patients to guide the clinical decision-making on whether to use adjuvant chemotherapy, radiotherapy, and endocrine therapy by detecting the expression levels of specific genes. The consensus-writing expert group, based on the characteristics, validation results, and accessibility of the multigene testing tools and combined with clinical practice, described the result interpretation and clinical application of OncotypeDx(®) (21-gene), Mammaprint(®) (70-gene), RecurIndex(®) (28-gene), EndoPredict(®)(12-gene), and BreastCancerIndex(®) (BCI, 7-gene) for hormone receptor-positive and human epidermal growth factor receptor 2-negative EBC. The development and validation process of each tool was also briefly introduced. It is expected that the consensus will help guide and standardize the clinical use of multigene testing tools and further improve the level of precise treatment for EBC.}, }
@article {pmid37875404, year = {2023}, author = {Luo, S and Angrick, M and Coogan, C and Candrea, DN and Wyse-Sookoo, K and Shah, S and Rabbani, Q and Milsap, GW and Weiss, AR and Anderson, WS and Tippett, DC and Maragakis, NJ and Clawson, LL and Vansteensel, MJ and Wester, BA and Tenore, FV and Hermansky, H and Fifer, MS and Ramsey, NF and Crone, NE}, title = {Stable Decoding from a Speech BCI Enables Control for an Individual with ALS without Recalibration for 3 Months.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2304853}, doi = {10.1002/advs.202304853}, pmid = {37875404}, issn = {2198-3844}, support = {U01DC016686/DC/NIDCD NIH HHS/United States ; UH3NS114439/NS/NINDS NIH HHS/United States ; }, abstract = {Brain-computer interfaces (BCIs) can be used to control assistive devices by patients with neurological disorders like amyotrophic lateral sclerosis (ALS) that limit speech and movement. For assistive control, it is desirable for BCI systems to be accurate and reliable, preferably with minimal setup time. In this study, a participant with severe dysarthria due to ALS operates computer applications with six intuitive speech commands via a chronic electrocorticographic (ECoG) implant over the ventral sensorimotor cortex. Speech commands are accurately detected and decoded (median accuracy: 90.59%) throughout a 3-month study period without model retraining or recalibration. Use of the BCI does not require exogenous timing cues, enabling the participant to issue self-paced commands at will. These results demonstrate that a chronically implanted ECoG-based speech BCI can reliably control assistive devices over long time periods with only initial model training and calibration, supporting the feasibility of unassisted home use.}, }
@article {pmid37875107, year = {2023}, author = {Meng, J and Zhao, Y and Wang, K and Sun, J and Yi, W and Xu, F and Xu, M and Ming, D}, title = {Rhythmic temporal prediction enhances neural representations of movement intention for brain-computer interface.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad0650}, pmid = {37875107}, issn = {1741-2552}, abstract = {Detecting movement intention is a typical use of brain-computer interfaces (BCI). However, as an endogenous electroencephalography (EEG) feature, the neural representation of movement is insufficient for improving motor-based BCI. This study aimed to develop a new movement augmentation BCI encoding paradigm by incorporating the cognitive function of rhythmic temporal prediction, and test the feasibility of this new paradigm in optimizing detections of movement intention. Methods: A visual-motion synchronization task was designed with two movement intentions (left vs. right) and three rhythmic temporal prediction conditions (1000ms vs.1500ms vs. no temporal prediction). Behavioural and EEG data of 24 healthy participants were recorded. Event-related potentials (ERP), event-related spectral perturbation (ERSP) induced by left- and right-finger movements, the common spatial patterns (CSP) and support vector machine, Riemann tangent space algorithm and logistic regression were used and compared across the three temporal prediction conditions, aiming to test the impact of temporal prediction on movement detection. Results: Behavioural results showed significantly smaller deviated time for 1000ms and 1500ms conditions. ERP analyses revealed 1000ms and 1500ms conditions led to rhythmic oscillations with a time lag in contralateral and ipsilateral areas of movement. Compared with no temporal prediction, 1000ms condition exhibited greater beta ERD lateralization in motor area (p<0.001) and larger beta ERD in frontal area (p<0.001). 1000ms condition achieved an averaged left-right decoding accuracy of 89.71% using CSP and 97.30% using Riemann tangent space, both significantly higher than no temporal prediction. Moreover, movement and temporal information can be decoded simultaneously, achieving 88.51% four-classification accuracy. Significance: The results not only confirm the effectiveness of rhythmic temporal prediction in enhancing detection ability of motor-based BCI, but also highlight the dual encodings of movement and temporal information within a single BCI paradigm, which is promising to expand the range of intentions that can be decoded by the BCI. .}, }
@article {pmid37871461, year = {2023}, author = {Cao, HL and Wei, W and Meng, YJ and Deng, W and Li, T and Li, ML and Guo, WJ}, title = {Disrupted white matter structural networks in individuals with alcohol dependence.}, journal = {Journal of psychiatric research}, volume = {168}, number = {}, pages = {13-21}, doi = {10.1016/j.jpsychires.2023.10.019}, pmid = {37871461}, issn = {1879-1379}, abstract = {Previous diffusion tensor imaging (DTI) studies have demonstrated widespread white matter microstructure damage in individuals with alcoholism. However, very little is known about the alterations in the topological architecture of white matter structural networks in alcohol dependence (AD). This study included 67 AD patients and 69 controls. The graph theoretical analysis method was applied to examine the topological organization of the white matter structural networks, and network-based statistics (NBS) were employed to detect structural connectivity alterations. Compared to controls, AD patients exhibited abnormal global network properties characterized by increased small-worldness, normalized clustering coefficient, clustering coefficient, and shortest path length; and decreased global efficiency and local efficiency. Further analyses revealed decreased nodal efficiency and degree centrality in AD patients mainly located in the default mode network (DMN), including the precuneus, anterior cingulate and paracingulate gyrus, median cingulate and paracingulate gyrus, posterior cingulate gyrus, and medial part of the superior frontal gyrus. Furthermore, based on NBS approaches, patients displayed weaker subnetwork connectivity mainly located in the region of the DMN. Additionally, altered network metrics were correlated with intelligence quotient (IQ) scores and global assessment function (GAF) scores. Our results may reveal the disruption of whole-brain white matter structural networks in AD individuals, which may contribute to our comprehension of the underlying pathophysiological mechanisms of alcohol addiction at the level of white matter structural networks.}, }
@article {pmid37871436, year = {2023}, author = {Akhlaghi, P and Ghouchani, A and Rouhi, G}, title = {The effect of defect size and location on the fracture risk of proximal tibia, following tumor curettage and cementation: An in-silico investigation.}, journal = {Computers in biology and medicine}, volume = {167}, number = {}, pages = {107564}, doi = {10.1016/j.compbiomed.2023.107564}, pmid = {37871436}, issn = {1879-0534}, abstract = {Even though, proximal tibia is a common site of giant cell tumor and bone fractures, following tumor removal, nonetheless very little attention has been paid to affecting factors on the fracture risk. Here, nonlinear voxel-based finite element models based on computed tomography images were developed to predict bone fracture load with defects with different sizes, which were located in the medial, lateral, anterior, and posterior region of the proximal tibia. Critical defect size was identified using One-sample t-test to assess if the mean difference between the bone strength for a defect size was significantly different from the intact bone strength. Then, the defects larger than critical size were reconstructed with cement and the mechanics of the bone-cement interface (BCI) was investigated to find the regions prone to separation at BCI. A significant increase in fracture risk was observed for the defects larger than 20 mm, which were located in the medial, lateral and anterior regions, and defects larger than 25 mm for those located in the posterior region of the proximal tibia. Furthermore, it was found that the highest and lowest fracture risks were associated with defects located in the medial and posterior regions, respectively, highlighting the importance of selecting the initial location of a cortical window for tumor removal by the surgeon. The results of the BCI analysis showed that the location and size of the cement had a direct impact on the extent of damage and its distribution. Identification of critical regions susceptible to separation at BCI, can provide critical comments to surgeons in selecting the optimal cement augmentation technique, which may ultimately prevent unnecessary surgical intervention, such as using screws and pins.}, }
@article {pmid37870175, year = {2023}, author = {Tian, Y and Yin, J and Wang, C and He, Z and Xie, J and Feng, X and Zhou, Y and Ma, T and Xie, Y and Li, X and Yang, T and Ren, C and Li, C and Zhao, Z}, title = {An Ultraflexible Electrode Array for Large-Scale Chronic Recording in the Nonhuman Primate Brain.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2302333}, doi = {10.1002/advs.202302333}, pmid = {37870175}, issn = {2198-3844}, support = {2022ZD0210300//National Science and Technology Innovation 2030 Major Program/ ; 2018SHZDZX05//Shanghai Municipal Science and Technology Major Project/ ; 2021SHZDZX//Shanghai Municipal Science and Technology Major Project/ ; LG202105-01-06//Lingang Laboratory/ ; }, abstract = {Single-unit (SU) recording in nonhuman primates (NHPs) is indispensible in the quest of how the brain works, yet electrodes currently used for the NHP brain are limited in signal longevity, stability, and spatial coverage. Using new structural materials, microfabrication, and penetration techniques, we develop a mechanically robust ultraflexible, 1 µm thin electrode array (MERF) that enables pial penetration and high-density, large-scale, and chronic recording of neurons along both vertical and horizontal cortical axes in the nonhuman primate brain. Recording from three monkeys yields 2,913 SUs from 1,065 functional recording channels (up to 240 days), with some SUs tracked for up to 2 months. Recording from the primary visual cortex (V1) reveals that neurons with similar orientation preferences for visual stimuli exhibited higher spike correlation. Furthermore, simultaneously recorded neurons in different cortical layers of the primary motor cortex (M1) show preferential firing for hand movements of different directions. Finally, it is shown that a linear decoder trained with neuronal spiking activity across M1 layers during monkey's hand movements can be used to achieve on-line control of cursor movement. Thus, the MERF electrode array offers a new tool for basic neuroscience studies and brain-machine interface (BMI) applications in the primate brain.}, }
@article {pmid37869141, year = {2023}, author = {Cao, P and Shi, D and Li, D and Zhu, Z and Zhu, J and Zhang, J and Bai, R}, title = {Modeling and in vivo experimental validation of 1,064 nm laser interstitial thermal therapy on brain tissue.}, journal = {Frontiers in neurology}, volume = {14}, number = {}, pages = {1237394}, pmid = {37869141}, issn = {1664-2295}, abstract = {INTRODUCTION: Laser interstitial thermal therapy (LITT) at 1064 nm is widely used to treat epilepsy and brain tumors; however, no numerical model exists that can predict the ablation region with careful in vivo validation.<br><br>METHODS: In this study, we proposed a model with a system of finite element methods simulating heat transfer inside the brain tissue, radiative transfer from the applicator into the brain tissue, and a model for tissue damage.<br><br>RESULTS: To speed up the computation for practical applications, we also validated P1-approximation as an efficient and fast method for calculating radiative transfer by comparing it with Monte Carlo simulation. Finally, we validated the proposed numerical model in vivo on six healthy canines and eight human patients with epilepsy and found strong agreement between the predicted temperature profile and ablation area and the magnetic resonance imaging-measured results.<br><br>DISCUSSION: Our results demonstrate the feasibility and reliability of the model in predicting the ablation area of 1,064&#x2009;nm LITT, which is important for presurgical planning when using LITT.}, }
@article {pmid37868195, year = {2023}, author = {Liang, R and Wang, L and Yang, Q and Xu, Q and Sun, S and Zhou, H and Zhao, M and Gao, J and Zheng, C and Yang, J and Ming, D}, title = {Time-course adaptive changes in hippocampal transcriptome and synaptic function induced by simulated microgravity associated with cognition.}, journal = {Frontiers in cellular neuroscience}, volume = {17}, number = {}, pages = {1275771}, pmid = {37868195}, issn = {1662-5102}, abstract = {INTRODUCTION: The investigation of cognitive function in microgravity, both short-term and long-term, remains largely descriptive. And the underlying mechanisms of the changes over time remain unclear.<br><br>METHODS: Behavioral tests, electrophysiological recording, and RNA sequencing were used to observe differences in behavior, synaptic plasticity, and gene expression.<br><br>RESULTS: Initially, we measured the performance of spatial cognition exposed to long-term simulated microgravity (SM). Both working memory and advanced cognitive abilities were enhanced. Somewhat surprisingly, the synaptic plasticity of the hippocampal CA3-CA1 synapse was impaired. To gain insight into the mechanism of changing regularity over time, transcriptome sequencing in the hippocampus was performed. The analysis identified 20 differentially expressed genes (DEGs) in the hippocampus after short-term modeling, 19 of which were up-regulated. Gene Ontology (GO) analysis showed that these up-regulated genes were mainly enriched in synaptic-related processes, such as Stxbp5l and Epha6. This might be related to the enhancement of working memory performance under short-term SM exposure. Under exposure to long-term SM, 7 DEGs were identified in the hippocampus, all of which were up-regulated and related to oxidative stress and metabolism, such as Depp1 and Lrg1. Compensatory effects occurred with increased modeling time.<br><br>DISCUSSION: To sum up, our current research indicates that the cognitive function under SM exposure is consistently maintained or potentially even being enhanced over both short and long durations. The underlying mechanisms are intricate and potentially linked to the differential expression of hippocampal-associated genes and alterations in synaptic function, with these effects being time-dependent. The present study will lay the experimental and theoretical foundation of the multi-level mechanism of cognitive function under space flight.}, }
@article {pmid37867413, year = {2023}, author = {Xu, J and Zu, T and Hsu, YC and Wang, X and Chan, KWY and Zhang, Y}, title = {Accelerating CEST imaging using a model-based deep neural network with synthetic training data.}, journal = {Magnetic resonance in medicine}, volume = {}, number = {}, pages = {}, doi = {10.1002/mrm.29889}, pmid = {37867413}, issn = {1522-2594}, support = {//MOE Frontier Science Center for Brain Science &amp; Brain-Machine Integration, Zhejiang University/ ; 81971605//National Natural Science Foundation of China/ ; 2022C04031//Key R&amp;D Program of Zhejiang Province/ ; 2020R01003//Leading Innovation and Entrepreneurship Team of Zhejiang Province/ ; }, abstract = {PURPOSE: To develop a model-based deep neural network for high-quality image reconstruction of undersampled multi-coil CEST data.<br><br>THEORY AND METHODS: Inspired by the variational network (VN), the CEST image reconstruction equation is unrolled into a deep neural network (CEST-VN) with a k-space data-sharing block that takes advantage of the inherent redundancy in adjacent CEST frames and 3D spatial-frequential convolution kernels that exploit correlations in the x-&#x3c9; domain. Additionally, a new pipeline based on multiple-pool Bloch-McConnell simulations is devised to synthesize multi-coil CEST data from publicly available anatomical MRI data. The proposed network is trained on simulated data with a CEST-specific loss function that jointly measures the structural and CEST contrast. The performance of CEST-VN was evaluated on four healthy volunteers and five brain tumor patients using retrospectively or prospectively undersampled data with various acceleration factors, and then compared with other conventional and state-of-the-art reconstruction methods.<br><br>RESULTS: The proposed CEST-VN method generated high-quality CEST source images and amide proton transfer-weighted maps in healthy and brain tumor subjects, consistently outperforming GRAPPA, blind compressed sensing, and the original VN. With the acceleration factors increasing from 3 to 6, CEST-VN with the same hyperparameters yielded similar and accurate reconstruction without apparent loss of details or increase of artifacts. The ablation studies confirmed the effectiveness of the CEST-specific loss function and data-sharing block used.<br><br>CONCLUSIONS: The proposed CEST-VN method can offer high-quality CEST source images and amide proton transfer-weighted maps from highly undersampled multi-coil data by integrating the deep learning prior and multi-coil sensitivity encoding model.}, }
@article {pmid37865081, year = {2023}, author = {Wu, F and Wu, J and Chen, X and Zhou, J and Du, Z and Tong, D and Zhang, H and Huang, Y and Yang, Y and Du, A and Ma, G}, title = {A secreted BPTI/Kunitz inhibitor domain-containing protein of barber's pole worm interacts with host NLRP3 inflammasome activation-associated G protein subunit to inhibit IL-1β and IL-18 maturation in vitro.}, journal = {Veterinary parasitology}, volume = {323}, number = {}, pages = {110052}, doi = {10.1016/j.vetpar.2023.110052}, pmid = {37865081}, issn = {1873-2550}, abstract = {Protease inhibitors are major components of excretory/secretory products released by parasitic nematodes and have been proposed to play roles in host-parasite interactions. Haemonchus contortus (the barber's pole worm) encodes for several serine protease inhibitors, and in a previous study we identified a trypsin inhibitor-like serine protease inhibitor of this blood-feeding nematode, SPI-I8, as necessary for anticoagulation. Here, we demonstrated that a bovine pancreatic trypsin inhibitor/Kunitz-type serine protease inhibitor (BPTI/Kunitz) domain-containing protein highly expressed in parasitic stages, HCON_00133150, is involved in suppressing proinflammatory cytokine production in mammalian cells. Fluorescent labelling of HCON_00133150 revealed a punctate localisation at the inner hypodermal membrane of H. contortus, an organ closely related to the excretory column. Yeast two-hybrid screening and immunoprecipitation-mass spectrometry identified that the recombinant HCON_00133150 physically interacted with a range of host proteins including the G protein subunit beta 1 of sheep (Ovis aries; OaGNB1), a negative regulator of NLRP3 inflammasome activation. Interestingly, heterologous expression of HCON_00133150 enhanced the inhibitory effect of OaGNB1 on NLRP3 inflammasome and the maturation of proinflammatory cytokines IL-1β and IL-18 in transfected cells. 1-to-1 orthologues (n = 33) of BPTI/Kunitz inhibitor domain-containing proteins were predicted in clades III, IV and V (but not clade I) parasitic nematodes. Structural (tandem BPTI/Kunitz inhibitor domains inverted into the globular reticulation) and functional (a GNB1 enhancer) characterisation of HCON_00133150 and its orthologues elucidated that these molecules might contribute to immune suppression by parasitic nematodes in animals and humans.}, }
@article {pmid37864083, year = {2023}, author = {Zhang, CK and Wang, P and Ji, YY and Zhao, JS and Gu, JX and Yan, XX and Fan, HW and Zhang, MM and Qiao, Y and Liu, XD and Li, BJ and Wang, MH and Dong, HL and Li, HH and Huang, PC and Li, YQ and Hou, WG and Li, JL and Chen, T}, title = {Potentiation of the lateral habenula-ventral tegmental area pathway underlines the susceptibility to depression in mice with chronic pain.}, journal = {Science China. Life sciences}, volume = {}, number = {}, pages = {}, pmid = {37864083}, issn = {1869-1889}, abstract = {Chronic pain often develops severe mood changes such as depression. However, how chronic pain leads to depression remains elusive and the mechanisms determining individuals' responses to depression are largely unexplored. Here we found that depression-like behaviors could only be observed in 67.9% of mice with chronic neuropathic pain, leaving 32.1% of mice with depression resilience. We determined that the spike discharges of the ventral tegmental area (VTA)-projecting lateral habenula (LHb) glutamatergic (Glu) neurons were sequentially increased in sham, resilient and susceptible mice, which consequently inhibited VTA dopaminergic (DA) neurons through a LHb[Glu]-VTA[GABA]-VTA[DA] circuit. Furthermore, the LHb[Glu]-VTA[DA] excitatory inputs were dampened via GABAB receptors in a pre-synaptic manner. Regulation of LHb-VTA pathway largely affected the development of depressive symptoms caused by chronic pain. Our study thus identifies a pivotal role of the LHb-VTA pathway in coupling chronic pain with depression and highlights the activity-dependent contribution of LHb[Glu]-to-VTA[DA] inhibition in depressive behavioral regulation.}, }
@article {pmid37863819, year = {2023}, author = {Lv, S and He, E and Luo, J and Liu, Y and Liang, W and Xu, S and Zhang, K and Yang, Y and Wang, M and Song, Y and Wu, Y and Cai, X}, title = {Using Human-Induced Pluripotent Stem Cell Derived Neurons on Microelectrode Arrays to Model Neurological Disease: A Review.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2301828}, doi = {10.1002/advs.202301828}, pmid = {37863819}, issn = {2198-3844}, support = {L2224042//National Natural Science Foundation of China/ ; 61960206012//National Natural Science Foundation of China/ ; 62121003//National Natural Science Foundation of China/ ; T2293731//National Natural Science Foundation of China/ ; 62171434//National Natural Science Foundation of China/ ; 61975206//National Natural Science Foundation of China/ ; 61971400//National Natural Science Foundation of China/ ; 61973292//National Natural Science Foundation of China/ ; XK2022XXC003//Frontier Interdiscipline Project of the Chinese Academy of Sciences/ ; 2022YFC2402501//National Key Research and Development Program of China/ ; 2022YFB3205602//National Key Research and Development Program of China/ ; 2021ZD02016030//Major Program of Scientific and Technical Innovation/ ; GJJSTD20210004//Scientific Instrument Developing Project of the Chinese Academy of Sciences/ ; }, abstract = {In situ physiological signals of in vitro neural disease models are essential for studying pathogenesis and drug screening. Currently, an increasing number of in vitro neural disease models are established using human-induced pluripotent stem cell (hiPSC) derived neurons (hiPSC-DNs) to overcome interspecific gene expression differences. Microelectrode arrays (MEAs) can be readily interfaced with two-dimensional (2D), and more recently, three-dimensional (3D) neural stem cell-derived in vitro models of the human brain to monitor their physiological activity in real time. Therefore, MEAs are emerging and useful tools to model neurological disorders and disease in vitro using human iPSCs. This is enabling a real-time window into neuronal signaling at the network scale from patient derived. This paper provides a comprehensive review of MEA's role in analyzing neural disease models established by hiPSC-DNs. It covers the significance of MEA fabrication, surface structure and modification schemes for hiPSC-DNs culturing and signal detection. Additionally, this review discusses advances in the development and use of MEA technology to study in vitro neural disease models, including epilepsy, autism spectrum developmental disorder (ASD), and others established using hiPSC-DNs. The paper also highlights the application of MEAs combined with hiPSC-DNs in detecting in vitro neurotoxic substances. Finally, the future development and outlook of multifunctional and integrated devices for in vitro medical diagnostics and treatment are discussed.}, }
@article {pmid37861815, year = {2023}, author = {Yang, J and Song, H and Zhan, H and Ding, M and Luan, T and Chen, J and Wei, H and Wang, J}, title = {The influence of preoperative urodynamic parameters on clinical results in patients with benign prostatic hyperplasia after transurethral resection of the prostate.}, journal = {World journal of urology}, volume = {}, number = {}, pages = {}, pmid = {37861815}, issn = {1433-8726}, abstract = {PURPOSE: To identify the urodynamic parameters affecting the clinical outcomes of transurethral resection of the prostate(TURP) surgery for patients with benign prostatic hyperplasia(BPH) by multifactor analysis and establish a regression model with diagnostic values.<br><br>METHODS: The medical records of patients who underwent TURP surgery for BPH between December 2018 and September 2021 were collected from the urology department of the Second Affiliated Hospital of Kunming Medical University, Kunming, China. The patients' clinical data and urodynamic parameters were collected before surgery. The urodynamic parameters affecting surgical efficacy were identified by multifactor analysis, and a regression model with diagnostic values was established and evaluated.<br><br>RESULTS: A total of 201 patients underwent TURP, of whom 144 had complete preoperative urodynamic data. Each urodynamic factor was subjected to multifactor analysis, and the bladder contractility index (BCI), bladder outflow obstruction index (BOOI), bladder residual urine, and bladder compliance (BC) were found to be independent influence factors on the efficacy of TURP in patients with BPH. The diagnostic value of the regression model was analyzed by receiver operating characteristics (ROC) analysis, and it was found that the AUC&#x2009;=&#x2009;0.939 (95% CI 0.886-0.972), for which the sensitivity and specificity were 95.19% and 80%, respectively.<br><br>CONCLUSIONS: The regression model had high diagnostic sensitivity and specificity in predicting the efficacy of surgery, and the diagnostic value was higher than that of individual urodynamic factors. Therefore, BCI, BOOI, bladder residual urine, and BC should be considered as independent influence factors on the efficacy of TURP surgery for BPH.}, }
@article {pmid37859766, year = {2023}, author = {Chen, R and Xu, G and Zhang, H and Zhang, X and Li, B and Wang, J and Zhang, S}, title = {A novel untrained SSVEP-EEG feature enhancement method using canonical correlation analysis and underdamped second-order stochastic resonance.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1246940}, pmid = {37859766}, issn = {1662-4548}, abstract = {OBJECTIVE: Compared with the light-flashing paradigm, the ring-shaped motion checkerboard patterns avoid uncomfortable flicker or brightness modulation, improving the practical interactivity of brain-computer interface (BCI) applications. However, due to fewer harmonic responses and more concentrated frequency energy elicited by the ring-shaped checkerboard patterns, the mainstream untrained algorithms such as canonical correlation analysis (CCA) and filter bank canonical correlation analysis (FBCCA) methods have poor recognition performance and low information transmission rate (ITR).<br><br>METHODS: To address this issue, a novel untrained SSVEP-EEG feature enhancement method using CCA and underdamped second-order stochastic resonance (USSR) is proposed to extract electroencephalogram (EEG) features.<br><br>RESULTS: In contrast to typical unsupervised dimensionality reduction methods such as common average reference (CAR), principal component analysis (PCA), multidimensional scaling (MDS), and locally linear embedding (LLE), CCA exhibits higher adaptability for SSVEP rhythm components.<br><br>CONCLUSION: This study recruits 42 subjects to evaluate the proposed method and experimental results show that the untrained method can achieve higher detection accuracy and robustness.<br><br>SIGNIFICANCE: This untrained method provides the possibility of applying a nonlinear model from one-dimensional signals to multi-dimensional signals.}, }
@article {pmid37857827, year = {2023}, author = {Zippi, EL and Shvartsman, GF and Vendrell-Llopis, N and Wallis, JD and Carmena, JM}, title = {Distinct neural representations during a brain-machine interface and manual reaching task in motor cortex, prefrontal cortex, and striatum.}, journal = {Scientific reports}, volume = {13}, number = {1}, pages = {17810}, pmid = {37857827}, issn = {2045-2322}, support = {R01 MH117763/MH/NIMH NIH HHS/United States ; R01 NS106094/NS/NINDS NIH HHS/United States ; R01NS106094/NH/NIH HHS/United States ; R01MH117763/MH/NIMH NIH HHS/United States ; }, mesh = {Animals ; *Brain-Computer Interfaces ; *Motor Cortex/physiology ; Cadmium ; Prefrontal Cortex/physiology ; Learning ; }, abstract = {Although brain-machine interfaces (BMIs) are directly controlled by the modulation of a select local population of neurons, distributed networks consisting of cortical and subcortical areas have been implicated in learning and maintaining control. Previous work in rodents has demonstrated the involvement of the striatum in BMI learning. However, the prefrontal cortex has been largely ignored when studying motor BMI control despite its role in action planning, action selection, and learning abstract tasks. Here, we compare local field potentials simultaneously recorded from primary motor cortex (M1), dorsolateral prefrontal cortex (DLPFC), and the caudate nucleus of the striatum (Cd) while nonhuman primates perform a two-dimensional, self-initiated, center-out task under BMI control and manual control. Our results demonstrate the presence of distinct neural representations for BMI and manual control in M1, DLPFC, and Cd. We find that neural activity from DLPFC and M1 best distinguishes control types at the go cue and target acquisition, respectively, while M1 best predicts target-direction at both task events. We also find effective connectivity from DLPFC → M1 throughout both control types and Cd → M1 during BMI control. These results suggest distributed network activity between M1, DLPFC, and Cd during BMI control that is similar yet distinct from manual control.}, }
@article {pmid37857637, year = {2023}, author = {Ma, D and Zheng, Y and Li, X and Zhou, X and Yang, Z and Zhang, Y and Wang, L and Zhang, W and Fang, J and Zhao, G and Hou, P and Nan, F and Yang, W and Su, N and Gao, Z and Guo, J}, title = {Ligand activation mechanisms of human KCNQ2 channel.}, journal = {Nature communications}, volume = {14}, number = {1}, pages = {6632}, pmid = {37857637}, issn = {2041-1723}, support = {2020YFA0908501//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; }, mesh = {Humans ; *Anticonvulsants/pharmacology ; Cryoelectron Microscopy ; Ligands ; Membrane Potentials ; *Analgesics ; KCNQ2 Potassium Channel/chemistry/metabolism ; KCNQ3 Potassium Channel/metabolism ; }, abstract = {The human voltage-gated potassium channel KCNQ2/KCNQ3 carries the neuronal M-current, which helps to stabilize the membrane potential. KCNQ2 can be activated by analgesics and antiepileptic drugs but their activation mechanisms remain unclear. Here we report cryo-electron microscopy (cryo-EM) structures of human KCNQ2-CaM in complex with three activators, namely the antiepileptic drug cannabidiol (CBD), the lipid phosphatidylinositol 4,5-bisphosphate (PIP2), and HN37 (pynegabine), an antiepileptic drug in the clinical trial, in an either closed or open conformation. The activator-bound structures, along with electrophysiology analyses, reveal the binding modes of two CBD, one PIP2, and two HN37 molecules in each KCNQ2 subunit, and elucidate their activation mechanisms on the KCNQ2 channel. These structures may guide the development of antiepileptic drugs and analgesics that target KCNQ2.}, }
@article {pmid37856256, year = {2023}, author = {Serrano-Amenos, C and Heydari, P and Liu, CY and Do, AH and Nenadic, Z}, title = {Power Budget of a Skull Unit in a Fully-Implantable Brain-Computer Interface: Bio-Heat Model.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {31}, number = {}, pages = {4029-4039}, doi = {10.1109/TNSRE.2023.3323916}, pmid = {37856256}, issn = {1558-0210}, mesh = {Humans ; *Brain-Computer Interfaces ; Hot Temperature ; Skull ; Head ; Prostheses and Implants ; }, abstract = {The aim of this study is to estimate the maximum power consumption that guarantees the thermal safety of a skull unit (SU). The SU is part of a fully-implantable bi-directional brain computer-interface (BD-BCI) system that aims to restore walking and leg sensation to those with spinal cord injury (SCI). To estimate the SU power budget, we created a bio-heat model using the finite element method (FEM) implemented in COMSOL. To ensure that our predictions were robust against the natural variation of the model's parameters, we also performed a sensitivity analysis. Based on our simulations, we estimated that the SU can nominally consume up to 70 mW of power without raising the surrounding tissues' temperature above the thermal safety threshold of 1°C. When considering the natural variation of the model's parameters, we estimated that the power budget could range between 47 and 81 mW. This power budget should be sufficient to power the basic operations of the SU, including amplification, serialization and A/D conversion of the neural signals, as well as control of cortical stimulation. Determining the power budget is an important specification for the design of the SU and, in turn, the design of a fully-implantable BD-BCI system.}, }
@article {pmid37853123, year = {2023}, author = {Ma, S and Chen, M and Jiang, Y and Xiang, X and Wang, S and Wu, Z and Li, S and Cui, Y and Wang, J and Zhu, Y and Zhang, Y and Ma, H and Duan, S and Li, H and Yang, Y and Lingle, CJ and Hu, H}, title = {Sustained antidepressant effect of ketamine through NMDAR trapping in the LHb.}, journal = {Nature}, volume = {}, number = {}, pages = {}, pmid = {37853123}, issn = {1476-4687}, abstract = {Ketamine, an N-methyl-D-aspartate receptor (NMDAR) antagonist[1], has revolutionized the treatment of depression because of its potent, rapid and sustained antidepressant effects[2-4]. Although the elimination half-life of ketamine is only 13 min in mice[5], its antidepressant activities can last for at least 24 h[6-9]. This large discrepancy poses an interesting basic biological question and has strong clinical implications. Here we demonstrate that after a single systemic injection, ketamine continues to suppress burst firing and block NMDARs in the lateral habenula (LHb) for up to 24 h. This long inhibition of NMDARs is not due to endocytosis but depends on the use-dependent trapping of ketamine in NMDARs. The rate of untrapping is regulated by neural activity. Harnessing the dynamic equilibrium of ketamine-NMDAR interactions by activating the LHb and opening local NMDARs at different plasma ketamine concentrations, we were able to either shorten or prolong the antidepressant effects of ketamine in vivo. These results provide new insights into the causal mechanisms of the sustained antidepressant effects of ketamine. The ability to modulate the duration of ketamine action based on the biophysical properties of ketamine-NMDAR interactions opens up new opportunities for the therapeutic use of ketamine.}, }
@article {pmid37853020, year = {2023}, author = {Phunruangsakao, C and Achanccaray, D and Bhattacharyya, S and Izumi, SI and Hayashibe, M}, title = {Effects of visual-electrotactile stimulation feedback on brain functional connectivity during motor imagery practice.}, journal = {Scientific reports}, volume = {13}, number = {1}, pages = {17752}, pmid = {37853020}, issn = {2045-2322}, support = {22H04764//MEXT | Japan Society for the Promotion of Science (JSPS)/ ; }, mesh = {Humans ; Feedback ; Photic Stimulation ; *Imagination/physiology ; Brain/physiology ; Imagery, Psychotherapy ; *Neurofeedback/methods ; Electroencephalography ; }, abstract = {The use of neurofeedback is an important aspect of effective motor rehabilitation as it offers real-time sensory information to promote neuroplasticity. However, there is still limited knowledge about how the brain's functional networks reorganize in response to such feedback. To address this gap, this study investigates the reorganization of the brain network during motor imagery tasks when subject to visual stimulation or visual-electrotactile stimulation feedback. This study can provide healthcare professionals with a deeper understanding of the changes in the brain network and help develop successful treatment approaches for brain-computer interface-based motor rehabilitation applications. We examine individual edges, nodes, and the entire network, and use the minimum spanning tree algorithm to construct a brain network representation using a functional connectivity matrix. Furthermore, graph analysis is used to detect significant features in the brain network that might arise in response to the feedback. Additionally, we investigate the power distribution of brain activation patterns using power spectral analysis and evaluate the motor imagery performance based on the classification accuracy. The results showed that the visual and visual-electrotactile stimulation feedback induced subject-specific changes in brain activation patterns and network reorganization in the [Formula: see text] band. Thus, the visual-electrotactile stimulation feedback significantly improved the integration of information flow between brain regions associated with motor-related commands and higher-level cognitive functions, while reducing cognitive workload in the sensory areas of the brain and promoting positive emotions. Despite these promising results, neither neurofeedback modality resulted in a significant improvement in classification accuracy, compared with the absence of feedback. These findings indicate that multimodal neurofeedback can modulate imagery-mediated rehabilitation by enhancing motor-cognitive communication and reducing cognitive effort. In future interventions, incorporating this technique to ease cognitive demands for participants could be crucial for maintaining their motivation to engage in rehabilitation.}, }
@article {pmid37853010, year = {2023}, author = {Sujatha Ravindran, A and Contreras-Vidal, J}, title = {An empirical comparison of deep learning explainability approaches for EEG using simulated ground truth.}, journal = {Scientific reports}, volume = {13}, number = {1}, pages = {17709}, pmid = {37853010}, issn = {2045-2322}, support = {1650536//National Science Foundation/ ; }, mesh = {*Neural Networks, Computer ; *Deep Learning ; Reproducibility of Results ; Electroencephalography/methods ; Machine Learning ; Algorithms ; }, abstract = {Recent advancements in machine learning and deep learning (DL) based neural decoders have significantly improved decoding capabilities using scalp electroencephalography (EEG). However, the interpretability of DL models remains an under-explored area. In this study, we compared multiple model explanation methods to identify the most suitable method for EEG and understand when some of these approaches might fail. A simulation framework was developed to evaluate the robustness and sensitivity of twelve back-propagation-based visualization methods by comparing to ground truth features. Multiple methods tested here showed reliability issues after randomizing either model weights or labels: e.g., the saliency approach, which is the most used visualization technique in EEG, was not class or model-specific. We found that DeepLift was consistently accurate as well as robust to detect the three key attributes tested here (temporal, spatial, and spectral precision). Overall, this study provides a review of model explanation methods for DL-based neural decoders and recommendations to understand when some of these methods fail and what they can capture in EEG.}, }
@article {pmid37852957, year = {2023}, author = {Huang, Q and Velthuis, H and Pereira, AC and Ahmad, J and Cooke, SF and Ellis, CL and Ponteduro, FM and Puts, NAJ and Dimitrov, M and Batalle, D and Wong, NML and Kowalewski, L and Ivin, G and Daly, E and Murphy, DGM and McAlonan, GM}, title = {Exploratory evidence for differences in GABAergic regulation of auditory processing in autism spectrum disorder.}, journal = {Translational psychiatry}, volume = {13}, number = {1}, pages = {320}, pmid = {37852957}, issn = {2158-3188}, mesh = {Adult ; Humans ; *Autism Spectrum Disorder ; Auditory Perception/physiology ; GABA-B Receptor Agonists/pharmacology/therapeutic use ; *Autistic Disorder ; gamma-Aminobutyric Acid ; }, abstract = {Altered reactivity and responses to auditory input are core to the diagnosis of autism spectrum disorder (ASD). Preclinical models implicate ϒ-aminobutyric acid (GABA) in this process. However, the link between GABA and auditory processing in humans (with or without ASD) is largely correlational. As part of a study of potential biosignatures of GABA function in ASD to inform future clinical trials, we evaluated the role of GABA in auditory repetition suppression in 66 adults (n = 28 with ASD). Neurophysiological responses (temporal and frequency domains) to repetitive standard tones and novel deviants presented in an oddball paradigm were compared after double-blind, randomized administration of placebo, 15 or 30 mg of arbaclofen (STX209), a GABA type B (GABAB) receptor agonist. We first established that temporal mismatch negativity was comparable between participants with ASD and those with typical development (TD). Next, we showed that temporal and spectral responses to repetitive standards were suppressed relative to responses to deviants in the two groups, but suppression was significantly weaker in individuals with ASD at baseline. Arbaclofen reversed weaker suppression of spectral responses in ASD but disrupted suppression in TD. A post hoc analysis showed that arbaclofen-elicited shift in suppression was correlated with autistic symptomatology measured using the Autism Quotient across the entire group, though not in the smaller sample of the ASD and TD group when examined separately. Thus, our results confirm: GABAergic dysfunction contributes to the neurophysiology of auditory sensory processing alterations in ASD, and can be modulated by targeting GABAB activity. These GABA-dependent sensory differences may be upstream of more complex autistic phenotypes.}, }
@article {pmid37851060, year = {2023}, author = {Paudel, KR and Clarence, DD and Panth, N and Manandhar, B and De Rubis, G and Devkota, HP and Gupta, G and Zacconi, FC and Williams, KA and Pont, LG and Singh, SK and Warkiani, ME and Adams, J and MacLoughlin, R and Oliver, BG and Chellappan, DK and Hansbro, PM and Dua, K}, title = {Zerumbone liquid crystalline nanoparticles protect against oxidative stress, inflammation and senescence induced by cigarette smoke extract in vitro.}, journal = {Naunyn-Schmiedeberg's archives of pharmacology}, volume = {}, number = {}, pages = {}, pmid = {37851060}, issn = {1432-1912}, abstract = {The purpose of this study was to evaluate the potential of zerumbone-loaded liquid crystalline nanoparticles (ZER-LCNs) in the protection of broncho-epithelial cells and alveolar macrophages against oxidative stress, inflammation and senescence induced by cigarette smoke extract in vitro. The effect of the treatment of ZER-LCNs on in vitro cell models of cigarette smoke extract (CSE)-treated mouse RAW264.7 and human BCi-NS1.1 basal epithelial cell lines was evaluated for their anti-inflammatory, antioxidant and anti-senescence activities using colorimetric and fluorescence-based assays, fluorescence imaging, RT-qPCR and proteome profiler kit. The ZER-LCNs successfully reduced the expression of pro-inflammatory markers including Il-6, Il-1β and Tnf-α, as well as the production of nitric oxide in RAW 264.7 cells. Additionally, ZER-LCNs successfully inhibited oxidative stress through reduction of reactive oxygen species (ROS) levels and regulation of genes, namely GPX2 and GCLC in BCi-NS1.1 cells. Anti-senescence activity of ZER-LCNs was also observed in BCi-NS1.1 cells, with significant reductions in the expression of SIRT1, CDKN1A and CDKN2A. This study demonstrates strong in vitro anti-inflammatory, antioxidative and anti-senescence activities of ZER-LCNs paving the path for this formulation to be translated into a promising therapeutic agent for chronic respiratory inflammatory conditions including COPD and asthma.}, }
@article {pmid37850195, year = {2023}, author = {Mao, T and Chai, Y and Guo, B and Quan, P and Rao, H}, title = {Sleep Architecture and Sleep EEG Alterations are Associated with Impaired Cognition Under Sleep Restriction.}, journal = {Nature and science of sleep}, volume = {15}, number = {}, pages = {823-838}, pmid = {37850195}, issn = {1179-1608}, abstract = {PURPOSE: Many studies have investigated the cognitive, emotional, and other impairments caused by sleep restriction. However, few studies have explored the relationship between cognitive performance and changes in sleep structure and electroencephalography (EEG) during sleep. The present study aimed to examine whether changes in sleep structure and EEG can account for cognitive impairment caused by sleep restriction.<br><br>PATIENTS AND METHODS: Sixteen young adults spent five consecutive nights (adaptation 9h, baseline 8h, 1st restriction 6h, 2nd restriction 6h, and recovery 10h) in a sleep laboratory, with polysomnography recordings taken during sleep. Throughout waking periods in each condition, participants completed the psychomotor vigilance test (PVT), which measures vigilant attention, and the Go/No-Go task, which measures inhibition control.<br><br>RESULTS: The results showed that sleep restriction significantly decreased the proportion of N1 and N2 sleep, increased the proportion of N3 sleep, and reduced the time spent awake after sleep onset (WASO) and sleep onset latency. Poorer performance on the PVT and Go/No Go task was associated with longer WASO, a larger proportion of N3 sleep, and a smaller proportion of N2 sleep. Additionally, the power spectral density of delta waves significantly increased after sleep restriction, and this increase predicted a decrease in vigilance and inhibition control the next day.<br><br>CONCLUSION: These findings suggest that sleep architecture and EEG signatures may partially explain cognitive impairment caused by sleep restriction.}, }
@article {pmid37846847, year = {2023}, author = {Távora-Vieira, D and Marino, R and Kuthubutheen, J and Broadbent, C and Acharya, A}, title = {Decision making in bone conduction and active middle ear implants - hearing outcomes and experiences over a 10-year period.}, journal = {Cochlear implants international}, volume = {}, number = {}, pages = {1-7}, doi = {10.1080/14670100.2023.2267900}, pmid = {37846847}, issn = {1754-7628}, abstract = {OBJECTIVES: To review the decision-making paradigm in the recommendations of BCI and aMEI overlapping candidacy for patients with conductive or mixed HL, and to determine if there are differences in hearing and quality of life outcomes between these implantable hearing devices.<br><br>METHODS: Retrospective data from patients receiving BCI or aMEI in the past decade were analysed. Patients were grouped into: 1. BCI candidates, 2. BCI or aMEI candidates, and 3. aMEI candidates. We compared outcomes and examined the impact of BC threshold, age at implantation, and duration of hearing loss on candidacy.<br><br>RESULTS: 89 participants were included: 30 BCI, 37 aMEI, and 22 BCI or aMEI candidates. All groups performed similarly in aided sound field threshold testing. BCI group had lower speech scores in quiet compared to 'BCI or aMEI.' No significant differences were found in APHAB global scores. BC threshold, duration of hearing loss, and age at implantation had no significant effects.<br><br>DISCUSSION: Outcomes were generally similar across groups, except for higher effective gain in the aMEI group.<br><br>CONCLUSION: Our proposed patient pathway and decision-making approach facilitate candidate selection for aMEI and BCI, aiming to optimise outcomes.}, }
@article {pmid37845811, year = {2023}, author = {Holt, MW and Robinson, EC and Shlobin, NA and Hanson, JT and Bozkurt, I}, title = {Intracortical brain-computer interfaces for improved motor function: a systematic review.}, journal = {Reviews in the neurosciences}, volume = {}, number = {}, pages = {}, pmid = {37845811}, issn = {2191-0200}, abstract = {In this systematic review, we address the status of intracortical brain-computer interfaces (iBCIs) applied to the motor cortex to improve function in patients with impaired motor ability. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 Guidelines for Systematic Reviews. Risk Of Bias In Non-randomized Studies - of Interventions (ROBINS-I) and the Effective Public Health Practice Project (EPHPP) were used to assess bias and quality. Advances in iBCIs in the last two decades demonstrated the use of iBCI to activate limbs for functional tasks, achieve neural typing for communication, and other applications. However, the inconsistency of performance metrics employed by these studies suggests the need for standardization. Each study was a pilot clinical trial consisting of 1-4, majority male (64.28 %) participants, with most trials featuring participants treated for more than 12 months (55.55 %). The systems treated patients with various conditions: amyotrophic lateral sclerosis, stroke, spinocerebellar degeneration without cerebellar involvement, and spinal cord injury. All participants presented with tetraplegia at implantation and were implanted with microelectrode arrays via pneumatic insertion, with nearly all electrode locations solely at the precentral gyrus of the motor cortex (88.88 %). The development of iBCI devices using neural signals from the motor cortex to improve motor-impaired patients has enhanced the ability of these systems to return ability to their users. However, many milestones remain before these devices can prove their feasibility for recovery. This review summarizes the achievements and shortfalls of these systems and their respective trials.}, }
@article {pmid37845318, year = {2023}, author = {Karas, K and Pozzi, L and Pedrocchi, A and Braghin, F and Roveda, L}, title = {Brain-computer interface for robot control with eye artifacts for assistive applications.}, journal = {Scientific reports}, volume = {13}, number = {1}, pages = {17512}, pmid = {37845318}, issn = {2045-2322}, abstract = {Human-robot interaction is a rapidly developing field and robots have been taking more active roles in our daily lives. Patient care is one of the fields in which robots are becoming more present, especially for people with disabilities. People with neurodegenerative disorders might not consciously or voluntarily produce movements other than those involving the eyes or eyelids. In this context, Brain-Computer Interface (BCI) systems present an alternative way to communicate or interact with the external world. In order to improve the lives of people with disabilities, this paper presents a novel BCI to control an assistive robot with user's eye artifacts. In this study, eye artifacts that contaminate the electroencephalogram (EEG) signals are considered a valuable source of information thanks to their high signal-to-noise ratio and intentional generation. The proposed methodology detects eye artifacts from EEG signals through characteristic shapes that occur during the events. The lateral movements are distinguished by their ordered peak and valley formation and the opposite phase of the signals measured at F7 and F8 channels. This work, as far as the authors' knowledge, is the first method that used this behavior to detect lateral eye movements. For the blinks detection, a double-thresholding method is proposed by the authors to catch both weak blinks as well as regular ones, differentiating itself from the other algorithms in the literature that normally use only one threshold. Real-time detected events with their virtual time stamps are fed into a second algorithm, to further distinguish between double and quadruple blinks from single blinks occurrence frequency. After testing the algorithm offline and in realtime, the algorithm is implemented on the device. The created BCI was used to control an assistive robot through a graphical user interface. The validation experiments including 5 participants prove that the developed BCI is able to control the robot.}, }
@article {pmid37844567, year = {2023}, author = {Rizzoglio, F and Altan, E and Ma, X and Bodkin, KL and Dekleva, BM and Solla, SA and Kennedy, A and Miller, LE}, title = {From monkeys to humans: observation-based EMG brain-computer interface decoders for humans with paralysis.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad038e}, pmid = {37844567}, issn = {1741-2552}, abstract = {Intracortical brain-computer interfaces (iBCIs) aim to enable individuals with paralysis to control the movement of virtual limbs and robotic arms. Because patients' paralysis prevents training a direct neural activity to limb movement decoder, most iBCIs rely on "observation-based" decoding in which the patient watches a moving cursor while mentally envisioning making the movement. However, this reliance on observed target motion for decoder development precludes its application to the prediction of unobservable motor output like muscle activity. Here, we ask whether recordings of muscle activity from a surrogate individual performing the same movement as the iBCI patient can be used as target for an iBCI decoder. We test two possible approaches, each using data from a human iBCI user and a monkey, both performing similar motor actions. In one approach, we trained a decoder to predict the electromyographic (EMG) activity of a monkey from neural signals recorded from a human. We then contrast this to a second approach, based on the hypothesis that the low-dimensional "latent" neural representations of motor behavior, known to be preserved across time for a given behavior, might also be preserved across individuals. We "transferred" an EMG decoder trained solely on monkey data to the human iBCI user after using Canonical Correlation Analysis to align the human latent signals to those of the monkey. We found that both direct and transfer decoding approaches allowed accurate EMG predictions between two monkeys and from a monkey to a human. Our findings suggest that these latent representations of behavior are consistent across animals and even primate species. These methods are an important initial step in the development of iBCI decoders that generate EMG predictions that could serve as signals for a biomimetic decoder controlling motion and impedance of a prosthetic arm, or even muscle force directly through Functional Electrical Stimulation.}, }
@article {pmid37841873, year = {2023}, author = {Crone, N and Candrea, D and Shah, S and Luo, S and Angrick, M and Rabbani, Q and Coogan, C and Milsap, G and Nathan, K and Wester, B and Anderson, W and Rosenblatt, K and Clawson, L and Maragakis, N and Vansteensel, M and Tenore, F and Ramsey, N and Fifer, M and Uchil, A}, title = {A click-based electrocorticographic brain-computer interface enables long-term high-performance switch-scan spelling.}, journal = {Research square}, volume = {}, number = {}, pages = {}, doi = {10.21203/rs.3.rs-3158792/v1}, pmid = {37841873}, abstract = {Background Brain-computer interfaces (BCIs) can restore communication in movement- and/or speech-impaired individuals by enabling neural control of computer typing applications. Single command "click" decoders provide a basic yet highly functional capability. Methods We sought to test the performance and long-term stability of click-decoding using a chronically implanted high density electrocorticographic (ECoG) BCI with coverage of the sensorimotor cortex in a human clinical trial participant (ClinicalTrials.gov, NCT03567213) with amyotrophic lateral sclerosis (ALS). We trained the participant's click decoder using a small amount of training data (< 44 minutes across four days) collected up to 21 days prior to BCI use, and then tested it over a period of 90 days without any retraining or updating. Results Using this click decoder to navigate a switch-scanning spelling interface, the study participant was able to maintain a median spelling rate of 10.2 characters per min. Though a transient reduction in signal power modulation interrupted testing with this fixed model, a new click decoder achieved comparable performance despite being trained with even less data (< 15 min, within one day). Conclusion These results demonstrate that a click decoder can be trained with a small ECoG dataset while retaining robust performance for extended periods, providing functional text-based communication to BCI users.}, }
@article {pmid37503096, year = {2023}, author = {Kukkar, KK and Rao, N and Huynh, D and Shah, S and Contreras-Vidal, JL and Parikh, PJ}, title = {Task-dependent Alteration in Delta Band Corticomuscular Coherence during Standing in Chronic Stroke Survivors.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, pmid = {37503096}, abstract = {Balance control is an important indicator of mobility and independence in activities of daily living. How the changes in functional integrity of corticospinal tract due to stroke affects the maintenance of upright stance remains to be known. We investigated the changes in functional coupling between the cortex and lower limb muscles during a challenging balance task over multiple frequency bands in chronic stroke survivors. Eleven stroke patients and nine healthy controls performed a challenging balance task. They stood on a computerized platform with/without somatosensory input distortion created by sway-referencing the support surface, thereby varying the difficulty levels of the task. We computed corticomuscular coherence between Cz (electroencephalography) and leg muscles and assessed balance performance using Berg Balance scale (BBS), Timed-up and go (TUG) and center of pressure (COP) measures. We found lower delta frequency band coherence in stroke patients when compared with healthy controls under medium difficulty condition for distal but not proximal leg muscles. For both groups, we found similar coherence at other frequency bands. On BBS and TUG, stroke patients showed poor balance. However, similar group differences were not consistently observed across COP measures. The presence of distal versus proximal effect suggests differences in the (re)organization of the corticospinal connections across the two muscles groups for balance control. We argue that the observed group difference in the delta coherence might be due to altered mechanisms for the detection of somatosensory modulation resulting from sway-referencing of the support platform for balance control.}, }
@article {pmid37398375, year = {2023}, author = {Celotto, M and Bím, J and Tlaie, A and De Feo, V and Lemke, S and Chicharro, D and Nili, H and Bieler, M and Hanganu-Opatz, IL and Donner, TH and Brovelli, A and Panzeri, S}, title = {An information-theoretic quantification of the content of communication between brain regions.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {37398375}, support = {R01 NS108410/NS/NINDS NIH HHS/United States ; R01 NS109961/NS/NINDS NIH HHS/United States ; U19 NS107464/NS/NINDS NIH HHS/United States ; }, abstract = {Quantifying the amount, content and direction of communication between brain regions is key to understanding brain function. Traditional methods to analyze brain activity based on the Wiener-Granger causality principle quantify the overall information propagated by neural activity between simultaneously recorded brain regions, but do not reveal the information flow about specific features of interest (such as sensory stimuli). Here, we develop a new information theoretic measure termed Feature-specific Information Transfer (FIT), quantifying how much information about a specific feature flows between two regions. FIT merges the Wiener-Granger causality principle with information-content specificity. We first derive FIT and prove analytically its key properties. We then illustrate and test them with simulations of neural activity, demonstrating that FIT identifies, within the total information flowing between regions, the information that is transmitted about specific features. We then analyze three neural datasets obtained with different recording methods, magneto- and electro-encephalography, and spiking activity, to demonstrate the ability of FIT to uncover the content and direction of information flow between brain regions beyond what can be discerned with traditional anaytical methods. FIT can improve our understanding of how brain regions communicate by uncovering previously hidden feature-specific information flow.}, }
@article {pmid37162924, year = {2023}, author = {Qi, C and Verheijen, BM and Kokubo, Y and Shi, Y and Tetter, S and Murzin, AG and Nakahara, A and Morimoto, S and Vermulst, M and Sasaki, R and Aronica, E and Hirokawa, Y and Oyanagi, K and Kakita, A and Ryskeldi-Falcon, B and Yoshida, M and Hasegawa, M and Scheres, SHW and Goedert, M}, title = {Tau Filaments from Amyotrophic Lateral Sclerosis/Parkinsonism-Dementia Complex (ALS/PDC) adopt the CTE Fold.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {37162924}, support = {R01 AG054641/AG/NIA NIH HHS/United States ; }, abstract = {The amyotrophic lateral sclerosis/parkinsonism-dementia complex (ALS/PDC) of the island of Guam and the Kii peninsula of Japan is a fatal neurodegenerative disease of unknown cause that is characterised by the presence of abundant filamentous tau inclusions in brains and spinal cords. Here we used electron cryo-microscopy (cryo-EM) to determine the structures of tau filaments from the cerebral cortex of three cases of ALS/PDC from Guam and eight cases from Kii, as well as from the spinal cord of two of the Guam cases. Tau filaments had the chronic traumatic encephalopathy (CTE) fold, with variable amounts of Type I and Type II filaments. Paired helical tau filaments were also found in two Kii cases. We also identified a novel Type III CTE tau filament, where protofilaments pack against each other in an anti-parallel fashion. ALS/PDC is the third known tauopathy with CTE-type filaments and abundant tau inclusions in cortical layers II/III, the others being CTE and subacute sclerosing panencephalitis. Because these tauopathies are believed to have environmental causes, our findings support the hypothesis that ALS/PDC is caused by exogenous factors.}, }
@article {pmid37841689, year = {2023}, author = {Yan, T and Suzuki, K and Kameda, S and Maeda, M and Mihara, T and Hirata, M}, title = {Chronic subdural electrocorticography in nonhuman primates by an implantable wireless device for brain-machine interfaces.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1260675}, pmid = {37841689}, issn = {1662-4548}, abstract = {BACKGROUND: Subdural electrocorticography (ECoG) signals have been proposed as a stable, good-quality source for brain-machine interfaces (BMIs), with a higher spatial and temporal resolution than electroencephalography (EEG). However, long-term implantation may lead to chronic inflammatory reactions and connective tissue encapsulation, resulting in a decline in signal recording quality. However, no study has reported the effects of the surrounding tissue on signal recording and device functionality thus far.<br><br>METHODS: In this study, we implanted a wireless recording device with a customized 32-electrode-ECoG array subdurally in two nonhuman primates for 15&#x2009;months. We evaluated the neural activities recorded from and wirelessly transmitted to the devices and the chronic tissue reactions around the electrodes. In addition, we measured the gain factor of the newly formed ventral fibrous tissue in vivo.<br><br>RESULTS: Time-frequency analyses of the acute and chronic phases showed similar signal features. The average root mean square voltage and power spectral density showed relatively stable signal quality after chronic implantation. Histological examination revealed thickening of the reactive tissue around the electrode array; however, no evident inflammation in the cortex. From gain factor analysis, we found that tissue proliferation under electrodes reduced the amplitude power of signals.<br><br>CONCLUSION: This study suggests that subdural ECoG may provide chronic signal recordings for future clinical applications and neuroscience research. This study also highlights the need to reduce proliferation of reactive tissue ventral to the electrodes to enhance long-term stability.}, }
@article {pmid37840917, year = {2023}, author = {Zeng, P and Wang, T and Zhang, L and Guo, F}, title = {Exploring the causes of augmentation in restless legs syndrome.}, journal = {Frontiers in neurology}, volume = {14}, number = {}, pages = {1160112}, pmid = {37840917}, issn = {1664-2295}, abstract = {Long-term drug treatment for Restless Legs Syndrome (RLS) patients can frequently result in augmentation, which is the deterioration of symptoms with an increased drug dose. The cause of augmentation, especially derived from dopamine therapy, remains elusive. Here, we review recent research and clinical progress on the possible mechanism underlying RLS augmentation. Dysfunction of the dopamine system highly possibly plays a role in the development of RLS augmentation, as dopamine agonists improve desensitization of dopamine receptors, disturb receptor interactions within or outside the dopamine receptor family, and interfere with the natural regulation of dopamine synthesis and release in the neural system. Iron deficiency is also indicated to contribute to RLS augmentation, as low iron levels can affect the function of the dopamine system. Furthermore, genetic risk factors, such as variations in the BTBD9 and MEIS1 genes, have been linked to an increased risk of RLS initiation and augmentation. Additionally, circadian rhythm, which controls the sleep-wake cycle, may also contribute to the worsening of RLS symptoms and the development of augmentation. Recently, Vitamin D deficiency has been suggested to be involved in RLS augmentation. Based on these findings, we propose that the progressive reduction of selective receptors, influenced by various pathological factors, reverses the overcompensation of the dopamine intensity promoted by short-term, low-dose dopaminergic therapy in the development of augmentation. More research is needed to uncover a deeper understanding of the mechanisms underlying the RLS symptom and to develop effective RLS augmentation treatments.}, }
@article {pmid37840542, year = {2023}, author = {Yang, X and Ballini, M and Sawigun, C and Hsu, WY and Weijers, JW and Putzeys, J and Lopez, CM}, title = {An AC-Coupled 1st-order Δ-ΔΣ Readout IC for Area-Efficient Neural Signal Acquisition.}, journal = {IEEE journal of solid-state circuits}, volume = {58}, number = {4}, pages = {949-960}, pmid = {37840542}, issn = {0018-9200}, support = {U01 NS115587/NS/NINDS NIH HHS/United States ; }, abstract = {The current demand for high-channel-count neural-recording interfaces calls for more area- and power-efficient readout architectures that do not compromise other electrical performances. In this paper, we present a miniature 128-channel neural recording integrated circuit (NRIC) for the simultaneous acquisition of local field potentials (LFPs) and action potentials (APs), which can achieve a very good compromise between area, power, noise, input range and electrode DC offset cancellation. An AC-coupled 1[st]-order digitally-intensive Δ-ΔΣ architecture is proposed to achieve this compromise and to leverage the advantages of a highly-scaled technology node. A prototype NRIC, including 128 channels, a newly-proposed area-efficient bulk-regulated voltage reference, biasing circuits and a digital control, has been fabricated in 22-nm FDSOI CMOS and fully characterized. Our proposed architecture achieves a total area per channel of 0.005 mm[2], a total power per channel of 12.57 μW, and an input-referred noise of 7.7 ± 0.4 μVrms in the AP band and 11.9 ± 1.1 μVrms in the LFP band. A very good channel-to-channel uniformity is demonstrated by our measurements. The chip has been validated in vivo, demonstrating its capability to successfully record full-band neural signals.}, }
@article {pmid37840396, year = {2023}, author = {Cho, Y and Jeong, HH and Shin, H and Pak, CJ and Cho, J and Kim, Y and Kim, D and Kim, T and Kim, H and Kim, S and Kwon, S and Hong, JP and Suh, HP and Lee, S}, title = {Hybrid Bionic Nerve Interface for Application in Bionic Limbs.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2303728}, doi = {10.1002/advs.202303728}, pmid = {37840396}, issn = {2198-3844}, support = {RS-2020-KD000196//Korea Medical Device Development Fund/ ; }, abstract = {Intuitive and perceptual neuroprosthetic systems require a high degree of neural control and a variety of sensory feedback, but reliable neural interfaces for long-term use that maintain their functionality are limited. Here, a novel hybrid bionic interface is presented, fabricated by integrating a biological interface (regenerative peripheral nerve interface (RPNI)) and a peripheral neural interface to enhance the neural interface performance between a nerve and bionic limbs. This interface utilizes a shape memory polymer buckle that can be easily implanted on a severed nerve and make contact with both the nerve and the muscle graft after RPNI formation. It is demonstrated that this interface can simultaneously record different signal information via the RPNI and the nerve, as well as stimulate them separately, inducing different responses. Furthermore, it is shown that this interface can record naturally evoked signals from a walking rabbit and use them to control a robotic leg. The long-term functionality and biocompatibility of this interface in rabbits are evaluated for up to 29 weeks, confirming its promising potential for enhancing prosthetic control.}, }
@article {pmid37840179, year = {2023}, author = {Ji, Y and Ma, BJ and Guo, XQ and Dong, HP and Ma, K}, title = {[Discussion on the composition and implementation of diagnosis and treatment strategies for whole field pain management strategy].}, journal = {Zhonghua yi xue za zhi}, volume = {103}, number = {39}, pages = {3083-3087}, doi = {10.3760/cma.j.cn112137-20230704-01135}, pmid = {37840179}, issn = {0376-2491}, abstract = {Pain is the fifth major vital sign, and chronic pain is a large category of diseases that affects health seriously. At present, the incidence of chronic pain is high, but the overall treatment satisfaction is low. It is necessary to continuously optimize pain diagnosis and treatment strategies and improve the connotation of pain management. Based on the clinical practice of our pain center, combined with relevant literature, the article proposes a diagnosis and treatment strategy of "whole field pain management" should be carried out from the four dimensions of feeling, emotion, cognition, and behavior. Innovative digital pain diagnosis and treatment technologies such as VR/MR and brain-computer interface are used to regulate emotional, cognitive, and behavioral regulation, and combined with lifestyle changes, rehabilitation physiotherapy, drugs, and minimally invasive interventional therapy to constitute a " whole field pain management strategy" to explore the new development direction of further improving the management of chronic pain.}, }
@article {pmid37839711, year = {2023}, author = {Luo, R and Mai, X and Meng, J}, title = {Effect of Motion State Variability on Error-related Potentials during Continuous Feedback Paradigms and Their Consequences for Classification.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {109982}, doi = {10.1016/j.jneumeth.2023.109982}, pmid = {37839711}, issn = {1872-678X}, abstract = {BACKGROUND: An erroneous motion would elicit the error-related potential (ErrP) when humans monitor the behavior of the external devices. This EEG modality has been largely applied to brain-computer interface in an active or passive manner with discrete visual feedback. However, the effect of variable motion state on ErrP morphology and classification performance raises concerns when the interaction is conducted with continuous visual feedback.<br><br>NEW METHOD: In the present study, we designed a cursor control experiment. Participants monitored a continuously moving cursor to reach the target on one side of the screen. Motion state varied multiple times with two factors: (1) motion direction and (2) motion speed. The effects of these two factors on the morphological characteristics and classification performance of ErrP were analyzed. Furthermore, an offline simulation was performed to evaluate the effectiveness of the proposed extended ErrP-decoder in resolving the interference by motion direction changes.<br><br>RESULTS: The statistical analyses revealed that motion direction and motion speed significantly influenced the amplitude of feedback-ERN and frontal-Pe components, while only motion direction significantly affected the classification performance.<br><br>Significant deviation was found in ErrP detection utilizing classical correct-versus-erroneous event training. However, this bias can be alleviated by 16% by the extended ErrP-decoder.<br><br>CONCLUSION: The morphology and classification performance of ErrP signal can be affected by motion state variability during continuous feedback paradigms. The results enhance the comprehension of ErrP morphological components and shed light on the detection of BCI's error behavior in practical continuous control.}, }
@article {pmid37790319, year = {2023}, author = {Jansen, R and Milaneschi, Y and Schranner, D and Kastenmuller, G and Arnold, M and Han, X and Dunlop, BW and ,  and Rush, AJ and Kaddurah-Daouk, R and Penninx, BW}, title = {The Metabolome-Wide Signature of Major Depressive Disorder.}, journal = {Research square}, volume = {}, number = {}, pages = {}, pmid = {37790319}, abstract = {Major Depressive Disorder (MDD) is an often-chronic condition with substantial molecular alterations and pathway dysregulations involved. Single metabolite, pathway and targeted metabolomics platforms have indeed revealed several metabolic alterations in depression including energy metabolism, neurotransmission and lipid metabolism. More comprehensive coverage of the metabolome is needed to further specify metabolic dysregulation in depression and reveal previously untargeted mechanisms. Here we measured 820 metabolites using the metabolome-wide Metabolon platform in 2770 subjects from a large Dutch clinical cohort with extensive depression clinical phenotyping (1101 current MDD, 868 remitted MDD, 801 healthy controls) at baseline and 1805 subjects at 6-year follow up (327 current MDD, 1045 remitted MDD, 433 healthy controls). MDD diagnosis was based on DSM-IV psychiatric interviews. Depression severity was measured with the Inventory of Depressive Symptomatology self-report. Associations between metabolites and MDD status and depression severity were assessed at baseline and at the 6-year follow-up. Metabolites consistently associated with MDD status or depression severity on both occasions were examined in Mendelian randomization (MR) analysis using metabolite (N=14,000) and MDD (N=800,000) GWAS results. At baseline, 139 and 126 metabolites were associated with current MDD status and depression severity, respectively, with 79 overlapping metabolites. Six years later, 34 out of the 79 metabolite associations were subsequently replicated. Downregulated metabolites were enriched with long-chain monounsaturated (P=6.7e-07) and saturated (P=3.2e-05) fatty acids and upregulated metabolites with lysophospholipids (P=3.4e-4). Adding BMI to the models changed results only marginally. MR analyses showed that genetically-predicted higher levels of the lysophospholipid 1-linoleoyl-GPE (18:2) were associated with greater risk of depression. The identified metabolome-wide profile of depression (severity) indicated altered lipid metabolism with downregulation of long-chain fatty acids and upregulation of lysophospholipids, for which causal involvement was suggested using genetic tools. This metabolomics signature offers a window on depression pathophysiology and a potential access point for the development of novel therapeutic approaches.}, }
@article {pmid37832939, year = {2023}, author = {Pancholi, S and Wachs, JP and Duerstock, BS}, title = {Use of Artificial Intelligence Techniques to Assist Individuals with Physical Disabilities.}, journal = {Annual review of biomedical engineering}, volume = {}, number = {}, pages = {}, doi = {10.1146/annurev-bioeng-082222-012531}, pmid = {37832939}, issn = {1545-4274}, abstract = {Assistive technologies (AT) enable people with disabilities to perform activities of daily living more independently, have greater access to community and healthcare services, and be more productive performing educational and/or employment tasks. Integrating artificial intelligence (AI) with various agents, including electronics, robotics, and software, has revolutionized AT, resulting in groundbreaking technologies such as mind-controlled exoskeletons, bionic limbs, intelligent wheelchairs, and smart home assistants. This article provides a review of various AI techniques that have helped those with physical disabilities, including brain-computer interfaces, computer vision, natural language processing, and human-computer interaction. The current challenges and future directions for AI-powered advanced technologies are also addressed. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 26 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.}, }
@article {pmid37830040, year = {2023}, author = {Chen, W and Yang, H}, title = {Editorial: New challenges and future perspectives in motivation and reward.}, journal = {Frontiers in behavioral neuroscience}, volume = {17}, number = {}, pages = {1293938}, pmid = {37830040}, issn = {1662-5153}, }
@article {pmid37829725, year = {2023}, author = {Crétot-Richert, G and De Vos, M and Debener, S and Bleichner, MG and Voix, J}, title = {Assessing focus through ear-EEG: a comparative study between conventional cap EEG and mobile in- and around-the-ear EEG systems.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {895094}, pmid = {37829725}, issn = {1662-4548}, abstract = {INTRODUCTION: As our attention is becoming a commodity that an ever-increasing number of applications are competing for, investing in modern day tools and devices that can detect our mental states and protect them from outside interruptions holds great value. Mental fatigue and distractions are impacting our ability to focus and can cause workplace injuries. Electroencephalography (EEG) may reflect concentration, and if EEG equipment became wearable and inconspicuous, innovative brain-computer interfaces (BCI) could be developed to monitor mental load in daily life situations. The purpose of this study is to investigate the potential of EEG recorded inside and around the human ear to determine levels of attention and focus.<br><br>METHODS: In this study, mobile and wireless ear-EEG were concurrently recorded with conventional EEG (cap) systems to collect data during tasks related to focus: an N-back task to assess working memory and a mental arithmetic task to assess cognitive workload. The power spectral density (PSD) of the EEG signal was analyzed to isolate consistent differences between mental load conditions and classify epochs using step-wise linear discriminant analysis (swLDA).<br><br>RESULTS AND DISCUSSION: Results revealed that spectral features differed statistically between levels of cognitive load for both tasks. Classification algorithms were tested on spectral features from twelve and two selected channels, for the cap and the ear-EEG. A two-channel ear-EEG model evaluated the performance of two dry in-ear electrodes specifically. Single-trial classification for both tasks revealed above chance-level accuracies for all subjects, with mean accuracies of: 96% (cap-EEG) and 95% (ear-EEG) for the twelve-channel models, 76% (cap-EEG) and 74% (in-ear-EEG) for the two-channel model for the N-back task; and 82% (cap-EEG) and 85% (ear-EEG) for the twelve-channel, 70% (cap-EEG) and 69% (in-ear-EEG) for the two-channel model for the arithmetic task. These results suggest that neural oscillations recorded with ear-EEG can be used to reliably differentiate between levels of cognitive workload and working memory, in particular when multi-channel recordings are available, and could, in the near future, be integrated into wearable devices.}, }
@article {pmid37829569, year = {2023}, author = {Liu, YF and Wang, W and Chen, XF}, title = {Progress and prospects in flexible tactile sensors.}, journal = {Frontiers in bioengineering and biotechnology}, volume = {11}, number = {}, pages = {1264563}, pmid = {37829569}, issn = {2296-4185}, abstract = {Flexible tactile sensors have the advantages of large deformation detection, high fault tolerance, and excellent conformability, which enable conformal integration onto the complex surface of human skin for long-term bio-signal monitoring. The breakthrough of flexible tactile sensors rather than conventional tactile sensors greatly expanded application scenarios. Flexible tactile sensors are applied in fields including not only intelligent wearable devices for gaming but also electronic skins, disease diagnosis devices, health monitoring devices, intelligent neck pillows, and intelligent massage devices in the medical field; intelligent bracelets and metaverse gloves in the consumer field; as well as even brain-computer interfaces. Therefore, it is necessary to provide an overview of the current technological level and future development of flexible tactile sensors to ease and expedite their deployment and to make the critical transition from the laboratory to the market. This paper discusses the materials and preparation technologies of flexible tactile sensors, summarizing various applications in human signal monitoring, robotic tactile sensing, and human-machine interaction. Finally, the current challenges on flexible tactile sensors are also briefly discussed, providing some prospects for future directions.}, }
@article {pmid37824505, year = {2023}, author = {Yao, J and Cai, Z and Qian, Z and Yang, B}, title = {A noval approach based on TCN-LSTM network for predicting waterlogging depth with waterlogging monitoring station.}, journal = {PloS one}, volume = {18}, number = {10}, pages = {e0286821}, pmid = {37824505}, issn = {1932-6203}, abstract = {As a result of climate change and rapid urbanization, urban waterlogging commonly caused by rainstorm, is becoming more frequent and more severe in developing countries. Urban waterlogging sometimes results in significant financial losses as well as human casualties. Accurate waterlogging depth prediction is critical for early warning system and emergency response. However, the existing hydrological models need to obtain more abundant hydrological data, and the model construction is complicated. The waterlogging depth prediction technology based on object detection model are highly dependent on image data. To solve the above problem, we propose a novel approach based on Temporal Convolutional Networks and Long Short-Term Memory networks to predicting urban waterlogging depth with Waterlogging Monitoring Station. The difficulty of data acquisition is small though Waterlogging Monitoring Station and TCN-LSTM model can be used to predict timely waterlogging depth. Waterlogging Monitoring Station is developed which integrates an automatic rain gauge and a water gauge. The rainfall and waterlogging depth can be obtained by periodic sampling at some areas with Waterlogging Monitoring Station. Precise hydrological data such as waterlogging depth and rainfall collected by Waterlogging Monitoring Station are used as training samples. Then training samples are used to train TCN-LSTM model, and finally a model with good prediction effect is obtained. The experimental results show that the difficulty of data acquisition is small, the complexity is low and the proposed TCN-LSTM hybrid model can properly predict the waterlogging depth of the current regional. There is no need for high dependence on image data. Meanwhile, compared with machine learning model and RNN model, TCN-LSTM model has higher prediction accuracy for time series data. Overall, the low-cost method proposed in this study can be used to obtain timely waterlogging warning information, and enhance the possibility of using existing social networks and traffic surveillance video systems to perform opportunistic waterlogging sensing.}, }
@article {pmid37824324, year = {2023}, author = {Wang, X and Liu, A and Wu, L and Guan, L and Chen, X}, title = {Improving Generalized Zero-Shot Learning SSVEP Classification Performance from Data-Efficient Perspective.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3324148}, pmid = {37824324}, issn = {1558-0210}, abstract = {Generalized zero-shot learning (GZSL) has significantly reduced the training requirements for steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). Traditional methods require complete class data sets for training, but GZSL allows for only partial class data sets, dividing them into 'seen' (those with training data) and 'unseen' classes (those without training data). However, inefficient utilization of SSVEP data limits the accuracy and information transfer rate (ITR) of existing GZSL methods. To this end, we proposed a framework for more effective utilization of SSVEP data at three systematically combined levels: data acquisition, feature extraction, and decision-making. First, prevalent SSVEP-based BCIs overlook the inter-subject variance in visual latency and employ fixed sampling starting time (SST). We introduced a dynamic sampling starting time (DSST) strategy at the data acquisition level. This strategy uses the classification results on the validation set to find the optimal sampling starting time (OSST) for each subject. In addition, we developed a Transformer structure to capture the global information of input data for compensating the small receptive field of existing networks. The global receptive fields of the Transformer can adequately process the information from longer input sequences. For the decision-making level, we designed a classifier selection strategy that can automatically select the optimal classifier for the seen and unseen classes, respectively. We also proposed a training procedure to make the above solutions in conjunction with each other. Our method was validated on three public datasets and outperformed the state-of-the-art (SOTA) methods. Crucially, we also outperformed the representative methods that require training data for all classes.}, }
@article {pmid37822240, year = {2023}, author = {Laport, F and Dapena, A and Castro, PM and Iglesias, DI and Vazquez-Araujo, FJ}, title = {Eye State Detection Using Frequency Features from 1 or 2-Channel EEG.}, journal = {International journal of neural systems}, volume = {}, number = {}, pages = {2350062}, doi = {10.1142/S0129065723500624}, pmid = {37822240}, issn = {1793-6462}, abstract = {Brain-computer interfaces (BCIs) establish a direct communication channel between the human brain and external devices. Among various methods, electroencephalography (EEG) stands out as the most popular choice for BCI design due to its non-invasiveness, ease of use, and cost-effectiveness. This paper aims to present and compare the accuracy and robustness of an EEG system employing one or two channels. We present both hardware and algorithms for the detection of open and closed eyes. Firstly, we utilize a low-cost hardware device to capture EEG activity from one or two channels. Next, we apply the discrete Fourier transform to analyze the signals in the frequency domain, extracting features from each channel. For classification, we test various well-known techniques, including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Decision Tree (DT), or Logistic Regression (LR). To evaluate the system, we conduct experiments, acquiring signals associated with open and closed eyes, and compare the performance between one and two channels. The results demonstrate that employing a system with two channels and using SVM, DT, or LR classifiers enhances robustness compared to a single-channel setup and allows us to achieve an accuracy percentage greater than 95% for both eye states.}, }
@article {pmid37820859, year = {2023}, author = {Hao, Z and Zhai, X and Peng, B and Cheng, D and Zhang, Y and Pan, Y and Dou, W}, title = {CAMBA framework: Unveiling the brain asymmetry alterations and longitudinal changes after stroke using resting-state EEG.}, journal = {NeuroImage}, volume = {282}, number = {}, pages = {120405}, doi = {10.1016/j.neuroimage.2023.120405}, pmid = {37820859}, issn = {1095-9572}, abstract = {Hemispheric asymmetry or lateralization is a fundamental principle of brain organization. However, it is poorly understood to what extent the brain asymmetries across different levels of functional organizations are evident in health or altered in brain diseases. Here, we propose a framework that integrates three degrees of brain interactions (isolated nodes, node-node, and edge-edge) into a unified analysis pipeline to capture the sliding window-based asymmetry dynamics at both the node and hemisphere levels. We apply this framework to resting-state EEG in healthy and stroke populations and investigate the stroke-induced abnormal alterations in brain asymmetries and longitudinal asymmetry changes during poststroke rehabilitation. We observe that the mean asymmetry in patients was abnormally enhanced across different frequency bands and levels of brain interactions, with these abnormal patterns strongly associated with the side of the stroke lesion. Compared to healthy controls, patients displayed significant alterations in asymmetry fluctuations, disrupting and reconfiguring the balance of inter-hemispheric integration and segregation. Additionally, analyses reveal that specific abnormal asymmetry metrics in patients tend to move towards those observed in healthy controls after short-term brain-computer interface rehabilitation. Furthermore, preliminary evidence suggests that baseline clinical and asymmetry features can predict poststroke improvements in the Fugl-Meyer assessment of the lower extremity (mean absolute error of about 2). Overall, these findings advance our understanding of hemispheric asymmetry. Our framework offers new insights into the mechanisms underlying brain alterations and recovery after a brain lesion, may help identify prognostic biomarkers, and can be easily extended to different functional modalities.}, }
@article {pmid37820004, year = {2023}, author = {Ortiz-Catalan, M and Zbinden, J and Millenaar, J and D'Accolti, D and Controzzi, M and Clemente, F and Cappello, L and Earley, EJ and Mastinu, E and Kolankowska, J and Munoz-Novoa, M and Jönsson, S and Cipriani, C and Sassu, P and Brånemark, R}, title = {A highly integrated bionic hand with neural control and feedback for use in daily life.}, journal = {Science robotics}, volume = {8}, number = {83}, pages = {eadf7360}, doi = {10.1126/scirobotics.adf7360}, pmid = {37820004}, issn = {2470-9476}, abstract = {Restoration of sensorimotor function after amputation has remained challenging because of the lack of human-machine interfaces that provide reliable control, feedback, and attachment. Here, we present the clinical implementation of a transradial neuromusculoskeletal prosthesis-a bionic hand connected directly to the user's nervous and skeletal systems. In one person with unilateral below-elbow amputation, titanium implants were placed intramedullary in the radius and ulna bones, and electromuscular constructs were created surgically by transferring the severed nerves to free muscle grafts. The native muscles, free muscle grafts, and ulnar nerve were implanted with electrodes. Percutaneous extensions from the titanium implants provided direct skeletal attachment and bidirectional communication between the implanted electrodes and a prosthetic hand. Operation of the bionic hand in daily life resulted in improved prosthetic function, reduced postamputation, and increased quality of life. Sensations elicited via direct neural stimulation were consistently perceived on the phantom hand throughout the study. To date, the patient continues using the prosthesis in daily life. The functionality of conventional artificial limbs is hindered by discomfort and limited and unreliable control. Neuromusculoskeletal interfaces can overcome these hurdles and provide the means for the everyday use of a prosthesis with reliable neural control fixated into the skeleton.}, }
@article {pmid37819985, year = {2023}, author = {Wang, R and Chen, X and Khalilian-Gourtani, A and Yu, L and Dugan, P and Friedman, D and Doyle, W and Devinsky, O and Wang, Y and Flinker, A}, title = {Distributed feedforward and feedback cortical processing supports human speech production.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {120}, number = {42}, pages = {e2300255120}, doi = {10.1073/pnas.2300255120}, pmid = {37819985}, issn = {1091-6490}, support = {IIS-1912286//National Science Foundation (NSF)/ ; R01NS109367//HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01NS115929//HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS)/ ; R01DC018805//HHS | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)/ ; }, abstract = {Speech production is a complex human function requiring continuous feedforward commands together with reafferent feedback processing. These processes are carried out by distinct frontal and temporal cortical networks, but the degree and timing of their recruitment and dynamics remain poorly understood. We present a deep learning architecture that translates neural signals recorded directly from the cortex to an interpretable representational space that can reconstruct speech. We leverage learned decoding networks to disentangle feedforward vs. feedback processing. Unlike prevailing models, we find a mixed cortical architecture in which frontal and temporal networks each process both feedforward and feedback information in tandem. We elucidate the timing of feedforward and feedback-related processing by quantifying the derived receptive fields. Our approach provides evidence for a surprisingly mixed cortical architecture of speech circuitry together with decoding advances that have important implications for neural prosthetics.}, }
@article {pmid37818111, year = {2023}, author = {Jia, G and Bai, S and Lin, Y and Wang, X and Zhu, L and Lyu, C and Sun, G and An, K and Roe, AW and Li, X and Gao, L}, title = {Representation of conspecific vocalizations in amygdala of awake marmosets.}, journal = {National science review}, volume = {10}, number = {11}, pages = {nwad194}, pmid = {37818111}, issn = {2053-714X}, abstract = {Human speech and animal vocalizations are important for social communication and animal survival. Neurons in the auditory pathway are responsive to a range of sounds, from elementary sound features to complex acoustic sounds. For social communication, responses to distinct patterns of vocalization are usually highly specific to an individual conspecific call, in some species. This includes the specificity of sound patterns and embedded biological information. We conducted single-unit recordings in the amygdala of awake marmosets and presented calls used in marmoset communication, calls of other species and calls from specific marmoset individuals. We found that some neurons (47/262) in the amygdala distinguished 'Phee' calls from vocalizations of other animals and other types of marmoset vocalizations. Interestingly, a subset of Phee-responsive neurons (22/47) also exhibited selectivity to one out of the three Phees from two different 'caller' marmosets. Our findings suggest that, while it has traditionally been considered the key structure in the limbic system, the amygdala also represents a critical stage of socially relevant auditory perceptual processing.}, }
@article {pmid37817699, year = {2023}, author = {Zhao, Y and Xiong, D and Yang, B and Xia, S and Zhang, X}, title = {Application Of Multigene Panel Detection In Breast Cancer.}, journal = {JPMA. The Journal of the Pakistan Medical Association}, volume = {73}, number = {9}, pages = {1862-1868}, doi = {10.47391/JPMA.6830}, pmid = {37817699}, issn = {0030-9982}, mesh = {Humans ; Female ; *Breast Neoplasms/diagnosis/genetics/drug therapy ; Gene Expression Profiling ; Prognosis ; Chemotherapy, Adjuvant ; Precision Medicine ; }, abstract = {Precision medicine will be the direction of future medical development, especially in cancer diagnosis and treatment. With the deepening of breast cancer-related research, new factors related to diagnosis, treatment and prognosis are constantly being discovered. Researchers combine different factorsto form a multigene panel testing, guiding clinicians' decision-making. The application scope of multigene panel detection is constantly expanding. At present, it has been tried in the prognosis evaluation of lymph node-positive and human epidermal growth factor receptor 2-positive breast cancer patients and the early screening of breast cancer. With continuous technological advancement, there will be broader application prospects in the future. The current narrative review was planned to evaluate the recent advances in applying multigene panel testing in breast cancer cases.}, }
@article {pmid37816342, year = {2023}, author = {Wei, Y and Wang, X and Luo, R and Mai, X and Li, S and Meng, J}, title = {Decoding movement frequencies and limbs based on steady-state movement-related rhythms from noninvasive EEG.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad01de}, pmid = {37816342}, issn = {1741-2552}, abstract = {OBJECTIVE: Decoding different types of movements noninvasively from electroencephalography (EEG) is an essential topic in neural engineering, especially in brain-computer interface (BCI). Although the widely used sensorimotor rhythm (SMR) is efficient in limb decoding, it lacks efficacy in decoding movement frequencies. Accumulating evidence supports the notion that the movement frequency is encoded in the steady-state movement-related rhythm (SSMRR). Our study has two primary objectives: firstly, to investigate the spatial-spectral representation of SSMRR in EEG during voluntary movements; secondly, to assess whether movement frequencies and limbs can be effectively decoded based on SSMRR.<br><br>APPROACH: To comprehensively examine the representation of SSMRR, we investigated the frequency characteristics and spatial patterns associated with various rhythmic finger movements. Coherence analysis was performed between the sensor or source domain EEG and finger movements recorded by data gloves. A fusion model based on spectral SNR features and FBCSP features was utilized to decode movement frequencies and limbs.<br><br>MAIN RESULTS: At the group-level, sensor domain, and source domain coherence maps demonstrated that the accurate movement frequency (f_0) and its first harmonic (f_1) were encoded in the contralateral motor cortex. For the four-class classification, including two movement frequencies for both hands, the decoding accuracies for externally paced and internally paced movements were 73.14% &#xb1; 15.86% and 66.30% &#xb1; 17.26% (averaged across ten subjects, chance levels at 31.05% and 30.96%). Notably, the average results of five subjects with the highest decoding accuracies reached 87.21% &#xb1; 7.44% and 80.44% &#xb1; 7.99%.<br><br>SIGNIFICANCE: Our results verified the EEG representation of SSMRR and proved that the movement frequency and limb could be effectively decoded based on spatial-spectral features extracted from SSMRR. We suggest that SSMRR can serve as a complement to SMR to expand the range of decodable movement types and the approaches of limb decoding.&#xD.}, }
@article {pmid37815970, year = {2023}, author = {Zhi, H and Yu, Z and Yu, T and Gu, Z and Yang, J}, title = {A Multi-Domain Convolutional Neural Network for EEG-Based Motor Imagery Decoding.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3323325}, pmid = {37815970}, issn = {1558-0210}, abstract = {Motor imagery (MI) decoding plays a crucial role in the advancement of electroencephalography (EEG)-based brain-computer interface (BCI) technology. Currently, most researches focus on complex deep learning structures for MI decoding. The growing complexity of networks may result in overfitting and lead to inaccurate decoding outcomes due to the redundant information. To address this limitation and make full use of the multi-domain EEG features, a multi-domain temporal-spatial-frequency convolutional neural network (TSFCNet) is proposed for MI decoding. The proposed network provides a novel mechanism that utilize the spatial and temporal EEG features combined with frequency and time-frequency characteristics. This network enables powerful feature extraction without complicated network structure. Specifically, the TSFCNet first employs the MixConv-Residual block to extract multiscale temporal features from multi-band filtered EEG data. Next, the temporal-spatial-frequency convolution block implements three shallow, parallel and independent convolutional operations in spatial, frequency and time-frequency domain, and captures high discriminative representations from these domains respectively. Finally, these features are effectively aggregated by average pooling layers and variance layers, and the network is trained with the joint supervision of the cross-entropy and the center loss. Our experimental results show that the TSFCNet outperforms the state-of-the-art models with superior classification accuracy and kappa values (82.72% and 0.7695 for dataset BCI competition IV 2a, 86.39% and 0.7324 for dataset BCI competition IV 2b). These competitive results demonstrate that the proposed network is promising for enhancing the decoding performance of MI BCIs.}, }
@article {pmid37815969, year = {2023}, author = {Bi, J and Chu, M}, title = {TDLNet: Transfer Data Learning Network for Cross-Subject Classification Based on Multiclass Upper Limb Motor Imagery EEG.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {31}, number = {}, pages = {3958-3967}, doi = {10.1109/TNSRE.2023.3323509}, pmid = {37815969}, issn = {1558-0210}, abstract = {The limited number of brain-computer interface based on motor imagery (MI-BCI) instruction sets for different movements of single limbs makes it difficult to meet practical application requirements. Therefore, designing a single-limb, multi-category motor imagery (MI) paradigm and effectively decoding it is one of the important research directions in the future development of MI-BCI. Furthermore, one of the major challenges in MI-BCI is the difficulty of classifying brain activity across different individuals. In this article, the transfer data learning network (TDLNet) is proposed to achieve the cross-subject intention recognition for multiclass upper limb motor imagery. In TDLNet, the Transfer Data Module (TDM) is used to process cross-subject electroencephalogram (EEG) signals in groups and then fuse cross-subject channel features through two one-dimensional convolutions. The Residual Attention Mechanism Module (RAMM) assigns weights to each EEG signal channel and dynamically focuses on the EEG signal channels most relevant to a specific task. Additionally, a feature visualization algorithm based on occlusion signal frequency is proposed to qualitatively analyze the proposed TDLNet. The experimental results show that TDLNet achieves the best classification results on two datasets compared to CNN-based reference methods and transfer learning method. In the 6-class scenario, TDLNet obtained an accuracy of 65%±0.05 on the UML6 dataset and 63%±0.06 on the GRAZ dataset. The visualization results demonstrate that the proposed framework can produce distinct classifier patterns for multiple categories of upper limb motor imagery through signals of different frequencies. The ULM6 dataset is available at https://dx.doi.org/10.21227/8qw6-f578.}, }
@article {pmid37815966, year = {2023}, author = {Mai, X and Ai, J and Wei, Y and Zhu, X and Meng, J}, title = {Phase-locked Time-shift Data Augmentation Method for SSVEP Brain-Computer Interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3323351}, pmid = {37815966}, issn = {1558-0210}, abstract = {Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) have achieved an information transfer rate (ITR) of over 300 bits/min, but abundant training data is required. The performance of SSVEP algorithms deteriorates greatly under limited data, and the existing time-shift data augmentation method fails to improve it because the phase-locked requirement between training samples is violated. To address this issue, this study proposes a novel augmentation method, namely phase-locked time-shift (PLTS), for SSVEP-BCI. The similarity between epochs at different time moments was evaluated, and a unique time-shift step was calculated for each class to augment additional data epochs in each trial. The results showed that the PLTS significantly improved the classification performance of SSVEP algorithms on the BETA SSVEP datasets. Moreover, under the condition of one calibration block, by slightly prolonging the calibration duration (from 48 s to 51.5 s), the ITR increased from 40.88 ±4.54 bits/min to 122.61 ± 7.05 bits/min with the PLTS. This study provides a new perspective on augmenting data epochs for training-based SSVEP-BCI, promotes the classification accuracy and ITR under limited training data, and thus facilitates the real-life applications of SSVEP-based brain spellers.}, }
@article {pmid37812934, year = {2023}, author = {Tan, J and Zhang, X and Wu, S and Song, Z and Chen, S and Huang, Y and Wang, Y}, title = {Audio-induced medial prefrontal cortical dynamics enhances coadaptive learning in brain-machine interfaces.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ad017d}, pmid = {37812934}, issn = {1741-2552}, abstract = {OBJECTIVES: Coadaptive brain-machine interfaces (BMIs) allow subjects and external devices to adapt to each other during the closed-loop control, which provides a promising solution for paralyzed individuals. Previous studies have focused on either improving sensory feedback to facilitate subject learning or developing adaptive algorithms to maintain stable decoder performance. In this work, we aim to design an efficient coadaptive BMI framework which not only facilitates the learning of subjects on new tasks with designed sensory feedback, but also improves decoders' learning ability by extracting sensory feedback-induced evaluation information.<br><br>APPROACH: We designed audio feedback during the trial according to the subjects' performance when they were trained to learn a new behavioral task. We compared the learning performance of two groups of Sprague Dawley rats, one with and the other without audio feedback to show whether this audio feedback could facilitate the subjects' learning. Compared with the traditional closed-loop in BMI systems, an additional closed-loop involving medial prefrontal cortex (mPFC) activity was introduced into the coadaptive framework. The neural dynamics of audio-induced mPFC activity was analyzed to investigate whether a significant neural response could be triggered. This audio-induced response was then translated into reward expectation information to guide the learning of decoders on a new task . The multiday decoding performance of the decoders with and without audio-induced reward expecation was compared to investigate whether the extracted information could accelerate decoders to learn a new task.<br><br>MAIN RESULTS: The behavior performance comparison showed that the average days for rats to achieve 80% well-trained behavioral performance was improved by 26.4% after introducing the designed audio feedback sequence. The analysis of neural dynamics showed that a significant neural response of mPFC activity could be elicited by the audio feedback and the visualization of audio-induced neural patterns was emerged and accompanied by the behavioral improvement of subjects. The multiday decoding performance comparison showed that the decoder taking the reward expectation information could achieve faster task learning by 33.8% on average across subjects.<br><br>SIGNIFICANCE: This study demonstrates that the designed audio feedback could improve the learning of subjects and the mPFC activity induced by audio feedback can be utilized to improve the decoder's learning efficiency on new tasks. The coadaptive framework involving mPFC dynamics in the closed-loop interaction can advance the BMIs into a more adaptive and efficient system with learning ability on new tasks.}, }
@article {pmid37812554, year = {2023}, author = {Chen, W and Liang, W and Liu, X and Lu, Z and Wan, P and Chen, Z}, title = {A Low Noise Neural Recording Frontend IC With Power Management for Closed-Loop Brain-Machine Interface Application.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBCAS.2023.3321297}, pmid = {37812554}, issn = {1940-9990}, abstract = {Brain-machine Interface (BMI) with implantable bioelectronics systems can provide an alternative way to cure neural diseases, while a power management system plays an important role in providing a stable voltage supply for the implanted chip. a prototype system of power management integrated circuit (PMIC) with heavy load capability supplying artifacts tolerable neural recording integrated circuit (ATNR-IC) is presented in this work. A reverse nested miller compensation (RNMC) low dropout regulator (LDO) with a transient enhancer is proposed for the PMIC. The power consumption is 0.55 mW and 22.5 mW at standby (SB) and full stimulation (ST) load, respectively. For a full load transition, the overshoot and downshoot of the LDO are 110 mV and 71 mV, respectively, which help improve the load transient response during neural stimulation. With the load current peak-to-peak range is about 560 μA supplied by a 4-channel stimulator, the whole PMIC can output a stable 3.3 V supply voltage, which indicates that this PMIC can be extended for more stimulating channels' scenarios. When the ATNR-IC is supplied for presented PMIC through a voltage divider network, it can amplify the signal consisting of 1 mVpp simulated neural signal and 20 mVpp simulated artifact by 28 dB with no saturation.}, }
@article {pmid37811702, year = {2023}, author = {Kim, H and Park, MK and Park, SN and Cho, HH and Choi, JY and Lee, CK and Lee, IW and Moon, IJ and Jung, JY and Jung, J and Lee, KY and Oh, JH and Park, HJ and Seo, JH and Song, JJ and Ha, J and Jang, JH and Choung, YH}, title = {Efficacy of the Bonebridge BCI602 for Adult Patients with Single-sided Deafness: A Prospective Multicenter Study.}, journal = {Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery}, volume = {}, number = {}, pages = {}, doi = {10.1002/ohn.520}, pmid = {37811702}, issn = {1097-6817}, abstract = {OBJECTIVE: To investigate the safety and efficacy of a novel active transcutaneous bone conduction implant (BCI) device for patients with single-sided deafness (SSD).<br><br>STUDY DESIGN: Prospective cohort study.<br><br>SETTING: Tertiary referral hospitals.<br><br>METHODS: This prospective multicenter study was conducted at 15 institutions nationwide. Thirty adult (aged &#x2265;19 years) SSD patients were recruited. They underwent implantation of an active transcutaneous BCI device (Bonebridge BCI602). Objective outcomes included aided pure-tone thresholds, aided speech discrimination scores (SDSs), and the Hearing in Noise Test (HINT) and sound localization test results. The Bern Benefit in Single-Sided Deafness (BBSS) questionnaire, the Abbreviated Profile of Hearing Aid Benefit (APHAB) questionnaire, and the Tinnitus Handicap Inventory (THI) were used to measure subjective benefits.<br><br>RESULTS: The mean aided pure-tone threshold was 34.2 (11.3), mean (SD), dB HL at 500 to 4000&#x2009;Hz. The mean total BBSS score was 27.5 (13.8). All APHAB questionnaire domain scores showed significant improvements: ease of communication, 33.6 (23.2) versus 22.6 (21.3), P&#x2009;=&#x2009;.025; reverberation, 44.8 (16.6) versus 32.8 (15.9), P&#x2009;=&#x2009;.002; background noise, 55.5 (23.6) versus 35.2 (18.1), P&#x2009;<&#x2009;.001; and aversiveness, 36.7 (22.8) versus 25.8 (21.4), P&#x2009;=&#x2009;.028. Moreover, the THI scores were significantly reduced [47.4 (30.1) versus 31.1 (27.0), P&#x2009;=&#x2009;.003]. Congenital SSD was a significant factor of subjective benefit (-11.643; 95% confidence interval: -21.946 to -1.340).<br><br>CONCLUSION: The BCI602 active transcutaneous BCI device can provide functional hearing gain without any adverse effects and is a feasible option for acquired SSD patients with long-term deafness.}, }
@article {pmid37810762, year = {2023}, author = {Friedrich, EVC and Neuper, C and Scherer, R}, title = {Editorial: Mind over brain, brain over mind: cognitive causes and consequences of controlling brain activity - volume II.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1280095}, pmid = {37810762}, issn = {1662-5161}, }
@article {pmid37809054, year = {2022}, author = {Ramírez-Moreno, MA and Cruz-Garza, JG and Acharya, A and Chatufale, G and Witt, W and Gelok, D and Reza, G and Contreras-Vidal, JL}, title = {Brain-to-brain communication during musical improvisation: a performance case study.}, journal = {F1000Research}, volume = {11}, number = {}, pages = {989}, pmid = {37809054}, issn = {2046-1402}, abstract = {Understanding and predicting others' actions in ecological settings is an important research goal in social neuroscience. Here, we deployed a mobile brain-body imaging (MoBI) methodology to analyze inter-brain communication between professional musicians during a live jazz performance. Specifically, bispectral analysis was conducted to assess the synchronization of scalp electroencephalographic (EEG) signals from three expert musicians during a three-part 45 minute jazz performance, during which a new musician joined every five minutes. The bispectrum was estimated for all musician dyads, electrode combinations, and five frequency bands. The results showed higher bispectrum in the beta and gamma frequency bands (13-50 Hz) when more musicians performed together, and when they played a musical phrase synchronously. Positive bispectrum amplitude changes were found approximately three seconds prior to the identified synchronized performance events suggesting preparatory cortical activity predictive of concerted behavioral action. Moreover, a higher amount of synchronized EEG activity, across electrode regions, was observed as more musicians performed, with inter-brain synchronization between the temporal, parietal, and occipital regions the most frequent. Increased synchrony between the musicians' brain activity reflects shared multi-sensory processing and movement intention in a musical improvisation task.}, }
@article {pmid37808091, year = {2023}, author = {Liang, R and Zhang, X and Li, Q and Wei, L and Liu, H and Kumar, A and Leadingham, KMK and Punnoose, J and Garcia, LP and Manbachi, A}, title = {Unidirectional brain-computer interface: Artificial neural network encoding natural images to fMRI response in the visual cortex.}, journal = {ArXiv}, volume = {}, number = {}, pages = {}, pmid = {37808091}, issn = {2331-8422}, abstract = {While significant advancements in artificial intelligence (AI) have catalyzed progress across various domains, its full potential in understanding visual perception remains underexplored. We propose an artificial neural network dubbed VISION, an acronym for "Visual Interface System for Imaging Output of Neural activity," to mimic the human brain and show how it can foster neuroscientific inquiries. Using visual and contextual inputs, this multimodal model predicts the brain's functional magnetic resonance imaging (fMRI) scan response to natural images. VISION successfully predicts human hemodynamic responses as fMRI voxel values to visual inputs with an accuracy exceeding state-of-the-art performance by 45%. We further probe the trained networks to reveal representational biases in different visual areas, generate experimentally testable hypotheses, and formulate an interpretable metric to associate these hypotheses with cortical functions. With both a model and evaluation metric, the cost and time burdens associated with designing and implementing functional analysis on the visual cortex could be reduced. Our work suggests that the evolution of computational models may shed light on our fundamental understanding of the visual cortex and provide a viable approach toward reliable brain-machine interfaces.}, }
@article {pmid37807372, year = {2023}, author = {Kim, B and Erickson, BA and Fernandez-Nuñez, G and Medaglia, JD and Mentzelopoulos, G and Rich, RR and Vitale, F}, title = {A - 175 EEG Phase Can be Predicted with Similar Accuracy across Cognitive States after Accounting for Power and SNR.}, journal = {Archives of clinical neuropsychology : the official journal of the National Academy of Neuropsychologists}, volume = {}, number = {}, pages = {}, doi = {10.1093/arclin/acad067.192}, pmid = {37807372}, issn = {1873-5843}, abstract = {OBJECTIVE: Electroencephalogram (EEG) phase can capture neural oscillation dynamics. Accordingly, EEG phase is a potential target for neurofeedback and brain-computer interfaces (BCIs). However, rigorous tests of the generalizability of real-time EEG phase are needed, requiring accurate phase estimates. We examined how cognitive states affect EEG phase prediction accuracy in the parieto-occipital alpha band.<br><br>DATA SELECTION: We identified datasets through public repositories. After preprocessing, we used the Educated Temporal Prediction Algorithm (ETP) to predict future peaks. We compared these predictions to the original signal, and defined accuracy from 0 to 100%. Our independent variable was cognitive state (eyes-open rest, eyes-closed rest, task), dependent variable was accuracy, and covariates were EEG instantaneous power and signal-to-noise ratio (SNR). We used a linear mixed-effects model, with random effects for dataset and individual.<br><br>DATA SYNTHESIS: Across the 11 datasets, we had 543 participants and 1,641,074 predictions. There were no significant accuracy differences among the conditions (p&#x2009;<&#x2009;1e-5), with a baseline accuracy of 59.07%. Power had an effect on accuracy, with a unit increase leading to a 13.1% increase in accuracy (p&#x2009;<&#x2009;1e-5). SNR had an effect on accuracy, with an effect size of 0.46% (p&#x2009;<&#x2009;1e-5), with a significant negative interaction with power with an effect size of -0.51% (p&#x2009;<&#x2009;1e-5).<br><br>CONCLUSION: Our results indicated that we could predict EEG phase accurately across cognitive conditions and datasets, with higher accuracy for high instantaneous band-power and SNR. Accordingly, real-time EEG phase experiments, closed-loop technologies, and BCIs should minimize external unwanted noise while targeting periods of high power, as opposed to manipulating experimental and cognitive conditions.}, }
@article {pmid37806513, year = {2023}, author = {Chen, J and Wang, T and Zhou, Y and Hong, Y and Zhang, S and Zhou, Z and Jiang, A and Liu, D}, title = {Microglia trigger the structural plasticity of GABAergic neurons in the hippocampal CA1 region of a lipopolysaccharide-induced neuroinflammation model.}, journal = {Experimental neurology}, volume = {}, number = {}, pages = {114565}, doi = {10.1016/j.expneurol.2023.114565}, pmid = {37806513}, issn = {1090-2430}, abstract = {It is well-established that microglia-mediated neuroinflammatory response involves numerous neuropsychiatric and neurodegenerative diseases. While the role of microglia in excitatory synaptic transmission has been widely investigated, the impact of innate immunity on the structural plasticity of GABAergic inhibitory synapses is not well understood. To investigate this, we established an inflammation model using lipopolysaccharide (LPS) and observed a prolonged microglial response in the hippocampal CA1 region of mice, which was associated with cognitive deficits in the open field test, Y-maze test, and novel object recognition test. Furthermore, we found an increased abundance of GABAergic interneurons and GABAergic synapse formation in the hippocampal CA1 region. The cognitive impairment caused by LPS injection could be reversed by blocking GABA receptor activity with (-)-Bicuculline methiodide. These findings suggest that the upregulation of GABAergic synapses induced by LPS-mediated microglial activation leads to cognitive dysfunction. Additionally, the depletion of microglia by PLX3397 resulted in a decrease in GABAergic interneurons and GABAergic inhibitory synapses, which blocked the cognitive decline induced by LPS. In conclusion, our findings indicate that excessive reinforcement of GABAergic inhibitory synapse formation via microglial activation contributes to LPS-induced cognitive impairment.}, }
@article {pmid37805540, year = {2023}, author = {Wirth, C and Toth, J and Arvaneh, M}, title = {Bayesian learning from multi-way EEG feedback for robot navigation and target identification.}, journal = {Scientific reports}, volume = {13}, number = {1}, pages = {16925}, pmid = {37805540}, issn = {2045-2322}, abstract = {Many brain-computer interfaces require a high mental workload. Recent research has shown that this could be greatly alleviated through machine learning, inferring user intentions via reactive brain responses. These signals are generated spontaneously while users merely observe assistive robots performing tasks. Using reactive brain signals, existing studies have addressed robot navigation tasks with a very limited number of potential target locations. Moreover, they use only binary, error-vs-correct classification of robot actions, leaving more detailed information unutilised. In this study a virtual robot had to navigate towards, and identify, target locations in both small and large grids, wherein any location could be the target. For the first time, we apply a system utilising detailed EEG information: 4-way classification of movements is performed, including specific information regarding when the target is reached. Additionally, we classify whether targets are correctly identified. Our proposed Bayesian strategy infers the most likely target location from the brain's responses. The experimental results show that our novel use of detailed information facilitates a more efficient and robust system than the state-of-the-art. Furthermore, unlike state-of-the-art approaches, we show scalability of our proposed approach: By tuning parameters appropriately, our strategy correctly identifies 98% of targets, even in large search spaces.}, }
@article {pmid37805019, year = {2023}, author = {Csaky, R and van Es, MWJ and Jones, OP and Woolrich, M}, title = {Interpretable many-class decoding for MEG.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {120396}, doi = {10.1016/j.neuroimage.2023.120396}, pmid = {37805019}, issn = {1095-9572}, abstract = {Multivariate pattern analysis (MVPA) of Magnetoencephalography (MEG) and Electroencephalography (EEG) data is a valuable tool for understanding how the brain represents and discriminates between different stimuli. Identifying the spatial and temporal signatures of stimuli is typically a crucial output of these analyses. Such analyses are mainly performed using linear, pairwise, sliding window decoding models. These allow for relative ease of interpretation, e.g. by estimating a time-course of decoding accuracy, but have limited decoding performance. On the other hand, full epoch multiclass decoding models, commonly used for brain-computer interface (BCI) applications, can provide better decoding performance. However interpretation methods for such models have been designed with a low number of classes in mind. In this paper, we propose an approach that combines a multiclass, full epoch decoding model with supervised dimensionality reduction, while still being able to reveal the contributions of spatiotemporal and spectral features using permutation feature importance. Crucially, we introduce a way of doing supervised dimensionality reduction of input features within a neural network optimized for the classification task, improving performance substantially. We demonstrate the approach on 3 different many-class task-MEG datasets using image presentations. Our results demonstrate that this approach consistently achieves higher accuracy than the peak accuracy of a sliding window decoder while estimating the relevant spatiotemporal features in the MEG signal.}, }
@article {pmid37804238, year = {2023}, author = {Ni, G and Xu, Z and Bai, Y and Zheng, Q and Zhao, R and Wu, Y and Ming, D}, title = {EEG-based assessment of temporal fine structure and envelope effect in mandarin syllable and tone perception.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {}, number = {}, pages = {}, doi = {10.1093/cercor/bhad366}, pmid = {37804238}, issn = {1460-2199}, support = {2022YFF1202400//Key Technologies Research and Development Program of China/ ; 81971698//National Natural Science Foundation of China/ ; }, abstract = {In recent years, speech perception research has benefited from low-frequency rhythm entrainment tracking of the speech envelope. However, speech perception is still controversial regarding the role of speech envelope and temporal fine structure, especially in Mandarin. This study aimed to discuss the dependence of Mandarin syllables and tones perception on the speech envelope and the temporal fine structure. We recorded the electroencephalogram (EEG) of the subjects under three acoustic conditions using the sound chimerism analysis, including (i) the original speech, (ii) the speech envelope and the sinusoidal modulation, and (iii) the fine structure of time and the modulation of the non-speech (white noise) sound envelope. We found that syllable perception mainly depended on the speech envelope, while tone perception depended on the temporal fine structure. The delta bands were prominent, and the parietal and prefrontal lobes were the main activated brain areas, regardless of whether syllable or tone perception was involved. Finally, we decoded the spatiotemporal features of Mandarin perception from the microstate sequence. The spatiotemporal feature sequence of the EEG caused by speech material was found to be specific, suggesting a new perspective for the subsequent auditory brain-computer interface. These results provided a new scheme for the coding strategy of new hearing aids for native Mandarin speakers.}, }
@article {pmid37803622, year = {2023}, author = {Yang, T and Zhang, P and Xing, L and Hu, J and Feng, R and Zhong, J and Li, W and Zhang, Y and Zhu, Q and Yang, Y and Gao, F and Qian, Z}, title = {Insights into brain perceptions of the different taste qualities and hedonic valence of food via scalp electroencephalogram.}, journal = {Food research international (Ottawa, Ont.)}, volume = {173}, number = {Pt 1}, pages = {113311}, doi = {10.1016/j.foodres.2023.113311}, pmid = {37803622}, issn = {1873-7145}, abstract = {Investigating brain activity is essential for exploring taste-experience related cues. The paper aimed to explore implicit (unconscious) emotional or physiological responses related to taste experiences using scalp electroencephalogram (EEG). We performed implicit measures of tastants of differing perceptual types (bitter, salty, sour and sweet) and intensities (low, medium, and high). The results showed that subjects were partially sensitive to different sensory intensities, i.e., for high intensities, taste stimuli could induce activation of different rhythm signals in the brain, with α and θ bands possibly being more sensitive to different taste types. Furthermore, the neural representations and corresponding sensory qualities (e.g., "sweet: pleasant" or "bitter: unpleasant") of different tastes could be discriminated at 250-1,500 ms after stimulus onset, and different tastes exhibited distinct temporal dynamic differences. Source localization indicated that different taste types activate brain areas associated with emotional eating, reward processing, and motivated tendencies, etc. Overall, our findings reveal a larger sophisticated taste map that accounted for the diversity of taste types in the human brain and assesses the emotion, reward, and motivated behavior represented by different tastes. This study provided basic insights and a perceptual foundation for the relationship between taste experience-related decisions and the prediction of brain activity.}, }
@article {pmid37803140, year = {2023}, author = {Xu, LL and Xie, JQ and Shen, JJ and Ying, MD and Chen, XZ}, title = {Neuron-derived exosomes mediate sevoflurane-induced neurotoxicity in neonatal mice via transferring lncRNA Gas5 and promoting M1 polarization of microglia.}, journal = {Acta pharmacologica Sinica}, volume = {}, number = {}, pages = {}, pmid = {37803140}, issn = {1745-7254}, abstract = {Sevoflurane exposure during rapid brain development induces neuronal apoptosis and causes memory and cognitive deficits in neonatal mice. Exosomes that transfer genetic materials including long non-coding RNAs (lncRNAs) between cells play a critical role in intercellular communication. However, the lncRNAs found in exosomes derived from neurons treated with sevoflurane and their potential role in promoting neurotoxicity remain unknown. In this study, we investigated the role of cross-talk of newborn mouse neurons with microglial cells in sevoflurane-induced neurotoxicity. Mouse hippocampal neuronal HT22 cells were exposed to sevoflurane, and then co-cultured with BV2 microglial cells. We showed that sevoflurane treatment markedly increased the expression of the lncRNA growth arrest-specific 5 (Gas5) in neuron-derived extracellular vesicles, which inhibited neuronal proliferation and induced neuronal apoptosis by promoting M1 polarization of microglia and the release of inflammatory cytokines. We further revealed that the exosomal lncRNA Gas5 significantly upregulated Foxo3 as a competitive endogenous RNA of miR-212-3p in BV2 cells, and activated the NF-κB pathway to promote M1 microglial polarization and the secretion of inflammatory cytokines, thereby exacerbating neuronal damage. In neonatal mice, intracranial injection of the exosomes derived from sevoflurane-treated neurons into the bilateral hippocampi significantly increased the proportion of M1 microglia, inhibited neuronal proliferation and promoted apoptosis, ultimately leading to neurotoxicity. Similar results were observed in vitro in BV2 cells treated with the CM from HT22 cells after sevoflurane exposure. We conclude that sevoflurane induces the transfer of lncRNA Gas5-containing exosomes from neurons, which in turn regulates the M1 polarization of microglia and contributes to neurotoxicity. Thus, modulating the expression of lncRNA Gas5 or the secretion of exosomes could be a strategy for addressing sevoflurane-induced neurotoxicity.}, }
@article {pmid37799298, year = {2023}, author = {Penev, YP and Beneke, A and Root, KT and Meisel, E and Kwak, S and Diaz, MJ and Root, JL and Hosseini, MR and Lucke-Wold, B}, title = {Therapeutic Effectiveness of Brain Computer Interfaces in Stroke Patients: A Systematic Review.}, journal = {Journal of experimental neurology}, volume = {4}, number = {3}, pages = {87-93}, pmid = {37799298}, issn = {2692-2819}, abstract = {BACKGROUND: Brain-computer interfaces (BCIs) are a rapidly advancing field which utilizes brain activity to control external devices for a myriad of functions, including the restoration of motor function. Clinically, BCIs have been especially impactful in patients who suffer from stroke-mediated damage. However, due to the rapid advancement in the field, there is a lack of accepted standards of practice. Therefore, the aim of this systematic review is to summarize the current literature published regarding the efficacy of BCI-based rehabilitation of motor dysfunction in stroke patients.<br><br>METHODOLOGY: This systematic review was performed in accordance with the guidelines set forth by the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 statement. PubMed, Embase, and Cochrane Library were queried for relevant articles and screened for inclusion criteria by two authors. All discrepancies were resolved by discussion among both reviewers and subsequent consensus.<br><br>RESULTS: 11/12 (91.6%) of studies focused on upper extremity outcomes and reported larger initial improvements for participants in the treatment arm (using BCI) as compared to those in the control arm (no BCI). 2/2 studies focused on lower extremity outcomes reported improvements for the treatment arm compared to the control arm.<br><br>DISCUSSION/CONCLUSION: This systematic review illustrates the utility BCI has for the restoration of upper extremity and lower extremity motor function in stroke patients and supports further investigation of BCI for other clinical indications.}, }
@article {pmid37798868, year = {2023}, author = {Yang, Y and Stewart, T and Zhang, C and Wang, P and Xu, Z and Jin, J and Huang, Y and Liu, Z and Lan, G and Liang, X and Sheng, L and Shi, M and Cai, Z and Zhang, J}, title = {Erythrocytic α-Synuclein and the Gut Microbiome: Kindling of the Gut-Brain Axis in Parkinson's Disease.}, journal = {Movement disorders : official journal of the Movement Disorder Society}, volume = {}, number = {}, pages = {}, doi = {10.1002/mds.29620}, pmid = {37798868}, issn = {1531-8257}, support = {82020108012//National Natural Science Foundation of China/ ; 81571226//National Natural Science Foundation of China/ ; 81671187//National Natural Science Foundation of China/ ; LZ23H090002//Natural Science Foundation of Zhejiang Province/ ; 2020R01001//Leading Innovation and Entrepreneurship Team of Zhejiang Province/ ; //Innovative Institute of Basic Medical Science of Zhejiang University/ ; //Nanhu Brain-computer Interface Institute/ ; }, abstract = {BACKGROUND: Progressive spreading of α-synuclein via gut-brain axis has been hypothesized in the pathogenesis of Parkinson's disease (PD). However, the source of seeding-capable α-synuclein in the gastrointestinal tract (GIT) has not been fully investigated. Additionally, the mechanism by which the GIT microbiome contributes to PD pathogenesis remains to be characterized.<br><br>OBJECTIVES: We aimed to investigate whether blood-derived &#x3b1;-synuclein might contribute to PD pathology via a gut-driven pathway and involve GIT microbiota.<br><br>METHODS: The GIT expression of &#x3b1;-synuclein and the transmission of extracellular vesicles (EVs) derived from erythrocytes/red blood cells (RBCs), with their cargo &#x3b1;-synuclein, to the GIT were explored with various methods, including radioactive labeling of RBC-EVs and direct analysis of the transfer of &#x3b1;-synuclein protein. The potential role of microbiota on the EVs transmission was further investigated by administering butyrate, the short-chain fatty acids produced by gut microbiota and studying mice with different &#x3b1;-synuclein genotypes.<br><br>RESULTS: This study demonstrated that RBC-EVs can effectively transport &#x3b1;-synuclein to the GIT in a region-dependent manner, along with variations closely associated with regional differences in the expression of gut-vascular barrier markers. The investigation further revealed that the infiltration of &#x3b1;-synuclein into the GIT was influenced significantly by butyrate and &#x3b1;-synuclein genotypes, which may also affect the GIT microbiome directly.<br><br>CONCLUSION: By demonstrating the transportation of &#x3b1;-synuclein through RBC-EVs to the GIT, and its potential association with gut-vascular barrier markers and gut microbiome, this work highlights a potential mechanism by which RBC &#x3b1;-synuclein may impact PD initiation and/or progression. &#xa9; 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.}, }
@article {pmid37794039, year = {2023}, author = {Chen, P and Liu, F and Lin, P and Li, P and Xiao, Y and Zhang, B and Pan, G}, title = {Open-loop analog programmable electrochemical memory array.}, journal = {Nature communications}, volume = {14}, number = {1}, pages = {6184}, pmid = {37794039}, issn = {2041-1723}, support = {61925603//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {Emerging memories have been developed as new physical infrastructures for hosting neural networks owing to their low-power analog computing characteristics. However, accurately and efficiently programming devices in an analog-valued array is still largely limited by the intrinsic physical non-idealities of the devices, thus hampering their applications in in-situ training of neural networks. Here, we demonstrate a passive electrochemical memory (ECRAM) array with many important characteristics necessary for accurate analog programming. Different image patterns can be open-loop and serially programmed into our ECRAM array, achieving high programming accuracies without any feedback adjustments. The excellent open-loop analog programmability has led us to in-situ train a bilayer neural network and reached software-like classification accuracy of 99.4% to detect poisonous mushrooms. The training capability is further studied in simulation for large-scale neural networks such as VGG-8. Our results present a new solution for implementing learning functions in an artificial intelligence hardware using emerging memories.}, }
@article {pmid37792662, year = {2023}, author = {Cui, H and Chi, X and Wang, L and Chen, X}, title = {A High-Rate Hybrid BCI System Based on High-Frequency SSVEP and sEMG.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2023.3321722}, pmid = {37792662}, issn = {2168-2208}, abstract = {Recently, various biosignals have been combined with electroencephalography (EEG) to build hybrid brain-computer interface (BCI) systems to improve system performance. Since steady-state visual evoked potential (SSVEP) and surface electromyography (sEMG) are easy-to-use, non-invasive techniques, and have high signal-to-noise ratio (SNR), hybrid BCI systems combining SSVEP and sEMG have received much attention in the BCI literature. However, most existing studies regarding hybrid BCIs based on SSVEP and sEMG adopt low-frequency visual stimuli to induce SSVEPs. The comfort of these systems needs further improvement to meet the practical application requirements. The present study realized a novel hybrid BCI combining high-frequency SSVEP and sEMG signals for spelling applications. EEG and sEMG were obtained simultaneously from the scalp and skin surface of subjects, respectively. These two types of signals were analyzed independently and then combined to determine the target stimulus. Our online results demonstrated that the developed hybrid BCI yielded a mean accuracy of 88.07 ± 1.43% and ITR of 159.12 ± 4.31 bits/min. These results exhibited the feasibility and effectiveness of fusing high-frequency SSVEP and sEMG towards improving the total BCI system performance.}, }
@article {pmid37792658, year = {2023}, author = {Chen, X and An, J and Wu, H and Li, S and Liu, B and Wu, D}, title = {Front-end Replication Dynamic Window (FRDW) for Online Motor Imagery Classification.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3321640}, pmid = {37792658}, issn = {1558-0210}, abstract = {Motor imagery (MI) is a classical paradigm in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Online accurate and fast decoding is very important to its successful applications. This paper proposes a simple yet effective front-end replication dynamic window (FRDW) algorithm for this purpose. Dynamic windows enable the classification based on a test EEG trial shorter than those used in training, improving the decision speed; front-end replication fills a short test EEG trial to the length used in training, improving the classification accuracy. Within-subject and cross-subject online MI classification experiments on three public datasets, with three different classifiers and three different data augmentation approaches, demonstrated that FRDW can significantly increase the information transfer rate in MI decoding. Additionally, FR can also be used in training data augmentation. FRDW helped win national champion of the China BCI Competition in 2022.}, }
@article {pmid37792657, year = {2023}, author = {Zhang, X and Chen, S and Wang, Y}, title = {Kernel Reinforcement Learning-assisted Adaptive Decoder Facilitates Stable and Continuous Brain Control Tasks.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3321756}, pmid = {37792657}, issn = {1558-0210}, abstract = {Brain-Machine Interfaces (BMIs) assist paralyzed people to brain control (BC) the neuro-prosthesis continuously moving in space. During the BC process, the subject imagines the movement of the real limb and adapts the brain activity according to the sensory feedback. The neural adaptation in the closed-loop control results in complex and changing brain signals. Simultaneously, the decoder interprets the time-varying functional mapping between neural activity and continuous trajectory. It is crucial and challenging to accurately and adaptively track the mapping to help the subject accomplish the BC task with a stable performance. Existing Kalman Filter (KF) based decoders achieve continuous trajectory control by linearly interpreting neural firing observations into self-evolving prosthetic states. However, the linear neural-state mapping might not accurately reflect the movement intention of the subject. In this paper, we propose a novel method that allows subjects to achieve continuous brain control efficiently and stably. The proposed method incorporates a kernel reinforcement learning method into a state-observation model to decode the nonlinearly neural observation into a continuous trajectory state. The state transition function ensures the continuity of the prosthetic state. The kernel reinforcement learning allows the quick adaptation of the nonlinear neural-movement mapping during the BC process. The proposed method is tested in an online brain control reaching task for rats. Compared with KF, our method achieved more successful trials, faster response time, shorter inter-trial time, and remained stable over days. These results demonstrate that the proposed method is an efficient tool to assist subjects in brain control tasks.}, }
@article {pmid37792654, year = {2023}, author = {Ma, G and Kang, J and Thompson, DE and Huggins, JE}, title = {BCI-Utility Metric for Asynchronous P300 Brain-Computer Interface Systems.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3322125}, pmid = {37792654}, issn = {1558-0210}, abstract = {The Brain-Computer Interface (BCI) was envisioned as an assistive technology option for people with severe movement impairments. The traditional synchronous event-related potential (ERP) BCI design uses a fixed communication speed and is vulnerable to variations in attention. Recent ERP BCI designs have added asynchronous features, including abstention and dynamic stopping, but it remains a open question of how to evaluate asynchronous BCI performance. In this work, we build on the BCI-Utility metric to create the first evaluation metric with special consideration of the asynchronous features of self-paced BCIs. This metric considers accuracy as all of the following three - probability of a correct selection when a selection was intended, probability of making a selection when a selection was intended, and probability of an abstention when an abstention was intended. Further, it considers the average time required for a selection when using dynamic stopping and the proportion of intended selections versus abstentions. We establish the validity of the derived metric via extensive simulations, and illustrate and discuss its practical usage on real-world BCI data. We describe the relative contribution of different inputs with plots of BCI-Utility curves under different parameter settings. Generally, the BCI-Utility metric increases as any of the accuracy values increase and decreases as the expected time for an intended selection increases. Furthermore, in many situations, we find shortening the expected time of an intended selection is the most effective way to improve the BCI-Utility, which necessitates the advancement of asynchronous BCI systems capable of accurate abstention and dynamic stopping.}, }
@article {pmid37791058, year = {2023}, author = {Hao, M and Fang, Q and Wu, B and Liu, L and Tang, H and Tian, F and Chen, L and Kong, D and Li, J}, title = {Rehabilitation effect of intelligent rehabilitation training system on hemiplegic limb spasms after stroke.}, journal = {Open life sciences}, volume = {18}, number = {1}, pages = {20220724}, pmid = {37791058}, issn = {2391-5412}, abstract = {This article aimed to explore the rehabilitation efficacy of intelligent rehabilitation training systems in hemiplegic limb spasms after stroke and provided more theoretical basis for the application of intelligent rehabilitation systems in the rehabilitation of hemiplegic limb spasms after stroke. To explore the rehabilitation efficacy of intelligent rehabilitation training system (RTS for short here) in post-stroke hemiplegic limb spasms, this study selected 99 patients with post-stroke hemiplegic limb spasms admitted to a local tertiary hospital from March 2021 to March 2023 as the research subjects. This article used blind selection to randomly divide them into three groups: control group 1, control group 2, and study group, with 33 patients in each group. Control group 1 used a conventional RTS, group 2 used the brain-computer interface RTS from reference 9, and research group used the intelligent RTS from this article. This article compared the degree of spasticity, balance ability score, motor function score, and daily living activity score of three groups of patients after 10 weeks of treatment. After 10 weeks of treatment, the number of patients in the study group with no spasms at level 0 (24) was significantly higher than the number of patients in group 1 (7) and group 2 (10), with a statistically significant difference (P < 0.05); In the comparison of Barthel index scores, after ten weeks of treatment, the total number of people in the study group with scores starting at 71-80 and 81-100 was 23. The total number of people in the score range of 71-80 and 81-100 in group 1 was 5, while in group 2, the total number of people in this score range was 8. The study group scored considerably higher than the control group and the difference was found to be statistically relevant (P < 0.05). In the Berg balance assessment scale and motor function assessment scale, after 10 weeks of treatment, the scores of the study group patients on both scales were significantly higher than those of group 1 and group 2 (P < 0.05). The intelligent RTS is beneficial for promoting the improvement of spasticity in stroke patients with hemiplegic limb spasms, as well as improving their balance ability, motor ability, and daily life activities. Its rehabilitation effect is good.}, }
@article {pmid37790428, year = {2023}, author = {Merk, T and Köhler, R and Peterson, V and Lyra, L and Vanhoecke, J and Chikermane, M and Binns, T and Li, N and Walton, A and Bush, A and Sisterson, N and Busch, J and Lofredi, R and Habets, J and Huebl, J and Zhu, G and Yin, Z and Zhao, B and Merkl, A and Bajbouj, M and Krause, P and Faust, K and Schneider, GH and Horn, A and Zhang, J and Kühn, A and Richardson, RM and Neumann, WJ}, title = {Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants.}, journal = {Research square}, volume = {}, number = {}, pages = {}, pmid = {37790428}, abstract = {Brain computer interfaces (BCI) provide unprecedented spatiotemporal precision that will enable significant expansion in how numerous brain disorders are treated. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for identifying and advancing novel clinical BCI approaches does not exist. Here, we developed a platform that integrates brain signal decoding with connectomics and demonstrate its utility across 123 hours of invasively recorded brain data from 73 neurosurgical patients treated for movement disorders, depression and epilepsy. First, we introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson's disease and epilepsy from the US, Europe and China. Next, we reveal network targets for emotion decoding in left prefrontal and cingulate circuits in DBS patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our platform provides rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neuromodulation therapies in response to the individual needs of patients.}, }
@article {pmid37790422, year = {2023}, author = {Zhang, Y and He, T and Boussard, J and Windolf, C and Winter, O and Trautmann, E and Roth, N and Barrell, H and Churchland, M and Steinmetz, NA and ,  and Varol, E and Hurwitz, C and Paninski, L}, title = {Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {37790422}, support = {/WT_/Wellcome Trust/United Kingdom ; K99 MH128772/MH/NIMH NIH HHS/United States ; U19 NS123716/NS/NINDS NIH HHS/United States ; }, abstract = {Neural decoding and its applications to brain computer interfaces (BCI) are essential for understanding the association between neural activity and behavior. A prerequisite for many decoding approaches is spike sorting , the assignment of action potentials (spikes) to individual neurons. Current spike sorting algorithms, however, can be inaccurate and do not properly model uncertainty of spike assignments, therefore discarding information that could potentially improve decoding performance. Recent advances in high-density probes (e.g., Neuropixels) and computational methods now allow for extracting a rich set of spike features from unsorted data; these features can in turn be used to directly decode behavioral correlates. To this end, we propose a spike sorting-free decoding method that directly models the distribution of extracted spike features using a mixture of Gaussians (MoG) encoding the uncertainty of spike assignments, without aiming to solve the spike clustering problem explicitly. We allow the mixing proportion of the MoG to change over time in response to the behavior and develop variational inference methods to fit the resulting model and to perform decoding. We benchmark our method with an extensive suite of recordings from different animals and probe geometries, demonstrating that our proposed decoder can consistently outperform current methods based on thresholding (i.e. multi-unit activity) and spike sorting. Open source code is available at https://github.com/yzhang511/density_decoding .}, }
@article {pmid37788912, year = {2023}, author = {Wang, J and Li, Y and Qi, L and Mamtilahun, M and Liu, C and Liu, Z and Shi, R and Wu, S and Yang, GY}, title = {Advanced rehabilitation in ischaemic stroke research.}, journal = {Stroke and vascular neurology}, volume = {}, number = {}, pages = {}, doi = {10.1136/svn-2022-002285}, pmid = {37788912}, issn = {2059-8696}, abstract = {At present, due to the rapid progress of treatment technology in the acute phase of ischaemic stroke, the mortality of patients has been greatly reduced but the number of disabled survivors is increasing, and most of them are elderly patients. Physicians and rehabilitation therapists pay attention to develop all kinds of therapist techniques including physical therapy techniques, robot-assisted technology and artificial intelligence technology, and study the molecular, cellular or synergistic mechanisms of rehabilitation therapies to promote the effect of rehabilitation therapy. Here, we discussed different animal and in vitro models of ischaemic stroke for rehabilitation studies; the compound concept and technology of neurological rehabilitation; all kinds of biological mechanisms of physical therapy; the significance, assessment and efficacy of neurological rehabilitation; the application of brain-computer interface, rehabilitation robotic and non-invasive brain stimulation technology in stroke rehabilitation.}, }
@article {pmid37787386, year = {2023}, author = {Moratti, S and Gundlach, C and de Echegaray, J and Müller, MM}, title = {Distinct patterns of spatial attentional modulation of steady-state visual evoked magnetic fields (SSVEFs) in subdivisions of the human early visual cortex.}, journal = {Psychophysiology}, volume = {}, number = {}, pages = {e14452}, doi = {10.1111/psyp.14452}, pmid = {37787386}, issn = {1540-5958}, support = {MU 972/24-1//Deutsche Forschungsgemeinschaft/ ; //Universidad Complutense de Madrid/ ; }, abstract = {In recent years, steady-state visual evoked potentials (SSVEPs) became an increasingly valuable tool to investigate neural dynamics of competitive attentional interactions and brain-computer interfaces. This is due to their good signal-to-noise ratio, allowing for single-trial analysis, and their ongoing oscillating nature that enables to analyze temporal dynamics of facilitation and suppression. Given the popularity of SSVEPs, it is surprising that only a few studies looked at the cortical sources of these responses. This is in particular the case when searching for studies that assessed the cortical sources of attentional SSVEP amplitude modulations. To address this issue, we used a typical spatial attention task and recorded neuromagnetic fields (MEG) while presenting frequency-tagged stimuli in the left and right visual fields, respectively. Importantly, we controlled for attentional deployment in a baseline period before the shifting cue. Subjects either attended to a central fixation cross or to two peripheral stimuli simultaneously. Results clearly showed that signal sources and attention effects were restricted to the early visual cortex: V1, V2, hMT+, precuneus, occipital-parietal, and inferior-temporal cortex. When subjects attended to central fixation first, shifting attention to one of the peripheral stimuli resulted in a significant activation increase for the to-be-attended stimulus with no activation decrease for the to-be-ignored stimulus in hMT+ and inferio-temporal cortex, but significant SSVEF decreases from V1 to occipito-parietal cortex. When attention was first deployed to both rings, shifting attention away from one ring basically resulted in a significant activation decrease in all areas for the then-to-be-ignored stimulus.}, }
@article {pmid37786664, year = {2023}, author = {Li, M and Qiu, M and Zhu, L and Kong, W}, title = {Feature hypergraph representation learning on spatial-temporal correlations for EEG emotion recognition.}, journal = {Cognitive neurodynamics}, volume = {17}, number = {5}, pages = {1271-1281}, pmid = {37786664}, issn = {1871-4080}, abstract = {Electroencephalogram(EEG) becomes popular in emotion recognition for its capability of selectively reflecting the real emotional states. Existing graph-based methods have made primary progress in representing pairwise spatial relationships, but leaving higher-order relationships among EEG channels and higher-order relationships inside EEG series. Constructing a hypergraph is a general way of representing higher-order relations. In this paper, we propose a spatial-temporal hypergraph convolutional network(STHGCN) to capture higher-order relationships that existed in EEG recordings. STHGCN is a two-block hypergraph convolutional network, in which feature hypergraphs are constructed over the spectrum, space, and time domains, to explore spatial and temporal correlations under specific emotional states, namely the correlations of EEG channels and the dynamic relationships of temporal stamps. What's more, a self-attention mechanism is combined with the hypergraph convolutional network to initialize and update the relationships of EEG series. The experimental results demonstrate that constructed feature hypergraphs can effectively capture the correlations among valuable EEG channels and the correlations inside valuable EEG series, leading to the best emotion recognition accuracy among the graph methods. In addition, compared with other competitive methods, the proposed method achieves state-of-art results on SEED and SEED-IV datasets.}, }
@article {pmid37726002, year = {2023}, author = {Bressler, S and Neely, R and Yost, RM and Wang, D and Read, HL}, title = {A wearable EEG system for closed-loop neuromodulation of sleep-related oscillations.}, journal = {Journal of neural engineering}, volume = {20}, number = {5}, pages = {}, doi = {10.1088/1741-2552/acfb3b}, pmid = {37726002}, issn = {1741-2552}, abstract = {Objective.Healthy sleep plays a critical role in general well-being. Enhancement of slow-wave sleep by targeting acoustic stimuli to particular phases of delta (0.5-2 Hz) waves has shown promise as a non-invasive approach to improve sleep quality. Closed-loop stimulation during other sleep phases targeting oscillations at higher frequencies such as theta (4-7 Hz) or alpha (8-12 Hz) could be another approach to realize additional health benefits. However, systems to track and deliver stimulation relative to the instantaneous phase of electroencephalogram (EEG) signals at these higher frequencies have yet to be demonstrated outside of controlled laboratory settings.Approach.Here we examine the feasibility of using an endpoint-corrected version of the Hilbert transform (ecHT) algorithm implemented on a headband wearable device to measure alpha phase and deliver phase-locked auditory stimulation during the transition from wakefulness to sleep, during which alpha power is greatest. First, the ecHT algorithm is implementedin silicoto evaluate the performance characteristics of this algorithm across a range of sleep-related oscillatory frequencies. Secondly, a pilot sleep study tests feasibility to use the wearable device by users in the home setting for measurement of EEG activity during sleep and delivery of real-time phase-locked stimulation.Main results.The ecHT is capable of computing the instantaneous phase of oscillating signals with high precision, allowing auditory stimulation to be delivered at the intended phases of neural oscillations with low phase error. The wearable system was capable of measuring sleep-related neural activity with sufficient fidelity for sleep stage scoring during the at-home study, and phase-tracking performance matched simulated results. Users were able to successfully operate the system independently using the companion smartphone app to collect data and administer stimulation, and presentation of auditory stimuli during sleep initiation did not negatively impact sleep onset.Significance.This study demonstrates the feasibility of closed-loop real-time tracking and neuromodulation of a range of sleep-related oscillations using a wearable EEG device. Preliminary results suggest that this approach could be used to deliver non-invasive neuromodulation across all phases of sleep.}, }
@article {pmid37786654, year = {2023}, author = {Liang, W and Jin, J and Daly, I and Sun, H and Wang, X and Cichocki, A}, title = {Novel channel selection model based on graph convolutional network for motor imagery.}, journal = {Cognitive neurodynamics}, volume = {17}, number = {5}, pages = {1283-1296}, pmid = {37786654}, issn = {1871-4080}, abstract = {Multi-channel electroencephalography (EEG) is used to capture features associated with motor imagery (MI) based brain-computer interface (BCI) with a wide spatial coverage across the scalp. However, redundant EEG channels are not conducive to improving BCI performance. Therefore, removing irrelevant channels can help improve the classification performance of BCI systems. We present a new method for identifying relevant EEG channels. Our method is based on the assumption that useful channels share related information and that this can be measured by inter-channel connectivity. Specifically, we treat all candidate EEG channels as a graph and define channel selection as the problem of node classification on a graph. Then we design a graph convolutional neural network (GCN) model for channels classification. Channels are selected based on the outputs of our GCN model. We evaluate our proposed GCN-based channel selection (GCN-CS) method on three MI datasets. On three datasets, GCN-CS achieves performance improvements by reducing the number of channels. Specifically, we achieve classification accuracies of 79.76% on Dataset 1, 89.14% on Dataset 2 and 87.96% on Dataset 3, which outperform competing methods significantly.}, }
@article {pmid37786651, year = {2023}, author = {Liu, X and Wang, K and Liu, F and Zhao, W and Liu, J}, title = {3D Convolution neural network with multiscale spatial and temporal cues for motor imagery EEG classification.}, journal = {Cognitive neurodynamics}, volume = {17}, number = {5}, pages = {1357-1380}, pmid = {37786651}, issn = {1871-4080}, abstract = {Recently, deep learning-based methods have achieved meaningful results in the Motor imagery electroencephalogram (MI EEG) classification. However, because of the low signal-to-noise ratio and the various characteristics of brain activities among subjects, these methods lack a subject adaptive feature extraction mechanism. Another issue is that they neglect important spatial topological information and the global temporal variation trend of MI EEG signals. These issues limit the classification accuracy. Here, we propose an end-to-end 3D CNN to extract multiscale spatial and temporal dependent features for improving the accuracy performance of 4-class MI EEG classification. The proposed method adaptively assigns higher weights to motor-related spatial channels and temporal sampling cues than the motor-unrelated ones across all brain regions, which can prevent influences caused by biological and environmental artifacts. Experimental evaluation reveals that the proposed method achieved an average classification accuracy of 93.06% and 97.05% on two commonly used datasets, demonstrating excellent performance and robustness for different subjects compared to other state-of-the-art methods.In order to verify the real-time performance in actual applications, the proposed method is applied to control the robot based on MI EEG signals. The proposed approach effectively addresses the issues of existing methods, improves the classification accuracy and the performance of BCI system, and has great application prospects.}, }
@article {pmid37782330, year = {2023}, author = {Ye, C and Bai, Y and Zheng, S and Yu, H and Ni, G}, title = {OCT imaging of endolymphatic hydrops in mice: association with hearing loss.}, journal = {Acta oto-laryngologica}, volume = {}, number = {}, pages = {1-7}, doi = {10.1080/00016489.2023.2262509}, pmid = {37782330}, issn = {1651-2251}, abstract = {BACKGROUND: The etiology of Ménière's disease (MD) is still not completely clear, but it is believed to be associated with endolymphatic hydrops (EH), which is characterized by auditory functional disorders. Vasopressin injection in C57BL/6J mice can induce EH and serve as a model for MD. Optical Coherence Tomography (OCT) has shown its advantages as a non-invasive imaging method for observing EH.AimInvestigating the relationship between hearing loss and EH to assist clinical hearing assessments and indicate the severity of hydrops.<br><br>METHODS: C57BL/6J mice received 50&#x2009;&#x3bc;g/100g/day vasopressin injections to induce EH. Auditory function was assessed using auditory brainstem response (ABR) and distortion product otoacoustic emissions (DPOAE). OCT was used to visualize the cochlea.<br><br>RESULT: OCT observed accumulation of fluid within the scala media in the cochlear apex. ABR showed significant hearing loss after 4&#x2009;weeks. DPOAE revealed low-frequency hearing loss at 2&#x2009;weeks and widespread damage across frequencies at 4&#x2009;weeks.<br><br>CONCLUSION: The development of hearing loss in mouse models of MD is consistent with EH manifestations.SignificanceThis study demonstrates the possibility of indirectly evaluating the extent of EH through auditory assessment and emphasizes the significant value of OCT for imaging cochlear structures.}, }
@article {pmid37781630, year = {2023}, author = {Ye, J and Collinger, JL and Wehbe, L and Gaunt, R}, title = {Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.09.18.558113}, pmid = {37781630}, abstract = {The neural population spiking activity recorded by intracortical brain-computer interfaces (iBCIs) contain rich structure. Current models of such spiking activity are largely prepared for individual experimental contexts, restricting data volume to that collectable within a single session and limiting the effectiveness of deep neural networks (DNNs). The purported challenge in aggregating neural spiking data is the pervasiveness of context-dependent shifts in the neural data distributions. However, large scale unsupervised pretraining by nature spans heterogeneous data, and has proven to be a fundamental recipe for successful representation learning across deep learning. We thus develop Neural Data Transformer 2 (NDT2), a spatiotemporal Transformer for neural spiking activity, and demonstrate that pretraining can leverage motor BCI datasets that span sessions, subjects, and experimental tasks. NDT2 enables rapid adaptation to novel contexts in downstream decoding tasks and opens the path to deployment of pretrained DNNs for iBCI control. Code: https://github.com/joel99/context_general_bci.}, }
@article {pmid37781027, year = {2023}, author = {Kong, L and Guo, X and Shen, Y and Xu, L and Huang, H and Lu, J and Hu, S}, title = {Pushing the Frontiers: Optogenetics for Illuminating the Neural Pathophysiology of Bipolar Disorder.}, journal = {International journal of biological sciences}, volume = {19}, number = {14}, pages = {4539-4551}, pmid = {37781027}, issn = {1449-2288}, mesh = {Animals ; Humans ; *Bipolar Disorder/genetics/complications ; Optogenetics ; Central Nervous System ; }, abstract = {Bipolar disorder (BD), a disabling mental disorder, is featured by the oscillation between episodes of depression and mania, along with disturbance in the biological rhythms. It is on an urgent demand to identify the intricate mechanisms of BD pathophysiology. Based on the continuous progression of neural science techniques, the dysfunction of circuits in the central nervous system was currently thought to be tightly associated with BD development. Yet, challenge exists since it depends on techniques that can manipulate spatiotemporal dynamics of neuron activity. Notably, the emergence of optogenetics has empowered researchers with precise timing and local manipulation, providing a possible approach for deciphering the pathological underpinnings of mental disorders. Although the application of optogenetics in BD research remains preliminary due to the scarcity of valid animal models, this technique will advance the psychiatric research at neural circuit level. In this review, we summarized the crucial aberrant brain activity and function pertaining to emotion and rhythm abnormities, thereby elucidating the underlying neural substrates of BD, and highlighted the importance of optogenetics in the pursuit of BD research.}, }
@article {pmid37778215, year = {2023}, author = {Mijani, A and Cherloo, MN and Tang, H and Zhan, L}, title = {Spectrum-Enhanced TRCA (SE-TRCA): A novel approach for direction detection in SSVEP-based BCI.}, journal = {Computers in biology and medicine}, volume = {166}, number = {}, pages = {107488}, doi = {10.1016/j.compbiomed.2023.107488}, pmid = {37778215}, issn = {1879-0534}, abstract = {The Steady State Visual Evoked Potential (SSVEP) is a widely used component in BCIs due to its high noise resistance and low equipment requirements. Recently, a novel SSVEP-based paradigm has been introduced for direction detection, in which, unlike the common SSVEP paradigms that use several frequency stimuli, only one flickering stimulus is used and it makes direction detection very challenging. So far, only the CCA method has been used for direction detection using SSVEP component analysis. Since Canonical Correlation Analysis (CCA) has some limitations, a Task-Related Component Analysis (TRCA) based method has been introduced for feature extraction to improve the direction detection performance. Although these methods have been proven efficient, they do not utilize the latent frequency information in the EEG signal. Therefore, the performance of direction detection using SSVEP component analysis is still suboptimal. For further improvement, the TRCA-based algorithm is enhanced by incorporating frequency information and introducing Spectrum-Enhanced TRCA (SE-TRCA). SE-TRCA method can utilize frequency information in conjunction with spatial information by concatenating the EEG signal and its shifted version. Accordingly, the obtained spatio-spectral filters perform as a Finite Impulse Response (FIR) filter. To evaluate the proposed SE-TRCA method, two different sorts of datasets (1) a hybrid BCI dataset (including SSVEP component for direction detection) and (2) a pure benchmark SSVEP dataset (including SSVEP component for frequency detection) have been used. Our experiments showed that the accuracy of direction detection using the proposed SE-TRCA and TRCA approaches compared to CCA-based approach have been increased by 23.35% and 28.24%, respectively. Furthermore, the accuracy of character recognition obtained from integrating P300 and SSVEP components in CCA, TRCA, and SETRCA approaches are 54.01%, 56.02%, and 58.56%, on the hybrid dataset, respectively. The evaluation of the SE-TRCA method on the benchmark SSVEP dataset demonstrates that the SE-TRCA method outperforms both CCA and TRCA, particularly regarding frequency detection accuracy. In this specific dataset, the SE-TRCA method achieved an impressive frequency detection accuracy of 98.19% for a 3-s signal, surpassing the accuracies of TRCA and CCA, which were 97.91% and 90.47%, respectively. These results demonstrated that the TRCA-based approach is more efficient than the CCA approach to extracting spatial filters. Moreover, SE-TRCA, extracting both Spectral and spatial information from the EEG signal, can capture more discriminative features from the SSVEP component and increase the accuracy of classification. The results of this study emphasize the effectiveness of the proposed SE-TRCA approach across different SSVEP paradigms and tasks. These findings provide strong evidence for the method's ability to generalize well in SSVEP analysis.}, }
@article {pmid37776853, year = {2023}, author = {Dong, Y and Li, Y and Xiang, X and Xiao, ZC and Hu, J and Li, Y and Li, H and Hu, H}, title = {Stress relief as a natural resilience mechanism against depression-like behaviors.}, journal = {Neuron}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuron.2023.09.004}, pmid = {37776853}, issn = {1097-4199}, abstract = {Relief, the appetitive state after the termination of aversive stimuli, is evolutionarily conserved. Understanding the behavioral role of this well-conserved phenomenon and its underlying neurobiological mechanisms are open and important questions. Here, we discover that the magnitude of relief from physical stress strongly correlates with individual resilience to depression-like behaviors in chronic stressed mice. Notably, blocking stress relief causes vulnerability to depression-like behaviors, whereas natural rewards supplied shortly after stress promotes resilience. Stress relief is mediated by reward-related mesolimbic dopamine neurons, which show minute-long, persistent activation after stress termination. Circuitry-wise, activation or inhibition of circuits downstream of the ventral tegmental area during the transient relief period bi-directionally regulates depression resilience. These results reveal an evolutionary function of stress relief in depression resilience and identify the neural substrate mediating this effect. Importantly, our data suggest a behavioral strategy of augmenting positive valence of stress relief with natural rewards to prevent depression.}, }
@article {pmid37318349, year = {2023}, author = {Litton, JK and Beck, JT and Jones, JM and Andersen, J and Blum, JL and Mina, LA and Brig, R and Danso, M and Yuan, Y and Abbattista, A and Noonan, K and Niyazov, A and Chakrabarti, J and Czibere, A and Symmans, WF and Telli, ML}, title = {Neoadjuvant Talazoparib in Patients With Germline BRCA1/2 Mutation-Positive, Early-Stage Triple-Negative Breast Cancer: Results of a Phase II Study.}, journal = {The oncologist}, volume = {28}, number = {10}, pages = {845-855}, pmid = {37318349}, issn = {1549-490X}, support = {//Pfizer/ ; }, mesh = {Humans ; *BRCA1 Protein/genetics ; Neoadjuvant Therapy ; *Triple Negative Breast Neoplasms/drug therapy/genetics ; BRCA2 Protein/genetics ; Quality of Life ; Antineoplastic Combined Chemotherapy Protocols/adverse effects ; Poly(ADP-ribose) Polymerase Inhibitors/adverse effects ; Germ-Line Mutation ; Anthracyclines/therapeutic use ; }, abstract = {BACKGROUND: The undetermined efficacy of the current standard-of-care neoadjuvant treatment, anthracycline/platinum-based chemotherapy, in patients with early-stage triple-negative breast cancer (TNBC) and germline BRCA mutations emphasizes the need for biomarker-targeted treatment, such as poly(ADP-ribose) polymerase inhibitors, in this setting. This phase II, single-arm, open-label study evaluated the efficacy and safety of neoadjuvant talazoparib in patients with germline BRCA1/2-mutated early-stage TNBC.<br><br>PATIENTS AND METHODS: Patients with germline BRCA1/2-mutated early-stage TNBC received talazoparib 1 mg once daily for 24 weeks (0.75 mg for moderate renal impairment) followed by surgery. The primary endpoint was pathologic complete response (pCR) by independent central review (ICR). Secondary endpoints included residual cancer burden (RCB) by ICR. Safety and tolerability of talazoparib and patient-reported outcomes were assessed.<br><br>RESULTS: Of 61 patients, 48 received &#x2265;80% talazoparib doses, underwent surgery, and were assessed for pCR or progressed before pCR assessment and considered nonresponders. pCR rate was 45.8% (95% confidence interval [CI], 32.0%-60.6%) and 49.2% (95% CI, 36.7%-61.6%) in the evaluable and intent-to-treat (ITT) population, respectively. RCB 0/I rate was 45.8% (95% CI, 29.4%-63.2%) and 50.8% (95% CI, 35.5%-66.0%) in the evaluable and ITT population, respectively. Treatment-related adverse events (TRAE) were reported in 58 (95.1%) patients. Most common grade 3 and 4 TRAEs were anemia (39.3%) and neutropenia (9.8%). There was no clinically meaningful detriment in quality of life. No deaths occurred during the reporting period; 2 deaths due to progressive disease occurred during long-term follow-up (>400 days after first dose).<br><br>CONCLUSIONS: Neoadjuvant talazoparib monotherapy was active despite pCR rates not meeting the prespecified threshold; these rates were comparable to those observed with combination anthracycline- and taxane-based chemotherapy regimens. Talazoparib was generally well tolerated.<br><br>CLINICALTRIALS.GOV IDENTIFIER: NCT03499353.}, }
@article {pmid37774694, year = {2023}, author = {Xie, Y and Wang, K and Meng, J and Yue, J and Meng, L and Yi, W and Jung, TP and Xu, M and Ming, D}, title = {Cross-dataset transfer learning for Motor Imagery signal classification via multi-task learning and pre-training.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acfe9c}, pmid = {37774694}, issn = {1741-2552}, abstract = {Deep Learning (DL) models have been proven to be effective in decoding Motor Imagery (MI) signals in Electroencephalogram (EEG) data. However, DL models' success relies heavily on large amounts of training data, whereas EEG data collection is laborious and time-consuming. Recently, cross-dataset transfer learning has emerged as a promising approach to meet the data requirements of DL models. Nevertheless, transferring knowledge across datasets involving different MI tasks remains a significant challenge in cross-dataset transfer learning, limiting the full utilization of valuable data resources. This study proposes a pre-training-based cross-dataset transfer learning method inspired by Hard Parameter Sharing in multi-task learning. The method enables knowledge transfer across datasets with distinct MI tasks. We also designed four fine-tuning schemes and conducted extensive experiments on them. The results showed that compared to models without pre-training, models with pre-training achieved a maximum increase in accuracy of 7.76%. Moreover, when limited training data were available, the pre-training method significantly improved DL model's accuracy by 27.34% at most. The experiments also revealed that pre-trained models exhibit faster convergence and remarkable robustness. The training time per subject could be reduced by up to 102.83 seconds, and the variance of classification accuracy decreased by 75.22% at best. This study represents the first comprehensive investigation of the cross-dataset transfer learning method between two datasets with different MI tasks. The proposed pre-training method requires only minimal fine-tuning data when applying DL models to new MI paradigms, making MI-BCI more practical and user-friendly.}, }
@article {pmid37773070, year = {2023}, author = {Ma, H and Khaled, HG and Wang, X and Mandelberg, NJ and Cohen, SM and He, X and Tsien, RW}, title = {Excitation-transcription coupling, neuronal gene expression and synaptic plasticity.}, journal = {Nature reviews. Neuroscience}, volume = {}, number = {}, pages = {}, pmid = {37773070}, issn = {1471-0048}, abstract = {Excitation-transcription coupling (E-TC) links synaptic and cellular activity to nuclear gene transcription. It is generally accepted that E-TC makes a crucial contribution to learning and memory through its role in underpinning long-lasting synaptic enhancement in late-phase long-term potentiation and has more recently been linked to late-phase long-term depression: both processes require de novo gene transcription, mRNA translation and protein synthesis. E-TC begins with the activation of glutamate-gated N-methyl-D-aspartate-type receptors and voltage-gated L-type Ca[2+] channels at the membrane and culminates in the activation of transcription factors in the nucleus. These receptors and ion channels mediate E-TC through mechanisms that include long-range signalling from the synapse to the nucleus and local interactions within dendritic spines, among other possibilities. Growing experimental evidence links these E-TC mechanisms to late-phase long-term potentiation and learning and memory. These advances in our understanding of the molecular mechanisms of E-TC mean that future efforts can focus on understanding its mesoscale functions and how it regulates neuronal network activity and behaviour in physiological and pathological conditions.}, }
@article {pmid37772806, year = {2023}, author = {Wang, WS and Shi, ZW and Chen, XL and Li, Y and Xiao, H and Zeng, YH and Pi, XD and Zhu, LQ}, title = {Biodegradable Oxide Neuromorphic Transistors for Neuromorphic Computing and Anxiety Disorder Emulation.}, journal = {ACS applied materials &amp; interfaces}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsami.3c07671}, pmid = {37772806}, issn = {1944-8252}, abstract = {Brain-inspired neuromorphic computing and portable intelligent electronic products have received increasing attention. In the present work, nanocellulose-gated indium tin oxide neuromorphic transistors are fabricated. The device exhibits good electrical performance. Short-term synaptic plasticities were mimicked, including excitatory postsynaptic current, paired-pulse facilitation, and dynamic high-pass synaptic filtering. Interestingly, an effective linear synaptic weight updating strategy was adopted, resulting in an excellent recognition accuracy of ∼92.93% for the Modified National Institute of Standard and Technology database adopting a two-layer multilayer perceptron neural network. Moreover, with unique interfacial protonic coupling, anxiety disorder behavior was conceptually emulated, exhibiting "neurosensitization", "primary and secondary fear", and "fear-adrenaline secretion-exacerbated fear". Finally, the neuromorphic transistors could be dissolved in water, demonstrating potential in "green" electronics. These findings indicate that the proposed oxide neuromorphic transistors would have potential as implantable chips for nerve health diagnosis, neural prostheses, and brain-machine interfaces.}, }
@article {pmid37771349, year = {2023}, author = {Xie, X and Zhang, D and Yu, T and Duan, Y and Daly, I and He, S}, title = {Editorial: Explainable and advanced intelligent processing in the brain-machine interaction.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1280281}, pmid = {37771349}, issn = {1662-5161}, }
@article {pmid37770286, year = {2023}, author = {Fernyhough, C and Borghi, AM}, title = {Inner speech as language process and cognitive tool.}, journal = {Trends in cognitive sciences}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.tics.2023.08.014}, pmid = {37770286}, issn = {1879-307X}, abstract = {Many people report a form of internal language known as inner speech (IS). This review examines recent growth of research interest in the phenomenon, which has broadly supported a theoretical model in which IS is a functional language process that can confer benefits for cognition in a range of domains. A key insight to have emerged in recent years is that IS is an embodied experience characterized by varied subjective qualities, which can be usefully modeled in artificial systems and whose neural signals have the potential to be decoded through advancing brain-computer interface technologies. Challenges for future research include understanding individual differences in IS and mapping form to function across IS subtypes.}, }
@article {pmid37769865, year = {2023}, author = {Kim, YT and Park, BS and Yang, HR and Yi, SO and Nam-Goong, IS and Kim, JG}, title = {Exploring the potential hypothalamic role in mediating cisplatin-induced negative energy balance.}, journal = {Chemico-biological interactions}, volume = {}, number = {}, pages = {110733}, doi = {10.1016/j.cbi.2023.110733}, pmid = {37769865}, issn = {1872-7786}, abstract = {Cisplatin is a chemotherapeutic drug commonly used for treating different types of cancer. However, long-term use can lead to side effects, including anorexia, nausea, vomiting, and weight loss, which negatively affect the patient's quality of life and ability to undergo chemotherapy. This study aimed to investigate the mechanisms underlying the development of a negative energy balance during cisplatin treatment. Mice treated with cisplatin exhibit reduced food intake, body weight, and energy expenditure. We observed altered neuronal activity in the hypothalamic nuclei involved in the regulation of energy metabolism in cisplatin-treated mice. In addition, we observed activation of microglia and inflammation in the hypothalamus following treatment with cisplatin. Consistent with this finding, inhibition of microglial activation effectively rescued cisplatin-induced anorexia and body weight loss. The present study identified the role of hypothalamic neurons and inflammation linked to microglial activation in the anorexia and body weight loss observed during cisplatin treatment. These findings provide insight into the mechanisms underlying the development of metabolic abnormalities during cisplatin treatment and suggest new strategies for managing these side effects.}, }
@article {pmid37769525, year = {2023}, author = {Brands, R and Tebart, N and Thommes, M and Bartsch, J}, title = {UV/Vis spectroscopy as an in-line monitoring tool for tablet content uniformity.}, journal = {Journal of pharmaceutical and biomedical analysis}, volume = {236}, number = {}, pages = {115721}, doi = {10.1016/j.jpba.2023.115721}, pmid = {37769525}, issn = {1873-264X}, abstract = {Continuous manufacturing provides advantages compared to batch manufacturing and is increasingly gaining importance in the pharmaceutical industry. In particular, the implementation of tablet processes in continuous plants is an important part of current research. For this, in-line real-time monitoring of product quality through process analytical technology (PAT) tools is crucial. This study focuses on an in-line UV/Vis spectroscopy method for monitoring the active pharmaceutical ingredient (API) content in tablets. UV/Vis spectroscopy is particularly advantageous here, because it allows univariate data analysis without complex data processing. Experiments were conducted on a rotary tablet press. The tablets consisted of 7- 13 wt% theophylline monohydrate as API, lactose monohydrate and magnesium stearate. Two tablet production rates were investigated, 7200 and 20000 tablets per hour. The UV/Vis probe was mounted at the ejection position and measurements were taken on the tablet sidewall. Validation was according to ICH Q2 with respect to specificity, linearity, precision, accuracy and range. The specificity for this formulation was proven and linearity was sufficient with coefficients of determination of 0.9891 for the low throughput and 0.9936 for the high throughput. Repeatability and intermediate precision were investigated. Both were sufficient, indicated by coefficients of variations with a maximum of 6.46% and 6.34%, respectively. The accuracy was evaluated by mean percent recovery. This showed a higher accuracy at 20000 tablets per hour than 7200 tablets per hour. However, both throughputs demonstrate sufficient accuracy. Finally, UV/Vis spectroscopy is a promising alternative to the common NIR and Raman Spectroscopy.}, }
@article {pmid37767723, year = {2023}, author = {Quan, Z and Yang, Z and Tang, X and Fu, C and Zhou, X and Huang, L and Xia, L and Zhang, X}, title = {A double-tuned [1] H/[31] P coil for rabbit heart metabolism detection at 3 T.}, journal = {NMR in biomedicine}, volume = {}, number = {}, pages = {e5049}, doi = {10.1002/nbm.5049}, pmid = {37767723}, issn = {1099-1492}, support = {226-2022-00136//Fundamental Research Funds for the Central Universities/ ; 226-2023-00125//Fundamental Research Funds for the Central Universities/ ; BE2022049//Key R&amp;D Program of Jiangsu Province/ ; 2018B030333001//Key-Area R&amp;D Program of Guangdong Province/ ; 2018YFA0701400//National Key Research and Development Program of China/ ; 81873889//National Natural Science Foundation of China/ ; 61771423//National Natural Science Foundation of China/ ; 81701774//National Natural Science Foundation of China/ ; 52293424//National Natural Science Foundation of China/ ; 52277232//National Natural Science Foundation of China/ ; 2021ZD0200401//STI 2030 - Major Projects/ ; LR23E070001//Zhejiang Provincial Natural Science Foundation of China/ ; }, abstract = {Magnetic resonance imaging (MRI)/magnetic resonance spectroscopy (MRS) employing proton nuclear resonance has emerged as a pivotal modality in clinical diagnostics and fundamental research. Nonetheless, the scope of MRI/MRS extends beyond protons, encompassing nonproton nuclei that offer enhanced metabolic insights. A notable example is phosphorus-31 ([31] P) MRS, which provides valuable information on energy metabolites within the skeletal muscle and cardiac tissues of individuals affected by diabetes. This study introduces a novel double-tuned coil tailored for [1] H and [31] P frequencies, specifically designed for investigating cardiac metabolism in rabbits. The proposed coil design incorporates a butterfly-like coil for [31] P transmission, a four-channel array for [31] P reception, and an eight-channel array for [1] H reception, all strategically arranged on a body-conformal elliptic cylinder. To assess the performance of the double-tuned coil, a comprehensive evaluation encompassing simulations and experimental investigations was conducted. The simulation results demonstrated that the proposed [31] P transmit design achieved acceptable homogeneity and exhibited comparable transmit efficiency on par with a band-pass birdcage coil. In vivo experiments further substantiated the coil's efficacy, revealing that the rabbit with experimentally induced diabetes exhibited a lower phosphocreatine/adenosine triphosphate ratio compared with its normal counterpart. These findings emphasize the potential of the proposed coil design as a promising tool for investigating the therapeutic effects of novel diabetes drugs within the context of animal experimentation. Its capability to provide detailed metabolic information establishes it as an indispensable asset within this realm of research.}, }
@article {pmid37765965, year = {2023}, author = {Chowdhury, RR and Muhammad, Y and Adeel, U}, title = {Enhancing Cross-Subject Motor Imagery Classification in EEG-Based Brain-Computer Interfaces by Using Multi-Branch CNN.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {18}, pages = {}, pmid = {37765965}, issn = {1424-8220}, mesh = {*Brain-Computer Interfaces ; Electroencephalography ; Brain ; Communication ; Neural Networks, Computer ; }, abstract = {A brain-computer interface (BCI) is a computer-based system that allows for communication between the brain and the outer world, enabling users to interact with computers using neural activity. This brain signal is obtained from electroencephalogram (EEG) signals. A significant obstacle to the development of BCIs based on EEG is the classification of subject-independent motor imagery data since EEG data are very individualized. Deep learning techniques such as the convolutional neural network (CNN) have illustrated their influence on feature extraction to increase classification accuracy. In this paper, we present a multi-branch (five branches) 2D convolutional neural network that employs several hyperparameters for every branch. The proposed model achieved promising results for cross-subject classification and outperformed EEGNet, ShallowConvNet, DeepConvNet, MMCNN, and EEGNet_Fusion on three public datasets. Our proposed model, EEGNet Fusion V2, achieves 89.6% and 87.8% accuracy for the actual and imagined motor activity of the eegmmidb dataset and scores of 74.3% and 84.1% for the BCI IV-2a and IV-2b datasets, respectively. However, the proposed model has a bit higher computational cost, i.e., it takes around 3.5 times more computational time per sample than EEGNet_Fusion.}, }
@article {pmid37765916, year = {2023}, author = {Khare, SK and Bajaj, V and Gaikwad, NB and Sinha, GR}, title = {Ensemble Wavelet Decomposition-Based Detection of Mental States Using Electroencephalography Signals.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {18}, pages = {}, pmid = {37765916}, issn = {1424-8220}, abstract = {Technological advancements in healthcare, production, automobile, and aviation industries have shifted working styles from manual to automatic. This automation requires smart, intellectual, and safe machinery to develop an accurate and efficient brain-computer interface (BCI) system. However, developing such BCI systems requires effective processing and analysis of human physiology. Electroencephalography (EEG) is one such technique that provides a low-cost, portable, non-invasive, and safe solution for BCI systems. However, the non-stationary and nonlinear nature of EEG signals makes it difficult for experts to perform accurate subjective analyses. Hence, there is an urgent need for the development of automatic mental state detection. This paper presents the classification of three mental states using an ensemble of the tunable Q wavelet transform, the multilevel discrete wavelet transform, and the flexible analytic wavelet transform. Various features are extracted from the subbands of EEG signals during focused, unfocused, and drowsy states. Separate and fused features from ensemble decomposition are classified using an optimized ensemble classifier. Our analysis shows that the fusion of features results in a dimensionality reduction. The proposed model obtained the highest accuracies of 92.45% and 97.8% with ten-fold cross-validation and the iterative majority voting technique. The proposed method is suitable for real-time mental state detection to improve BCI systems.}, }
@article {pmid37765751, year = {2023}, author = {Zhang, C and Chu, H and Ma, M}, title = {Decoding Algorithm of Motor Imagery Electroencephalogram Signal Based on CLRNet Network Model.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {18}, pages = {}, pmid = {37765751}, issn = {1424-8220}, mesh = {*Algorithms ; Neural Networks, Computer ; Electroencephalography ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; }, abstract = {EEG decoding based on motor imagery is an important part of brain-computer interface technology and is an important indicator that determines the overall performance of the brain-computer interface. Due to the complexity of motor imagery EEG feature analysis, traditional classification models rely heavily on the signal preprocessing and feature design stages. End-to-end neural networks in deep learning have been applied to the classification task processing of motor imagery EEG and have shown good results. This study uses a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) network to obtain spatial information and temporal correlation from EEG signals. The use of cross-layer connectivity reduces the network gradient dispersion problem and enhances the overall network model stability. The effectiveness of this network model is demonstrated on the BCI Competition IV dataset 2a by integrating CNN, BiLSTM and ResNet (called CLRNet in this study) to decode motor imagery EEG. The network model combining CNN and BiLSTM achieved 87.0% accuracy in classifying motor imagery patterns in four classes. The network stability is enhanced by adding ResNet for cross-layer connectivity, which further improved the accuracy by 2.0% to achieve 89.0% classification accuracy. The experimental results show that CLRNet has good performance in decoding the motor imagery EEG dataset. This study provides a better solution for motor imagery EEG decoding in brain-computer interface technology research.}, }
@article {pmid37760412, year = {2023}, author = {Marciniak, B and Kciuk, M and Mujwar, S and Sundaraj, R and Bukowski, K and Gruszka, R}, title = {In Vitro and In Silico Investigation of BCI Anticancer Properties and Its Potential for Chemotherapy-Combined Treatments.}, journal = {Cancers}, volume = {15}, number = {18}, pages = {}, pmid = {37760412}, issn = {2072-6694}, support = {2021/05/X/NZ3/01147//National Science Center/ ; }, abstract = {BACKGROUND: DUSP6 phosphatase serves as a negative regulator of MAPK kinases involved in numerous cellular processes. BCI has been identified as a potential allosteric inhibitor with anticancer activity. Our study was designed to test the anticancer properties of BCI in colon cancer cells, to characterize the effect of this compound on chemotherapeutics such as irinotecan and oxaliplatin activity, and to identify potential molecular targets for this inhibitor.<br><br>METHODS: BCI cytotoxicity, proapoptotic activity, and cell cycle distribution were investigated in vitro on three colon cancer cell lines (DLD1, HT-29, and Caco-2). In silico investigation was prepared to assess BCI drug-likeness and identify potential molecular targets.<br><br>RESULTS: The exposure of colorectal cancer cells with BCI resulted in antitumor effects associated with cell cycle arrest and induction of apoptosis. BCI exhibited strong cytotoxicity on DLD1, HT-29, and Caco-2 cells. BCI showed no significant interaction with irinotecan, but strongly attenuated the anticancer activity of oxaliplatin when administered together. Analysis of synergy potential further confirmed the antagonistic interaction between these two compounds. In silico investigation indicated CDK5 as a potential new target of BCI.<br><br>CONCLUSIONS: Our studies point to the anticancer potential of BCI but note the need for a precise mechanism of action.}, }
@article {pmid37759889, year = {2023}, author = {Cui, Y and Xie, S and Fu, Y and Xie, X}, title = {Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis.}, journal = {Brain sciences}, volume = {13}, number = {9}, pages = {}, pmid = {37759889}, issn = {2076-3425}, support = {62220106007//National Natural Science Foundation of China/ ; 2020ZDLGY04-01//Shaanxi Provincial Key R&amp;D Program/ ; }, abstract = {Motor imagery (MI) electroencephalography (EEG) is natural and comfortable for controllers, and has become a research hotspot in the field of the brain-computer interface (BCI). Exploring the inter-subject MI-BCI performance variation is one of the fundamental problems in MI-BCI application. EEG microstates with high spatiotemporal resolution and multichannel information can represent brain cognitive function. In this paper, four EEG microstates (MS1, MS2, MS3, MS4) were used in the analysis of the differences in the subjects' MI-BCI performance, and the four microstate feature parameters (the mean duration, the occurrences per second, the time coverage ratio, and the transition probability) were calculated. The correlation between the resting-state EEG microstate feature parameters and the subjects' MI-BCI performance was measured. Based on the negative correlation of the occurrence of MS1 and the positive correlation of the mean duration of MS3, a resting-state microstate predictor was proposed. Twenty-eight subjects were recruited to participate in our MI experiments to assess the performance of our resting-state microstate predictor. The experimental results show that the average area under curve (AUC) value of our resting-state microstate predictor was 0.83, and increased by 17.9% compared with the spectral entropy predictor, representing that the microstate feature parameters can better fit the subjects' MI-BCI performance than spectral entropy predictor. Moreover, the AUC of microstate predictor is higher than that of spectral entropy predictor at both the single-session level and average level. Overall, our resting-state microstate predictor can help MI-BCI researchers better select subjects, save time, and promote MI-BCI development.}, }
@article {pmid37756271, year = {2023}, author = {Nunes, JD and Vourvopoulos, A and Blanco-Mora, DA and Jorge, C and Fernandes, JC and Bermudez I Badia, S and Figueiredo, P}, title = {Brain activation by a VR-based motor imagery and observation task: An fMRI study.}, journal = {PloS one}, volume = {18}, number = {9}, pages = {e0291528}, pmid = {37756271}, issn = {1932-6203}, mesh = {Adult ; Humans ; Magnetic Resonance Imaging ; Brain/diagnostic imaging ; *Brain-Computer Interfaces ; Imagery, Psychotherapy ; *Virtual Reality ; }, abstract = {Training motor imagery (MI) and motor observation (MO) tasks is being intensively exploited to promote brain plasticity in the context of post-stroke rehabilitation strategies. This may benefit from the use of closed-loop neurofeedback, embedded in brain-computer interfaces (BCI's) to provide an alternative non-muscular channel, which may be further augmented through embodied feedback delivered through virtual reality (VR). Here, we used functional magnetic resonance imaging (fMRI) in a group of healthy adults to map brain activation elicited by an ecologically-valid task based on a VR-BCI paradigm called NeuRow, whereby participants perform MI of rowing with the left or right arm (i.e., MI), while observing the corresponding movement of the virtual arm of an avatar (i.e., MO), on the same side, in a first-person perspective. We found that this MI-MO task elicited stronger brain activation when compared with a conventional MI-only task based on the Graz BCI paradigm, as well as to an overt motor execution task. It recruited large portions of the parietal and occipital cortices in addition to the somatomotor and premotor cortices, including the mirror neuron system (MNS), associated with action observation, as well as visual areas related with visual attention and motion processing. Overall, our findings suggest that the virtual representation of the arms in an ecologically-valid MI-MO task engage the brain beyond conventional MI tasks, which we propose could be explored for more effective neurorehabilitation protocols.}, }
@article {pmid37756179, year = {2023}, author = {Zhao, Z and Lin, Y and Wang, Y and Gao, X}, title = {Single-trial EEG Classification Using Spatio-temporal Weighting and Correlation Analysis for RSVP-Based Collaborative Brain Computer Interface.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2023.3309255}, pmid = {37756179}, issn = {1558-2531}, abstract = {OBJECTIVE: Since single brain computer interface (BCI) is limited in performance, it is necessary to develop collaborative BCI (cBCI) systems which integrate multi-user electroencephalogram (EEG) information to improve system performance. However, there are still some challenges in cBCI systems, including effective discriminant feature extraction of multi-user EEG data, fusion algorithms, time reduction of system calibration, etc. Methods: This study proposed an event-related potential (ERP) feature extraction and classification algorithm of spatio-temporal weighting and correlation analysis (STC) to improve the performance of cBCI systems. The proposed STC algorithm consisted of three modules. First, source extraction and interval modeling were used to overcome the problem of inter-trial variability. Second, spatio-temporal weighting and temporal projection were utilized to extract effective discriminant features for multi-user information fusion and cross-session transfer. Third, correlation analysis was conducted to match target/non-target templates for classification of multi-user and cross-session datasets.<br><br>RESULTS: The collaborative cross-session datasets of rapid serial visual presentation (RSVP) from 14 subjects were used to evaluate the performance of the EEG classification algorithm. For single-user/collaborative EEG classification of within-session and cross-session datasets, STC had significantly higher performance than the existing state-of-the-art machine learning algorithms.<br><br>CONCLUSION: It was demonstrated that STC was effective to improve the classification performance of multi-user collaboration and cross-session transfer for RSVP-based BCI systems, and was helpful to reduce the system calibration time.}, }
@article {pmid37753636, year = {2023}, author = {Csaky, R and van Es, MWJ and Jones, OP and Woolrich, M}, title = {Group-level brain decoding with deep learning.}, journal = {Human brain mapping}, volume = {}, number = {}, pages = {}, doi = {10.1002/hbm.26500}, pmid = {37753636}, issn = {1097-0193}, support = {203139/Z/16/Z/WT_/Wellcome Trust/United Kingdom ; 215573/Z/19/Z/WT_/Wellcome Trust/United Kingdom ; 106183/Z/14/Z/WT_/Wellcome Trust/United Kingdom ; }, abstract = {Decoding brain imaging data are gaining popularity, with applications in brain-computer interfaces and the study of neural representations. Decoding is typically subject-specific and does not generalise well over subjects, due to high amounts of between subject variability. Techniques that overcome this will not only provide richer neuroscientific insights but also make it possible for group-level models to outperform subject-specific models. Here, we propose a method that uses subject embedding, analogous to word embedding in natural language processing, to learn and exploit the structure in between-subject variability as part of a decoding model, our adaptation of the WaveNet architecture for classification. We apply this to magnetoencephalography data, where 15 subjects viewed 118 different images, with 30 examples per image; to classify images using the entire 1 s window following image presentation. We show that the combination of deep learning and subject embedding is crucial to closing the performance gap between subject- and group-level decoding models. Importantly, group models outperform subject models on low-accuracy subjects (although slightly impair high-accuracy subjects) and can be helpful for initialising subject models. While we have not generally found group-level models to perform better than subject-level models, the performance of group modelling is expected to be even higher with bigger datasets. In order to provide physiological interpretation at the group level, we make use of permutation feature importance. This provides insights into the spatiotemporal and spectral information encoded in the models. All code is available on GitHub (https://github.com/ricsinaruto/MEG-group-decode).}, }
@article {pmid37750334, year = {2023}, author = {Chen, B and Jiang, L and Lu, G and Li, Y and Zhang, S and Huang, X and Xu, P and Li, F and Yao, D}, title = {Altered dynamic network interactions in children with ASD during face recognition revealed by time-varying EEG networks.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {}, number = {}, pages = {}, doi = {10.1093/cercor/bhad355}, pmid = {37750334}, issn = {1460-2199}, support = {#2022ZD0208500//STI 2030-Major Projects/ ; #62103085//National Natural Science Foundation of China/ ; HNBBL230203//Scientific Research of Brain Science and Brain Computer Interface Technology/ ; }, abstract = {Although the electrophysiological event-related potential in face processing (e.g. N170) is widely accepted as a face-sensitivity biomarker that is deficient in children with autism spectrum disorders, the time-varying brain networks during face recognition are still awaiting further investigation. To explore the social deficits in autism spectrum disorder, especially the time-varying brain networks during face recognition, the current study analyzed the N170, cortical activity, and time-varying networks under 3 tasks (face-upright, face-inverted, and house-upright) in autism spectrum disorder and typically developing children. The results revealed a smaller N170 amplitude in autism spectrum disorder compared with typically developing, along with decreased cortical activity mainly in occipitotemporal areas. Concerning the time-varying networks, the atypically stronger information flow and brain network connections across frontal, parietal, and temporal regions in autism spectrum disorder were reported, which reveals greater effort was exerted by autism spectrum disorder to obtain comparable performance to the typically developing children, although the amplitude of N170 was still smaller than that of the typically developing children. Different brain activation states and interaction patterns of brain regions during face processing were discovered between autism spectrum disorder and typically developing. These findings shed light on the face-processing mechanisms in children with autism spectrum disorder and provide new insight for understanding the social dysfunction of autism spectrum disorder.}, }
@article {pmid37748558, year = {2023}, author = {Feng, Z and Wang, S and Qian, L and Xu, M and Wu, K and Kakkos, I and Guan, C and Sun, Y}, title = {μ-STAR: A novel framework for spatio-temporal M/EEG source imaging optimized by microstates.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {120372}, doi = {10.1016/j.neuroimage.2023.120372}, pmid = {37748558}, issn = {1095-9572}, abstract = {Source imaging of Electroencephalography (EEG) and Magnetoencephalography (MEG) provides a noninvasive way of monitoring brain activities with high spatial and temporal resolution. In order to address this highly ill-posed problem, conventional source imaging models adopted spatio-temporal constraints that assume spatial stability of the source activities, neglecting the transient characteristics of M/EEG. In this work, a novel source imaging method μ-STAR that includes a microstate analysis and a spatio-temporal Bayesian model was introduced to address this problem. Specifically, the microstate analysis was applied to achieve automatic determination of time window length with quasi-stable source activity pattern for optimal reconstruction of source dynamics. Then a user-specific spatial prior and data-driven temporal basis functions were utilized to characterize the spatio-temporal information of sources within each state. The solution of the source reconstruction was obtained through a computationally efficient algorithm based upon variational Bayesian and convex analysis. The performance of the μ-STAR was first assessed through numerical simulations, where we found that the determination and inclusion of optimal temporal length in the spatio-temporal prior significantly improved the performance of source reconstruction. More importantly, the μ-STAR model achieved robust performance under various settings (i.e., source numbers/areas, SNR levels, and source depth) with fast convergence speed compared with five widely-used benchmark models (including wMNE, STV, SBL, BESTIES, &amp; SI-STBF). Additional validations on real data were then performed on two publicly-available datasets (including block-design face-processing ERP and continuous resting-state EEG). The reconstructed source activities exhibited spatial and temporal neurophysiological plausible results consistent with previously-revealed neural substrates, thereby further proving the feasibility of the μ-STAR model for source imaging in various applications.}, }
@article {pmid37748476, year = {2023}, author = {Iivanainen, J and Carter, TR and Trumbo, M and McKay, J and Taulu, S and Wang, J and Stephen, J and Schwindt, PDD and Borna, A}, title = {Single-trial classification of evoked responses to auditory tones using OPM- and SQUID-MEG.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acfcd9}, pmid = {37748476}, issn = {1741-2552}, abstract = {Optically pumped magnetometers (OPMs) are emerging as a near-room-temperature alternative to superconducting quantum interference devices (SQUIDs) for magnetoencephalography (MEG). In contrast to SQUIDs, OPMs can be placed in a close proximity to subject's scalp potentially increasing the signal-to-noise ratio and spatial resolution of MEG. However, experimental demonstrations of these suggested benefits are still scarce. Here, to compare a 24-channel OPM-MEG system to a commercial whole-head SQUID system in a data-driven way, we quantified their performance in classifying single-trial evoked responses. Approach: We measured evoked responses to three auditory tones in six participants using both OPM- and SQUID-MEG systems. We performed pairwise temporal classification of the single-trial responses with linear discriminant analysis as well as multiclass classification with both EEGNet convolutional neural network and xDAWN decoding. Main results: OPMs provided higher classification accuracies than SQUIDs having a similar coverage of the left hemisphere of the participant. However, the SQUID sensors covering the whole helmet had classification scores larger than those of OPMs for two of the tone pairs, demonstrating the benefits of a whole-head measurement. Significance: The results demonstrate that the current OPM-MEG system provides high-quality data about the brain with room for improvement for high bandwidth non-invasive brain-computer interfacing. .}, }
@article {pmid37748474, year = {2023}, author = {Kaongoen, N and Choi, J and Choi, JW and Kwon, H and Hwang, C and Hwang, G and Kim, BH and Jo, S}, title = {The future of wearable EEG: A review of ear-EEG technology and its applications.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acfcda}, pmid = {37748474}, issn = {1741-2552}, abstract = {This review paper provides a comprehensive overview of ear-EEG technology, which involves recording electroencephalogram (EEG) signals from electrodes placed in or around the ear, and its applications in the field of neural engineering. Approach: We conducted a thorough literature search using multiple databases to identify relevant studies related to ear-EEG technology and its various applications. We selected 123 publications and synthesized the information to highlight the main findings and trends in this field. Main Results: Our review highlights the potential of ear-EEG technology as the future of wearable EEG technology. We discuss the advantages and limitations of ear-EEG compared to traditional scalp-based EEG and methods to overcome those limitations. Through our review, we found that ear-EEG is a promising method that produces comparable results to conventional scalp-based methods. We review the development of ear-EEG sensing devices, including the design, types of sensors, and materials. We also review the current state of research on ear-EEG in different application areas such as brain-computer interfaces, and clinical monitoring. Significance: This review paper is the first to focus solely on reviewing ear-EEG research articles. As such, it serves as a valuable resource for researchers, clinicians, and engineers working in the field of neural engineering. Our review sheds light on the exciting future prospects of ear-EEG, and its potential to advance neural engineering research and become the future of wearable EEG technology.}, }
@article {pmid37747857, year = {2023}, author = {Tresols, JJT and Chanel, CPC and Dehais, F}, title = {POMDP-BCI: A Benchmark of (re)active BCI using POMDP to Issue Commands.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2023.3318578}, pmid = {37747857}, issn = {1558-2531}, abstract = {OBJECTIVE: Past research in Brain-Computer Interfaces (BCI) have presented different decoding algorithms for different modalities. Meanwhile, highly specific decision making processes have been developed for some of these modalities, while others lack such a component in their classic pipeline. The present work proposes a model based on Partially Observable Markov Decission Process (POMDP) that works as a high-level decision making framework for three different active/reactive BCI modalities.<br><br>METHODS: We tested our approach on three different BCI modalities using publicly available datasets. We compared the general POMDP model as a decision making process with state of the art methods for each BCI modality. Accuracy, false positive (FP) trials, no-action (NA) trials and average decision time are presented as metrics.<br><br>RESULTS: Our results show how the presented POMDP models achieve comparable or better performance to the presented baseline methods, while being usable for the three proposed experiments without significant changes. Crucially, it offers the possibility of taking no-action (NA) when the decoding does not perform well.<br><br>CONCLUSION: The present work implements a flexible POMDP model that acts as a sequential decision framework for BCI systems that lack such a component, and perform comparably to those that include it.<br><br>SIGNIFICANCE: We believe the proposed POMDP framework provides several interesting properties for future BCI developments, mainly the generalizability to any BCI modality and the possible integration of other physiological or brain data pipelines under a unified decision-making framework.}, }
@article {pmid37747230, year = {2023}, author = {Khan, NN and Sweet, T and Harvey, CA and Warschausky, S and Huggins, JE and Thompson, DE}, title = {P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation.}, journal = {Journal of visualized experiments : JoVE}, volume = {}, number = {199}, pages = {}, doi = {10.3791/64959}, pmid = {37747230}, issn = {1940-087X}, support = {R21 HD054697/HD/NICHD NIH HHS/United States ; }, mesh = {*Brain-Computer Interfaces ; Mental Processes ; }, abstract = {Performance estimation is a necessary step in the development and validation of Brain-Computer Interface (BCI) systems. Unfortunately, even modern BCI systems are slow, making collecting sufficient data for validation a time-consuming task for end users and experimenters alike. Yet without sufficient data, the random variation in performance can lead to false inferences about how well a BCI is working for a particular user. For example, P300 spellers commonly operate around 1-5 characters per minute. To estimate accuracy with a 5% resolution requires 20 characters (4-20 min). Despite this time investment, the confidence bounds for accuracy from 20 characters can be as much as ±23% depending on observed accuracy. A previously published method, Classifier-Based Latency Estimation (CBLE), was shown to be highly correlated with BCI accuracy. This work presents a protocol for using CBLE to predict a user's P300 speller accuracy from relatively few characters (~3-8) of typing data. The resulting confidence bounds are tighter than those produced by traditional methods. The method can thus be used to estimate BCI performance more quickly and/or more accurately.}, }
@article {pmid37746153, year = {2023}, author = {Liu, T and Ye, A}, title = {Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1251968}, pmid = {37746153}, issn = {1662-4548}, abstract = {BACKGROUND: For non-invasive brain-computer interface systems (BCIs) with multiple electroencephalogram (EEG) channels, the key factor limiting their convenient application in the real world is how to perform reasonable channel selection while ensuring task accuracy, which can be modeled as a multi-objective optimization problem. Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the aforementioned problem.<br><br>METHODS: The multi-objective optimization problem model was designed based on the channel connectivity matrix and comprises two objectives: one is the task accuracy and the other one can sensitively indicate the removal status of channels in BCIs. The proposed DK-MOEA adopted a two-space framework, consisting of the population space and the knowledge space. Furthermore, a knowledge-assisted update operator was introduced to enhance the search efficiency of the population space by leveraging the domain knowledge stored in the knowledge space.<br><br>RESULTS: The proposed two-objective problem model and DK-MOEA were tested on a fatigue detection task and four state-of-the-art multi-objective evolutionary algorithms were used for comparison. The experimental results indicated that the proposed algorithm achieved the best results among all the comparative algorithms for most cases by the Wilcoxon rank sum test at a significance level of 0.05. DK-MOEA was also compared with a version without the utilization of domain knowledge and the experimental results validated the effectiveness of the knowledge-assisted mutation operator. Moreover, the comparison between DK-MOEA and a traditional classification algorithm using all channels demonstrated that DK-MOEA can strike the balance between task accuracy and the number of selected channels.<br><br>CONCLUSION: The formulated two-objective optimization model enabled the selection of a minimal number of channels without compromising classification accuracy. The utilization of domain knowledge improved the performance of DK-MOEA. By adopting the proposed two-objective problem model and DK-MOEA, a balance can be achieved between the number of the selected channels and the accuracy of the fatigue detection task. The methods proposed in this paper can reduce the complexity of subsequent data processing and enhance the convenience of practical applications.}, }
@article {pmid37746053, year = {2023}, author = {Huang, Z and Liao, Z and Ou, G and Chen, L and Zhang, Y}, title = {Authentication using c-VEP evoked in a mild-burdened cognitive task.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1240451}, pmid = {37746053}, issn = {1662-5161}, abstract = {In recent years, more and more researchers are devoting themselves to the studies about authentication based on biomarkers. Among a wide variety of biomarkers, code-modulated visual evoked potential (c-VEP) has attracted increasing attention due to its significant role in the field of brain-computer interface. In this study, we designed a mild-burdened cognitive task (MBCT), which can check whether participants focus their attention on the visual stimuli that evoke c-VEP. Furthermore, we investigated the authentication based on the c-VEP evoked in the cognitive task by introducing a deep learning method. Seventeen participants were recruited to take part in the MBCT experiments including two sessions, which were carried out on two different days. The c-VEP signals from the first session were extracted to train the authentication deep models. The c-VEP data of the second session were used to verify the models. It achieved a desirable performance, with the average accuracy and F1 score, respectively, of 0.92 and 0.89. These results show that c-VEP carries individual discriminative characteristics and it is feasible to develop a practical authentication system based on c-VEP.}, }
@article {pmid37745380, year = {2023}, author = {Chen, X and Wang, R and Khalilian-Gourtani, A and Yu, L and Dugan, P and Friedman, D and Doyle, W and Devinsky, O and Wang, Y and Flinker, A}, title = {A Neural Speech Decoding Framework Leveraging Deep Learning and Speech Synthesis.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.09.16.558028}, pmid = {37745380}, abstract = {Decoding human speech from neural signals is essential for brain-computer interface (BCI) technologies restoring speech function in populations with neurological deficits. However, it remains a highly challenging task, compounded by the scarce availability of neural signals with corresponding speech, data complexity, and high dimensionality, and the limited publicly available source code. Here, we present a novel deep learning-based neural speech decoding framework that includes an ECoG Decoder that translates electrocorticographic (ECoG) signals from the cortex into interpretable speech parameters and a novel differentiable Speech Synthesizer that maps speech parameters to spectrograms. We develop a companion audio-to-audio auto-encoder consisting of a Speech Encoder and the same Speech Synthesizer to generate reference speech parameters to facilitate the ECoG Decoder training. This framework generates natural-sounding speech and is highly reproducible across a cohort of 48 participants. Among three neural network architectures for the ECoG Decoder, the 3D ResNet model has the best decoding performance (PCC=0.804) in predicting the original speech spectrogram, closely followed by the SWIN model (PCC=0.796). Our experimental results show that our models can decode speech with high correlation even when limited to only causal operations, which is necessary for adoption by real-time neural prostheses. We successfully decode speech in participants with either left or right hemisphere coverage, which could lead to speech prostheses in patients with speech deficits resulting from left hemisphere damage. Further, we use an occlusion analysis to identify cortical regions contributing to speech decoding across our models. Finally, we provide open-source code for our two-stage training pipeline along with associated preprocessing and visualization tools to enable reproducible research and drive research across the speech science and prostheses communities.}, }
@article {pmid37744725, year = {2023}, author = {Kuo, CH and Tu, TH and Chen, KT}, title = {Editorial: Advanced technological applications in neurosurgery.}, journal = {Frontiers in surgery}, volume = {10}, number = {}, pages = {1277997}, pmid = {37744725}, issn = {2296-875X}, }
@article {pmid37741695, year = {2023}, author = {Gao, Z}, title = {Adenosine A2A receptor and glia.}, journal = {International review of neurobiology}, volume = {170}, number = {}, pages = {29-48}, doi = {10.1016/bs.irn.2023.08.002}, pmid = {37741695}, issn = {2162-5514}, mesh = {Humans ; *Receptor, Adenosine A2A ; *Neuroglia ; Astrocytes ; Microglia ; Neurons ; }, abstract = {The adenosine A2A receptor (A2AR) is abundantly expressed in the brain, including both neurons and glial cells. While the expression of A2AR is relative low in glia, its levels elevate robustly in astrocytes and microglia under pathological conditions. Elevated A2AR appears to play a detrimental role in a number of disease states, by promoting neuroinflammation and astrocytic reaction to contribute to the progression of neurodegenerative and psychiatric diseases.}, }
@article {pmid37741227, year = {2023}, author = {Lee, S and Kim, H and Kim, JB and Kim, DJ}, title = {Effects of altered functional connectivity on motor imagery brain-computer interfaces based on the laterality of paralysis in hemiplegia patients.}, journal = {Computers in biology and medicine}, volume = {166}, number = {}, pages = {107435}, doi = {10.1016/j.compbiomed.2023.107435}, pmid = {37741227}, issn = {1879-0534}, abstract = {Motor imagery (MI)-based brain-computer interfaces are widely employed for improving the rehabilitation of paralyzed people and their quality of life. It has been well documented that brain activity patterns in the primary motor cortex and sensorimotor cortex during MI are similar to those of motor execution/imagery. However, individuals paralyzed owing to various neurological disorders have debilitated activation of the motor control region. Therefore, the differences in brain activation based on the paralysis location should be considered. We analyzed brain activation patterns using the electroencephalogram (EEG) acquired while performing MI on the right upper limb to investigate hemiplegia-related brain activation patterns. Participants with hemiplegia of the right upper limb (n=7) and left upper limb (n=4) performed the MI task within the right upper limb. EEG signals were acquired using 14 channels based on a 10-20 global system, and analyzed for event-related desynchronization (ERD) based on event-related spectral perturbation and functional connectivity, using the weighted phase-lag index of both hemispheres at the location of hemiplegia. Enhanced ERD was found in the ipsilateral region, compared to the contralateral region, after MI of the affected limb. The reduced difference in the centrality of the channels was observed in all subjects, likely reflecting an altered brain network from increased interhemispheric connections. Furthermore, the tendency of distinct network-based features depending on the MI task on the affected limb was diluted between the inter-hemispheres. Analysis of interaction between inter-region using functional connectivity could provide avenues for further investigation of BCI strategy through the brain state of individuals with hemiplegia.}, }
@article {pmid37741066, year = {2023}, author = {Li, Z and Wang, X and Xing, Y and Zhang, X and Yu, T and Li, X}, title = {Measuring multivariate phase synchronization with symbolization and permutation.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {167}, number = {}, pages = {838-846}, doi = {10.1016/j.neunet.2023.07.007}, pmid = {37741066}, issn = {1879-2782}, abstract = {Phase synchronization is an important mechanism for the information processing of neurons in the brain. Most of the current phase synchronization measures are bivariate and focus on the synchronization between pairs of time series. However, these methods do not provide a full picture of global interactions in neural systems. Considering the prevalence and importance of multivariate neural signal analysis, there is an urgent need to quantify global phase synchronization (GPS) in neural networks. Therefore, we propose a new measure named symbolic phase difference and permutation entropy (SPDPE), which symbolizes the phase difference in multivariate neural signals and estimates GPS according to the permutation patterns of the symbolic sequences. The performance of SPDPE was evaluated using simulated data generated by Kuramoto and Rössler model. The results demonstrate that SPDPE exhibits low sensitivity to data length and outperforms existing methods in accurately characterizing GPS and effectively resisting noise. Moreover, to validate the method with real data, it was applied to classify seizures and non-seizures by calculating the GPS of stereoelectroencephalography (SEEG) data recorded from the onset zones of ten epilepsy patients. We believe that SPDPE will improve the estimation of GPS in many applications, such as EEG-based brain-computer interfaces, brain modeling, and simultaneous EEG-fMRI analysis.}, }
@article {pmid37740917, year = {2023}, author = {Zhou, T and Kawasaki, K and Suzuki, T and Hasegawa, I and Roe, AW and Tanigawa, H}, title = {Mapping information flow between the inferotemporal and prefrontal cortices via neural oscillations in memory retrieval and maintenance.}, journal = {Cell reports}, volume = {42}, number = {10}, pages = {113169}, doi = {10.1016/j.celrep.2023.113169}, pmid = {37740917}, issn = {2211-1247}, abstract = {Interaction between the inferotemporal (ITC) and prefrontal (PFC) cortices is critical for retrieving information from memory and maintaining it in working memory. Neural oscillations provide a mechanism for communication between brain regions. However, it remains unknown how information flow via neural oscillations is functionally organized in these cortices during these processes. In this study, we apply Granger causality analysis to electrocorticographic signals from both cortices of monkeys performing visual association tasks to map information flow. Our results reveal regions within the ITC where information flow to and from the PFC increases via specific frequency oscillations to form clusters during memory retrieval and maintenance. Theta-band information flow in both directions increases in similar regions in both cortices, suggesting reciprocal information exchange in those regions. These findings suggest that specific subregions function as nodes in the memory information-processing network between the ITC and the PFC.}, }
@article {pmid37739947, year = {2023}, author = {Wang, H and Zhang, X and Li, J and Li, B and Gao, X and Hao, Z and Fu, J and Zhou, Z and Atia, M}, title = {Driving risk cognition of passengers in highly automated driving based on the prefrontal cortex activity via fNIRS.}, journal = {Scientific reports}, volume = {13}, number = {1}, pages = {15839}, pmid = {37739947}, issn = {2045-2322}, support = {52072215//National Natural Science Foundation of China (National Science Foundation of China)/ ; 52221005//National Natural Science Foundation of China (National Science Foundation of China)/ ; U1964203//National Natural Science Foundation of China (National Science Foundation of China)/ ; 2022YFB2503003//National key R &amp; D Program of China/ ; 2020YFB1600303//National key R &amp; D Program of China/ ; }, mesh = {Humans ; *Cognition ; *Prefrontal Cortex ; Brain ; Spectrum Analysis ; Autonomous Vehicles ; }, abstract = {For high-level automated vehicles, the human being acts as the passenger instead of the driver and does not need to operate vehicles, it makes the brain-computer interface system of high-level automated vehicles depend on the brain state of passengers rather than that of drivers. Particularly when confronting challenging driving situations, how to implement the mental states of passengers into safe driving is a vital choice in the future. Quantifying the cognition of the driving risk of the passenger is a basic step in achieving this goal. In this paper, the passengers' mental activities in low-risk episode and high-risk episode were compared, the influences on passengers' mental activities caused by driving scenario risk was first explored via fNIRS. The results showed that the mental activities of passengers caused by driving scenario risk in the Brodmann area 10 are very active, which was verified by examining the real-driving data collected in corresponding challenging experiments, and there is a positive correlation between the cerebral oxygen and the driving risk field. This initial finding provides a possible solution to design a human-centred intelligent system to promise safe driving for high-level automated vehicles using passengers' driving risk cognition.}, }
@article {pmid37738741, year = {2023}, author = {Chen, K and Garcia Padilla, C and Kiselyov, K and Kozai, TDY}, title = {Cell-specific alterations in autophagy-lysosomal activity near the chronically implanted microelectrodes.}, journal = {Biomaterials}, volume = {302}, number = {}, pages = {122316}, doi = {10.1016/j.biomaterials.2023.122316}, pmid = {37738741}, issn = {1878-5905}, abstract = {Intracortical microelectrodes that can record and stimulate brain activity have become a valuable technique for basic science research and clinical applications. However, long-term implantation of these microelectrodes can lead to progressive neurodegeneration in the surrounding microenvironment, characterized by elevation in disease-associated markers. Dysregulation of autophagy-lysosomal degradation, a major intracellular waste removal process, is considered a key factor in the onset and progression of neurodegenerative diseases. It is plausible that similar dysfunctions in autophagy-lysosomal degradation contribute to tissue degeneration following implantation-induced focal brain injury, ultimately impacting recording performance. To understand how the focal, persistent brain injury caused by long-term microelectrode implantation impairs autophagy-lysosomal pathway, we employed two-photon microscopy and immunohistology. This investigation focused on the spatiotemporal characterization of autophagy-lysosomal activity near the chronically implanted microelectrode. We observed an aberrant accumulation of immature autophagy vesicles near the microelectrode over the chronic implantation period. Additionally, we found deficits in autophagy-lysosomal clearance proximal to the chronic implant, which was associated with an accumulation of autophagy cargo and a reduction in lysosomal protease level during the chronic period. Furthermore, our evidence demonstrates reactive astrocytes have myelin-containing lysosomes near the microelectrode, suggesting its role of myelin engulfment during acute implantation period. Together, this study sheds light on the process of brain tissue degeneration caused by long-term microelectrode implantation, with a specific focus on impaired intracellular waste degradation.}, }
@article {pmid37738340, year = {2023}, author = {Abbasi, A and Lassagne, H and Estebanez, L and Goueytes, D and Shulz, DE and Ego-Stengel, V}, title = {Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback.}, journal = {Science advances}, volume = {9}, number = {38}, pages = {eadh1328}, pmid = {37738340}, issn = {2375-2548}, mesh = {Humans ; Animals ; Mice ; Feedback ; *Brain-Computer Interfaces ; Learning ; *Motor Cortex ; Motor Neurons ; }, abstract = {Neuroprosthetics offer great hope for motor-impaired patients. One obstacle is that fine motor control requires near-instantaneous, rich somatosensory feedback. Such distributed feedback may be recreated in a brain-machine interface using distributed artificial stimulation across the cortical surface. Here, we hypothesized that neuronal stimulation must be contiguous in its spatiotemporal dynamics to be efficiently integrated by sensorimotor circuits. Using a closed-loop brain-machine interface, we trained head-fixed mice to control a virtual cursor by modulating the activity of motor cortex neurons. We provided artificial feedback in real time with distributed optogenetic stimulation patterns in the primary somatosensory cortex. Mice developed a specific motor strategy and succeeded to learn the task only when the optogenetic feedback pattern was spatially and temporally contiguous while it moved across the topography of the somatosensory cortex. These results reveal spatiotemporal properties of the sensorimotor cortical integration that set constraints on the design of neuroprosthetics.}, }
@article {pmid37737710, year = {2023}, author = {Xin, J and Shi, Y and Zhang, X and Yuan, X and Xin, Y and He, H and Shen, J and Blankenship, RE and Xu, X}, title = {Carotenoid assembly regulates quinone diffusion and the Roseiflexus castenholzii reaction center-light harvesting complex architecture.}, journal = {eLife}, volume = {12}, number = {}, pages = {}, pmid = {37737710}, issn = {2050-084X}, support = {32171227//National Natural Science Foundation of China/ ; 31870740//National Natural Science Foundation of China/ ; 31570738//National Natural Science Foundation of China/ ; LR22C020002//Zhejiang Provincial Outstanding Youth Science Foundation/ ; 32301056//National Natural Science Foundation of China/ ; }, mesh = {Cytoplasm ; *Quinones ; *Carotenoids ; }, abstract = {Carotenoid (Car) pigments perform central roles in photosynthesis-related light harvesting (LH), photoprotection, and assembly of functional pigment-protein complexes. However, the relationships between Car depletion in the LH, assembly of the prokaryotic reaction center (RC)-LH complex, and quinone exchange are not fully understood. Here, we analyzed native RC-LH (nRC-LH) and Car-depleted RC-LH (dRC-LH) complexes in Roseiflexus castenholzii, a chlorosome-less filamentous anoxygenic phototroph that forms the deepest branch of photosynthetic bacteria. Newly identified exterior Cars functioned with the bacteriochlorophyll B800 to block the proposed quinone channel between LHαβ subunits in the nRC-LH, forming a sealed LH ring that was disrupted by transmembrane helices from cytochrome c and subunit X to allow quinone shuttling. dRC-LH lacked subunit X, leading to an exposed LH ring with a larger opening, which together accelerated the quinone exchange rate. We also assigned amino acid sequences of subunit X and two hypothetical proteins Y and Z that functioned in forming the quinone channel and stabilizing the RC-LH interactions. This study reveals the structural basis by which Cars assembly regulates the architecture and quinone exchange of bacterial RC-LH complexes. These findings mark an important step forward in understanding the evolution and diversity of prokaryotic photosynthetic apparatus.}, }
@article {pmid37736411, year = {2023}, author = {Jovanovic, LI and Jervis Rademeyer, H and Pakosh, M and Musselman, KE and Popovic, MR and Marquez-Chin, C}, title = {Scoping Review on Brain-Computer Interface-Controlled Electrical Stimulation Interventions for Upper Limb Rehabilitation in Adults: A Look at Participants, Interventions, and Technology.}, journal = {Physiotherapy Canada. Physiotherapie Canada}, volume = {75}, number = {3}, pages = {276-290}, pmid = {37736411}, issn = {0300-0508}, abstract = {PURPOSE: While current rehabilitation practice for improving arm and hand function relies on physical/occupational therapy, a growing body of research evaluates the effects of technology-enhanced rehabilitation. We review interventions that combine a brain-computer interface (BCI) with electrical stimulation (ES) for upper limb movement rehabilitation to summarize the evidence on (1) populations of study participants, (2) BCI-ES interventions, and (3) the BCI-ES systems.<br><br>METHOD: After searching seven databases, two reviewers identified 23 eligible studies. We consolidated information on the study participants, interventions, and approaches used to develop integrated BCI-ES systems. The included studies investigated the use of BCI-ES interventions with stroke and spinal cord injury (SCI) populations. All studies used electroencephalography to collect brain signals for the BCI, and functional electrical stimulation was the most common type of ES. The BCI-ES interventions were typically conducted without a therapist, with sessions varying in both frequency and duration.<br><br>RESULTS: Of the 23 eligible studies, only 3 studies involved the SCI population, compared to 20 involving individuals with stroke.<br><br>CONCLUSIONS: Future BCI-ES interventional studies could address this gap. Additionally, standardization of device and rehabilitation modalities, and study-appropriate involvement with therapists, can be considered to advance this intervention towards clinical implementation.}, }
@article {pmid37736145, year = {2023}, author = {Boscolo Galazzo, I and Tonin, L and Miladinović, A and Storti, SF}, title = {Editorial: Brain-connectivity-based computer interfaces.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1281446}, pmid = {37736145}, issn = {1662-5161}, }
@article {pmid37736040, year = {2023}, author = {Mirfathollahi, A and Ghodrati, MT and Shalchyan, V and Zarrindast, MR and Daliri, MR}, title = {Decoding hand kinetics and kinematics using somatosensory cortex activity in active and passive movement.}, journal = {iScience}, volume = {26}, number = {10}, pages = {107808}, pmid = {37736040}, issn = {2589-0042}, abstract = {Area 2 of the primary somatosensory cortex (S1), encodes proprioceptive information of limbs. Several studies investigated the encoding of movement parameters in this area. However, the single-trial decoding of these parameters, which can provide additional knowledge about the amount of information available in sub-regions of this area about instantaneous limb movement, has not been well investigated. We decoded kinematic and kinetic parameters of active and passive hand movement during center-out task using conventional and state-based decoders. Our results show that this area can be used to accurately decode position, velocity, force, moment, and joint angles of hand. Kinematics had higher accuracies compared to kinetics and active trials were decoded more accurately than passive trials. Although the state-based decoder outperformed the conventional decoder in the active task, it was the opposite in the passive task. These results can be used in intracortical micro-stimulation procedures to provide proprioceptive feedback to BCI subjects.}, }
@article {pmid37735237, year = {2023}, author = {Liang, S and Zhao, L and Ni, P and Wang, Q and Guo, W and Xu, Y and Cai, J and Tao, S and Li, X and Deng, W and Palaniyappan, L and Li, T}, title = {Frontostriatal circuitry and the tryptophan kynurenine pathway in major psychiatric disorders.}, journal = {Psychopharmacology}, volume = {}, number = {}, pages = {}, pmid = {37735237}, issn = {1432-2072}, abstract = {RATIONALE: An imbalance of the tryptophan kynurenine pathway (KP) commonly occurs in psychiatric disorders, though the neurocognitive and network-level effects of this aberration are unclear.<br><br>OBJECTIVES: In this study, we examined the connection between dysfunction in the frontostriatal brain circuits, imbalances in the tryptophan kynurenine pathway (KP), and neurocognition in major psychiatric disorders.<br><br>METHODS: Forty first-episode medication-naive patients with schizophrenia (SCZ), fifty patients with bipolar disorder (BD), fifty patients with major depressive disorder (MDD), and forty-two healthy controls underwent resting-state functional magnetic resonance imaging. Plasma levels of KP metabolites were measured, and neurocognitive function was evaluated. Frontostriatal connectivity and KP metabolites were compared between groups while controlling for demographic and clinical characteristics. Canonical correlation analyses were conducted to explore multidimensional relationships between frontostriatal circuits-KP and KP-cognitive features.<br><br>RESULTS: Patient groups shared hypoconnectivity between bilateral ventrolateral prefrontal cortex (vlPFC) and left insula, with disorder-specific dysconnectivity in SCZ related to PFC, left dorsal striatum hypoconnectivity. The BD group had higher anthranilic acid and lower xanthurenic acid levels than the other groups. KP metabolites and ratios related to disrupted frontostriatal dysconnectivity in a transdiagnostic manner. The SCZ group and MDD group separately had high-dimensional associations between KP metabolites and cognitive measures.<br><br>CONCLUSIONS: The findings suggest that KP may influence cognitive performance across psychiatric conditions via frontostriatal dysfunction.}, }
@article {pmid37733286, year = {2023}, author = {Prinsloo, S and Kaptchuk, TJ and De Ridder, D and Lyle, R and Bruera, E and Novy, D and Barcenas, CH and Cohen, LG}, title = {Brain-computer interface relieves chronic chemotherapy-induced peripheral neuropathy: A randomized, double-blind, placebo-controlled trial.}, journal = {Cancer}, volume = {}, number = {}, pages = {}, doi = {10.1002/cncr.35027}, pmid = {37733286}, issn = {1097-0142}, support = {1K01AT008485-01//National Center for Complimentary and Integrative Health/ ; CCR-14-800//The Rising Tide Foundation/ ; }, abstract = {BACKGROUND: Chemotherapy-induced peripheral neuropathy (CIPN) includes negative sensations that remain a major chronic problem for cancer survivors. Previous research demonstrated that neurofeedback (a closed-loop brain-computer interface [BCI]) was effective at treating CIPN versus a waitlist control (WLC). The authors' a priori hypothesis was that BCI would be superior to placebo feedback (placebo control [PLC]) and to WLC in alleviating CIPN and that changes in brain activity would predict symptom report.<br><br>METHODS: Randomization to one of three conditions occurred between November 2014 and November 2018. Breast cancer survivors no longer in treatment were assessed at baseline, at the end of 20 treatment sessions, and 1 month later. Auditory and visual rewards were given over 20 sessions based on each patient's ability to modify their own electroencephalographic signals. The Pain Quality Assessment Scale (PQAS) at the end of treatment was the primary outcome, and changes in electroencephalographic signals and 1-month data also were examined.<br><br>RESULTS: The BCI and PLC groups reported significant symptom reduction. The BCI group demonstrated larger effect size differences from the WLC group than the PLC group (mean change score: BCI vs. WLC, -2.60 vs. 0.38; 95% confidence interval, -3.67, -1.46 [p&#xa0;=&#xa0;.000; effect size, 1.07]; PLC, -2.26; 95% confidence interval, -3.33, -1.19 [p&#xa0;=&#xa0;.001 vs. WLC; effect size, 0.9]). At 1 month, symptoms continued to improve only for the BCI group. Targeted brain changes at the end of treatment predicted symptoms at 1 month for the BCI group only.<br><br>CONCLUSIONS: BCI is a promising treatment for CIPN and may have a longer lasting effect than placebo (nonspecific BCI), which is an important consideration for long-term symptom relief. Although scientifically interesting, the ability to separate real from placebo treatment may not be as important as understanding the placebo effects differently from effects of the intervention.<br><br>PLAIN LANGUAGE SUMMARY: Chemotherapy-induced nerve pain (neuropathy) can be disabling for cancer survivors; however, the way symptoms are felt depends on how the brain interprets the signals from nerves in the body. We determined that the perception of neuropathy can be changed by working directly with the brain. Survivors in our trial played 20 sessions of a type of video game that was designed to change the way the brain processed sensation and movement. In this, our second trial, we again observed significant improvement in symptoms that lasted after the treatment was complete.}, }
@article {pmid37732305, year = {2023}, author = {Li, F and Zhang, D and Chen, J and Tang, K and Li, X and Hou, Z}, title = {Research hotspots and trends of brain-computer interface technology in stroke: a bibliometric study and visualization analysis.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1243151}, pmid = {37732305}, issn = {1662-4548}, abstract = {BACKGROUND: The incidence and mortality rates of stroke are escalating due to the growing aging population, which presents a significant hazard to human health. In the realm of stroke, brain-computer interface (BCI) technology has gained considerable attention as a means to enhance treatment efficacy and improve quality of life. Consequently, a bibliometric visualization analysis was performed to investigate the research hotspots and trends of BCI technology in stroke, with the objective of furnishing reference and guidance for future research.<br><br>METHODS: This study utilized the Science Citation Index Expanded (SCI-Expanded) within the Web of Science Core Collection (WoSCC) database as the data source, selecting relevant literature published between 2013 and 2022 as research sample. Through the application of VOSviewer 1.6.19 and CiteSpace 6.2.R2 visualization analysis software, as well as the bibliometric online analysis platform, the scientific knowledge maps were constructed and subjected to visualization display, and statistical analysis.<br><br>RESULTS: This study encompasses a total of 693 relevant literature, which were published by 2,556 scholars from 975 institutions across 53 countries/regions and have been collected by 185 journals. In the past decade, BCI technology in stroke research has exhibited an upward trend in both annual publications and citations. China and the United States are high productivity countries, while the University of Tubingen stands out as the most contributing institution. Birbaumer N and Pfurtscheller G are the authors with the highest publication and citation frequency in this field, respectively. Frontiers in Neuroscience has published the most literature, while Journal of Neural Engineering has the highest citation frequency. The research hotspots in this field cover keywords such as stroke, BCI, rehabilitation, motor imagery (MI), motor recovery, electroencephalogram (EEG), neurorehabilitation, neural plasticity, task analysis, functional electrical stimulation (FES), motor impairment, feature extraction, and induced movement therapy, which to a certain extent reflect the development trend and frontier research direction of this field.<br><br>CONCLUSION: This study comprehensively and visually presents the extensive and in-depth literature resources of BCI technology in stroke research in the form of knowledge maps, which facilitates scholars to gain a more convenient understanding of the development and prospects in this field, thereby promoting further research work.}, }
@article {pmid37732253, year = {2023}, author = {Kosnoff, J and Yu, K and Liu, C and He, B}, title = {Transcranial Focused Ultrasound to V5 Enhances Human Visual Motion Brain-Computer Interface by Modulating Feature-Based Attention.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.09.04.556252}, pmid = {37732253}, abstract = {Paralysis affects roughly 1 in 50 Americans. While there is no cure for the condition, brain-computer interfaces (BCI) can allow users to control a device with their mind, bypassing the paralyzed region. Non-invasive BCIs still have high error rates, which is hypothesized to be reduced with concurrent targeted neuromodulation. This study examines whether transcranial focused ultrasound (tFUS) modulation can improve BCI outcomes, and what the underlying mechanism of action might be through high-density electroencephalography (EEG)-based source imaging (ESI) analyses. V5-targeted tFUS significantly reduced the error for the BCI speller task. ESI analyses showed significantly increased theta activity in the tFUS condition at both V5 and downstream the dorsal visual processing pathway. Correlation analysis indicates that the dorsal processing pathway connection was preserved during tFUS stimulation, whereas extraneous connections were severed. These results suggest that V5-targeted tFUS' mechanism of action is to raise the brain's feature-based attention to visual motion.}, }
@article {pmid37731047, year = {2023}, author = {Ozcelik, F and VanRullen, R}, title = {Natural scene reconstruction from fMRI signals using generative latent diffusion.}, journal = {Scientific reports}, volume = {13}, number = {1}, pages = {15666}, pmid = {37731047}, issn = {2045-2322}, support = {ANR-18-CE37-0007-01//Agence Nationale de la Recherche (French National Research Agency)/ ; ANR-19-PI3A-0004//Agence Nationale de la Recherche (French National Research Agency)/ ; }, mesh = {Magnetic Resonance Imaging ; Benchmarking ; *Brachytherapy ; Brain/diagnostic imaging ; *Brain-Computer Interfaces ; }, abstract = {In neural decoding research, one of the most intriguing topics is the reconstruction of perceived natural images based on fMRI signals. Previous studies have succeeded in re-creating different aspects of the visuals, such as low-level properties (shape, texture, layout) or high-level features (category of objects, descriptive semantics of scenes) but have typically failed to reconstruct these properties together for complex scene images. Generative AI has recently made a leap forward with latent diffusion models capable of generating high-complexity images. Here, we investigate how to take advantage of this innovative technology for brain decoding. We present a two-stage scene reconstruction framework called "Brain-Diffuser". In the first stage, starting from fMRI signals, we reconstruct images that capture low-level properties and overall layout using a VDVAE (Very Deep Variational Autoencoder) model. In the second stage, we use the image-to-image framework of a latent diffusion model (Versatile Diffusion) conditioned on predicted multimodal (text and visual) features, to generate final reconstructed images. On the publicly available Natural Scenes Dataset benchmark, our method outperforms previous models both qualitatively and quantitatively. When applied to synthetic fMRI patterns generated from individual ROI (region-of-interest) masks, our trained model creates compelling "ROI-optimal" scenes consistent with neuroscientific knowledge. Thus, the proposed methodology can have an impact on both applied (e.g. brain-computer interface) and fundamental neuroscience.}, }
@article {pmid37728715, year = {2023}, author = {Wang, W and Li, B and Wang, H and Wang, X and Qin, Y and Shi, X and Liu, S}, title = {EEG-FMCNN: A fusion multi-branch 1D convolutional neural network for EEG-based motor imagery classification.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {37728715}, issn = {1741-0444}, abstract = {Motor imagery (MI) electroencephalogram (EEG) signal is recognized as a promising paradigm for brain-computer interface (BCI) systems and has been extensively employed in various BCI applications, including assisting disabled individuals, controlling devices and environments, and enhancing human capabilities. The high-performance decoding capability of MI-EEG signals is a key issue that impacts the development of the industry. However, decoding MI-EEG signals is challenging due to the low signal-to-noise ratio and inter-subject variability. In response to the aforementioned core problems, this paper proposes a novel end-to-end network, a fusion multi-branch 1D convolutional neural network (EEG-FMCNN), to decode MI-EEG signals without pre-processing. The utilization of multi-branch 1D convolution not only exhibits a certain level of noise tolerance but also addresses the issue of inter-subject variability to some extent. This is attributed to the ability of multi-branch architectures to capture information from different frequency bands, enabling the establishment of optimal convolutional scales and depths. Furthermore, we incorporate 1D squeeze-and-excitation (SE) blocks and shortcut connections at appropriate locations to further enhance the generalization and robustness of the network. In the BCI Competition IV-2a dataset, our proposed model has obtained good experimental results, achieving accuracies of 78.82% and 68.41% for subject-dependent and subject-independent modes, respectively. In addition, extensive ablative experiments and fine-tuning experiments were conducted, resulting in a notable 7% improvement in the average performance of the network, which holds significant implications for the generalization and application of the network.}, }
@article {pmid37727590, year = {2023}, author = {Asanza, V and Lorente-Leyva, LL and Peluffo-Ordóñez, DH and Montoya, D and Gonzalez, K}, title = {MILimbEEG: A dataset of EEG signals related to upper and lower limb execution of motor and motor imagery tasks.}, journal = {Data in brief}, volume = {50}, number = {}, pages = {109540}, pmid = {37727590}, issn = {2352-3409}, abstract = {Biomedical Electroencephalography (EEG) signals are the result of measuring the electric potential difference generated on the scalp surface by neural activity corresponding to each brain area. Accurate and automatic detection of neural activity from the upper and lower limbs using EEG may be helpful in rehabilitating people suffering from mobility limitations or disabilities. This article presents a dataset containing 7440 CSV files from 60 test subjects during motor and motor imagery tasks. The motor and motor imagery tasks performed by the test subjects were: Closing Left Hand (CLH), Closing Right Hand (CRH), Dorsal flexion of Left Foot (DLF), Plantar flexion of Left Foot (PLF), Dorsal flexion of Right Foot (DRF), Plantar flexion of Right Foot (PRF) and Resting in between tasks (Rest). The volunteers were recruited from research colleagues at ESPOL and patients at the Luis Vernaza Hospital in Guayaquil, Ecuador. Each CSV file has 501 rows, of which the first one lists the electrodes from 0 to 15, and the remaining 500 rows correspond to 500 data recorded during the task is performed due to sample rate. In addition, each file has 17 columns, of which the first one indicates the sampling number and the remaining 16 columns represent 16 surface EEG electrodes. As a data recording equipment, the OpenBCI is used in a monopolar setup with a sampling rate of 125 Hz. This work includes statistical measures about the demographic information of all recruited test subjects. Finally, we outline the experimental methodology used to record EEG signals during upper and lower limb task execution. This dataset is called MILimbEEG and contains microvolt (µV) EEG signals acquired during motor and motor imagery tasks. The collected data may facilitate the evaluation of EEG signal detection and classification models dedicated to task recognition.}, }
@article {pmid37725740, year = {2023}, author = {Ju, C and Guan, C}, title = {Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective From the Time-Frequency Analysis.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2023.3307470}, pmid = {37725740}, issn = {2162-2388}, abstract = {The motor imagery (MI) classification has been a prominent research topic in brain-computer interfaces (BCIs) based on electroencephalography (EEG). Over the past few decades, the performance of MI-EEG classifiers has seen gradual enhancement. In this study, we amplify the geometric deep-learning-based MI-EEG classifiers from the perspective of time-frequency analysis, introducing a new architecture called Graph-CSPNet. We refer to this category of classifiers as Geometric Classifiers, highlighting their foundation in differential geometry stemming from EEG spatial covariance matrices. Graph-CSPNet utilizes novel manifold-valued graph convolutional techniques to capture the EEG features in the time-frequency domain, offering heightened flexibility in signal segmentation for capturing localized fluctuations. To evaluate the effectiveness of Graph-CSPNet, we employ five commonly used publicly available MI-EEG datasets, achieving near-optimal classification accuracies in nine out of 11 scenarios. The Python repository can be found at https://github.com/GeometricBCI/Tensor-CSPNet-and-Graph-CSPNet.}, }
@article {pmid37725735, year = {2023}, author = {Yang, S and Wang, H and Pang, Y and Azghadi, MR and Linares-Barranco, B}, title = {NADOL: Neuromorphic Architecture for Spike-driven Online Learning By Dendrites.}, journal = {IEEE transactions on biomedical circuits and systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBCAS.2023.3316968}, pmid = {37725735}, issn = {1940-9990}, abstract = {Biologically plausible learning with neuronal dendrites is a promising perspective to improve the spike-driven learning capability by introducing dendritic processing as an additional hyperparameter. Neuromorphic computing is an effective and essential solution towards spike-based machine intelligence and neural learning systems. However, on-line learning capability for neuromorphic models is still an open challenge. In this study a novel neuromorphic architecture with dendritic on-line learning (NADOL) is presented, which is a novel efficient methodology for brain-inspired intelligence on embedded hardware. With the feature of distributed processing using spiking neural network, NADOL can cut down the power consumption and enhance the learning efficiency and convergence speed. A detailed analysis for NADOL is presented, which demonstrates the effects of different conditions on learning capabilities, including neuron number in hidden layer, dendritic segregation parameters, feedback connection, and connection sparseness with various levels of amplification. Piecewise linear approximation approach is used to cut down the computational resource cost. The experimental results demonstrate a remarkable learning capability that surpasses other solutions, with NADOL exhibiting superior performance over the GPU platform in dendritic learning. This study's applicability extends across diverse domains, including the Internet of Things, robotic control, and brain-machine interfaces. Moreover, it signifies a pivotal step in bridging the gap between artificial intelligence and neuroscience through the introduction of an innovative neuromorphic paradigm.}, }
@article {pmid37724211, year = {2023}, author = {Ayoub, M and Ballout, AA and Zayek, RA and Ayoub, NF}, title = {Mind + Machine: ChatGPT as a Basic Clinical Decisions Support Tool.}, journal = {Cureus}, volume = {15}, number = {8}, pages = {e43690}, pmid = {37724211}, issn = {2168-8184}, abstract = {Background Generative artificial intelligence (AI) has integrated into various industries as it has demonstrated enormous potential in automating elaborate processes and enhancing complex decision-making. The ability of these chatbots to critically triage, diagnose, and manage complex medical conditions, remains unknown and requires further research. Objective This cross-sectional study sought to quantitatively analyze the appropriateness of ChatGPT (OpenAI, San Francisco, CA, US) in its ability to triage, synthesize differential diagnoses, and generate treatment plans for nine diverse but common clinical scenarios. Methods Various common clinical scenarios were developed. Each was input into ChatGPT, and the chatbot was asked to develop diagnostic and treatment plans. Five practicing physicians independently scored ChatGPT's responses to the clinical scenarios. Results The average overall score for the triage ranking was 4.2 (SD 0.7). The lowest overall score was for the completeness of the differential diagnosis at 4.1 (0.5). The highest overall scores were seen with the accuracy of the differential diagnosis, initial treatment plan, and overall usefulness of the response (all with an average score of 4.4). Variance among physician scores ranged from 0.24 for accuracy of the differential diagnosis to 0.49 for appropriateness of triage ranking. Discussion ChatGPT has the potential to augment clinical decision-making. More extensive research, however, is needed to ensure accuracy and appropriate recommendations are provided.}, }
@article {pmid37723506, year = {2023}, author = {Khanal, S and Miani, C and Finne, E and Zielke, J and Boeckmann, M}, title = {Effectiveness of behavior change interventions for smoking cessation among expectant and new fathers: findings from a systematic review.}, journal = {BMC public health}, volume = {23}, number = {1}, pages = {1812}, pmid = {37723506}, issn = {1471-2458}, mesh = {Male ; Pregnancy ; Female ; Humans ; *Smoking Cessation ; Behavior Therapy ; Databases, Factual ; Language ; Fathers ; Randomized Controlled Trials as Topic ; }, abstract = {BACKGROUND: Smoking cessation during pregnancy and the postpartum period by both women and their partners offers multiple health benefits. However, compared to pregnant/postpartum women, their partners are less likely to actively seek smoking cessation services. There is an increased recognition about the importance of tailored approaches to smoking cessation for expectant and new fathers. While Behavior Change Interventions (BCIs) are a promising approach for smoking cessation interventions, evidence on effectiveness exclusively among expectant and new fathers are fragmented and does not allow for many firm conclusions to be drawn.<br><br>METHODS: We conducted a systematic review on effectiveness of BCIs on smoking cessation outcomes of expectant and new fathers both through individual and/or couple-based interventions. Peer reviewed articles were identified from eight databases without any date or language restriction.Two independent reviewers screened studies for relevance, assessed methodological quality of relevant studies, and extracted data from studies using a predeveloped data extraction sheet.<br><br>RESULTS: We retrieved 1222 studies, of which 39 were considered for full text screening after reviewing the titles and abstracts. An additional eight studies were identified from reviewing the reference list of review articles picked up by the databases search. A total of nine Randomised Control Trials were included in the study. Six studies targeted expectant/new fathers, two targeted couples and one primarily targeted women with an intervention component to men. While the follow-up measurements for men varied across studies, the majority reported biochemically verified quit rates at 6&#xa0;months. Most of the interventions showed positive effects on cessation outcomes. BCI were heterogenous across studies. Findings are suggestive of gender targeted interventions being more likely to have positive cessation outcomes.<br><br>CONCLUSIONS: This systematic review found limited evidence supporting the effectiveness of BCI among expectant and new fathers, although the majority of studies show positive effects of these interventions on smoking cessation outcomes. There remains a need for more research targeted at expectant and new fathers. Further, there is a need to identify how smoking cessation service delivery can better address the needs of (all) gender(s) during pregnancy.}, }
@article {pmid37683652, year = {2023}, author = {Tang, J and Xi, X and Wang, T and Wang, J and Li, L and Lü, Z}, title = {Analysis of corticomuscular-cortical functional network based on time-delayed maximal information spectral coefficient.}, journal = {Journal of neural engineering}, volume = {20}, number = {5}, pages = {}, doi = {10.1088/1741-2552/acf7f7}, pmid = {37683652}, issn = {1741-2552}, abstract = {Objective. The study of brain networks has become an influential tool for investigating post-stroke brain function. However, studies on the dynamics of cortical networks associated with muscle activity are limited. This is crucial for elucidating the altered coordination patterns in the post-stroke motor control system.Approach. In this study, we introduced the time-delayed maximal information spectral coefficient (TDMISC) method to assess the local frequency band characteristics (alpha, beta, and gamma bands) of functional corticomuscular coupling (FCMC) and cortico-cortical network parameters. We validated the effectiveness of TDMISC using a unidirectionally coupled Hénon maps model and a neural mass model.Main result. A grip task with 25% of maximum voluntary contraction was designed, and simulation results demonstrated that TDMISC accurately characterizes signals' local frequency band and directional properties. In the gamma band, the affected side showed significantly strong FCMC in the ascending direction. However, in the beta band, the affected side exhibited significantly weak FCMC in all directions. For the cortico-cortical network parameters, the affected side showed a lower clustering coefficient than the unaffected side in all frequency bands. Additionally, the affected side exhibited a longer shortest path length than the unaffected side in all frequency bands. In all frequency bands, the unaffected motor cortex in the stroke group exerted inhibitory effects on the affected motor cortex, the parietal associative areas, and the somatosensory cortices.Significance. These results provide meaningful insights into neural mechanisms underlying motor dysfunction.}, }
@article {pmid37721299, year = {2024}, author = {Guo, Y and Sun, L and Zhong, W and Zhang, N and Zhao, Z and Tian, W}, title = {Artificial intelligence-assisted repair of peripheral nerve injury: a new research hotspot and associated challenges.}, journal = {Neural regeneration research}, volume = {19}, number = {3}, pages = {663-670}, doi = {10.4103/1673-5374.380909}, pmid = {37721299}, issn = {1673-5374}, abstract = {Artificial intelligence can be indirectly applied to the repair of peripheral nerve injury. Specifically, it can be used to analyze and process data regarding peripheral nerve injury and repair, while study findings on peripheral nerve injury and repair can provide valuable data to enrich artificial intelligence algorithms. To investigate advances in the use of artificial intelligence in the diagnosis, rehabilitation, and scientific examination of peripheral nerve injury, we used CiteSpace and VOSviewer software to analyze the relevant literature included in the Web of Science from 1994-2023. We identified the following research hotspots in peripheral nerve injury and repair: (1) diagnosis, classification, and prognostic assessment of peripheral nerve injury using neuroimaging and artificial intelligence techniques, such as corneal confocal microscopy and coherent anti-Stokes Raman spectroscopy; (2) motion control and rehabilitation following peripheral nerve injury using artificial neural networks and machine learning algorithms, such as wearable devices and assisted wheelchair systems; (3) improving the accuracy and effectiveness of peripheral nerve electrical stimulation therapy using artificial intelligence techniques combined with deep learning, such as implantable peripheral nerve interfaces; (4) the application of artificial intelligence technology to brain-machine interfaces for disabled patients and those with reduced mobility, enabling them to control devices such as networked hand prostheses; (5) artificial intelligence robots that can replace doctors in certain procedures during surgery or rehabilitation, thereby reducing surgical risk and complications, and facilitating postoperative recovery. Although artificial intelligence has shown many benefits and potential applications in peripheral nerve injury and repair, there are some limitations to this technology, such as the consequences of missing or imbalanced data, low data accuracy and reproducibility, and ethical issues (e.g., privacy, data security, research transparency). Future research should address the issue of data collection, as large-scale, high-quality clinical datasets are required to establish effective artificial intelligence models. Multimodal data processing is also necessary, along with interdisciplinary collaboration, medical-industrial integration, and multicenter, large-sample clinical studies.}, }
@article {pmid37719745, year = {2023}, author = {Ding, Y and Guo, K and Wang, X and Chen, M and Li, X and Wu, Y}, title = {Brain functional connectivity and network characteristics changes after vagus nerve stimulation in patients with refractory epilepsy.}, journal = {Translational neuroscience}, volume = {14}, number = {1}, pages = {20220308}, pmid = {37719745}, issn = {2081-3856}, abstract = {OBJECTIVE: This study aims to investigate the impact of vagus nerve stimulation (VNS) on the connectivity and small-world metrics of brain functional networks during seizure periods.<br><br>METHODS: Ten refractory epilepsy patients underwent video encephalographic monitoring before and after VNS treatment. The 2-min electroencephalogram segment containing the ictal was selected for each participant, resulting in a total of 20&#x2009;min of seizure data. The weighted phase lag index (wPLI) and small-world metrics were calculated for the whole frequency band and different frequency bands (delta, theta, alpha, beta, and gamma). Finally, the relevant metrics were statistically analyzed, and the false discovery rate was used to correct for differences after multiple comparisons.<br><br>RESULTS: In the whole band, the wPLI was notably enhanced, and the network metrics, including degree (D), clustering coefficient (CC), and global efficiency (GE), increased, while characteristic path length (CPL) decreased (P < 0.01). In different frequency bands, the wPLI between the parieto-occipital and frontal regions was significantly strengthened in the delta and beta bands, while the wPLI within the frontal region and between the frontal and parieto-occipital regions were significantly reduced in the beta and gamma bands (P < 0.01). In the low-frequency band (<13&#x2009;Hz), the small-world metrics demonstrated significantly increased CC, D, and GE, with a significantly decreased CPL, indicating a more efficient network organization. In contrast, in the gamma band, the GE decreased, and the CPL increased, suggesting a shift toward less efficient network organization.<br><br>CONCLUSION: VNS treatment can significantly change the wPLI and small-world metrics. These findings contribute to a deeper understanding of the impact of VNS therapy on brain networks and provide objective indicators for evaluating the efficacy of VNS.}, }
@article {pmid37717810, year = {2023}, author = {Mahemuti, Y and Kadeer, K and Su, R and Abula, A and Aili, Y and Maimaiti, A and Abulaiti, S and Maimaitituerxun, M and Miao, T and Jiang, S and Axier, A and Aisha, M and Wang, Y and Cheng, X}, title = {TSPO exacerbates acute cerebral ischemia/reperfusion injury by inducing autophagy dysfunction.}, journal = {Experimental neurology}, volume = {}, number = {}, pages = {114542}, doi = {10.1016/j.expneurol.2023.114542}, pmid = {37717810}, issn = {1090-2430}, abstract = {Autophagy is considered a double-edged sword, with a role in the regulation of the pathophysiological processes of the central nervous system (CNS) after cerebral ischemia-reperfusion injury (CIRI). The 18-kDa translocator protein (TSPO) is a highly conserved protein, with its expression level in the nervous system closely associated with the regulation of pathophysiological processes. In addition, the ligand of TSPO reduces neuroinflammation in brain diseases, but the potential role of TSPO in CIRI is largely undiscovered. On this basis, we investigated whether TSPO regulates neuroinflammatory response by affecting autophagy in microglia. In our study, increased expression of TSPO was detected in rat brain tissues with transient middle cerebral artery occlusion (tMCAO) and in BV2 microglial cells exposed to oxygen-glucose deprivation or reoxygenation (OGD/R) treatment, respectively. In addition, we confirmed that autophagy was over-activated during CIRI by increased expression of autophagy activation related proteins with Beclin-1 and LC3B, while the expression of p62 was decreased. The degradation process of autophagy was inhibited, while the expression levels of LAMP-1 and Cathepsin-D were significantly reduced. Results of confocal laser microscopy and transmission electron microscopy (TEM) indicated that autophagy flux was disordered. In contrast, inhibition of TSPO prevented autophagy over-activation both in vivo and in vitro. Interestingly, suppression of TSPO alleviated nerve cell damage by reducing reactive oxygen species (ROS) and pro-inflammatory factors, including TNF-α and IL-6 in microglia cells. In summary, these results indicated that TSPO might affect CIRI by mediating autophagy dysfunction and thus might serve as a potential target for ischemic stroke treatment.}, }
@article {pmid37717506, year = {2023}, author = {Yang, X and Zhu, HR and Tao, YJ and Deng, RH and Tao, SW and Meng, YJ and Wang, HY and Li, XJ and Wei, W and Yu, H and Liang, R and Wang, Q and Deng, W and Zhao, LS and Ma, XH and Li, ML and Xu, JJ and Li, J and Liu, YS and Tang, Z and Du, XD and Coid, JW and Greenshaw, AJ and Li, T and Guo, WJ}, title = {Multivariate classification based on large-scale brain networks during early abstinence predicted lapse among male detoxified alcohol-dependent patients.}, journal = {Asian journal of psychiatry}, volume = {89}, number = {}, pages = {103767}, doi = {10.1016/j.ajp.2023.103767}, pmid = {37717506}, issn = {1876-2026}, abstract = {Identifying biomarkers to predict lapse of alcohol-dependence (AD) is essential for treatment and prevention strategies, but remains remarkably challenging. With an aim to identify neuroimaging features for predicting AD lapse, 66 male AD patients during early-abstinence (baseline) after hospitalized detoxification underwent resting-state functional magnetic resonance imaging and were then followed-up for 6 months. The relevance-vector-machine (RVM) analysis on baseline large-scale brain networks yielded an elegant model for differentiating relapsing patients (n = 38) from abstainers, with the area under the curve of 0.912 and the accuracy by leave-one-out cross-validation of 0.833. This model captured key information about neuro-connectome biomarkers for predicting AD lapse.}, }
@article {pmid37715921, year = {2023}, author = {Fan, J and Xu, H}, title = {Serotonin: A Bridge for Infant-mother Bonding.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {37715921}, issn = {1995-8218}, }
@article {pmid37714145, year = {2023}, author = {Tian, L and Zhao, T and Dong, L and Liu, Q and Zheng, Y}, title = {Passive array micro-magnetic stimulation device based on multi-carrier wireless flexible control for magnetic neuromodulation.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acfa23}, pmid = {37714145}, issn = {1741-2552}, abstract = {The passive micro-magnetic stimulation (µMS) devices typically consist of an external transmitting coil and a single internal micro-coil, which enables a point-to-point energy supply from the external coil to the internal coil and the realization of magnetic neuromodulation via wireless energy transmission. The internal array of micro coils can achieve multi-target stimulation without movement, which improves the focus and effectiveness of magnetic stimulations. However, achieving a free selection of an appropriate external coil to deliver energy to a particular internal array of micro-coils for multiple stimulation targets has been challenging. To address this challenge, this study uses a multi-carrier modulation technique to transmit the energy of the external coil. Approach: In this study, a theoretical model of a multi-carrier resonant compensation network for the array µMS is established based on the principle of magnetically coupled resonance. The resonant frequency coupling parameter corresponding to each micro-coil of the array µMS is determined, and the magnetic field interference between the external coil and its non-resonant micro-coils is eliminated. Therefore, an effective magnetic stimulation threshold for a micro-coil corresponding to the target is determined, and wireless free control of the internal micro-coil array is achieved by using an external transmitting coil. Main results: The passive µMS array model is designed using a multi-carrier wireless modulation method, and its synergistic modulation of the magnetic stimulation of synaptic plasticity Long-term Potentiation in multiple hippocampal regions is investigated using hippocampal isolated brain slices. Significance: The results presented in this study could provide theoretical and experimental bases for implantable micro-magnetic device-targeted therapy, introducing an efficient method for diagnosis and treatment of neurological diseases and providing innovative ideas for in-depth application of micro-magnetic stimulation in the neuroscience field. .}, }
@article {pmid37714143, year = {2023}, author = {Fadli, RAA and Yamanouchi, Y and Jovanovic, LI and Popovic, MR and Marquez-Chin, C and Nomura, T and Milosevic, M}, title = {Effectiveness of motor and prefrontal cortical areas for brain-controlled functional electrical stimulation neuromodulation.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acfa22}, pmid = {37714143}, issn = {1741-2552}, abstract = {Brain-computer interface (BCI)-controlled functional electrical stimulation (FES) could excite the central nervous system to enhance upper limb motor recovery. Our current study assessed the effectiveness of motor and prefrontal cortical activity-based BCI-FES to help elucidate the underlying neuromodulation mechanisms of this neurorehabilitation approach. Approach: The primary motor cortex (M1) and prefrontal cortex (PFC) BCI-FES interventions were performed for 25 min on separate days with twelve non-disabled participants. During the interventions, a single electrode from the contralateral M1 or PFC was used to detect event-related desynchronization (ERD) in the calibrated frequency range. If the BCI system detected ERD within 15 s of motor imagery, FES activated wrist extensor muscles. Otherwise, if the BCI system did not detect ERD within 15 s, a subsequent trial was initiated without FES. To evaluate neuromodulation effects, corticospinal excitability was assessed using single-pulse transcranial magnetic stimulation, and cortical excitability was assessed by motor imagery ERD and resting-state functional connectivity before, immediately, 30 min, and 60 min after each intervention. Main results: M1 and PFC BCI-FES interventions had similar success rates of approximately 80%, while the M1 intervention was faster in detecting ERD activity. Consequently, only the M1 intervention effectively elicited corticospinal excitability changes for at least 60 min around the targeted cortical area in the M1, suggesting a degree of spatial localization. However, cortical excitability measures did not indicate changes after either M1 or PFC BCI-FES. Significance: Neural mechanisms underlying the effectiveness of BCI-FES neuromodulation may be attributed to the M1 direct corticospinal projections and/or the closer timing between ERD detection and FES, which likely enhanced Hebbian-like plasticity by synchronizing cortical activation detected by the BCI system with the sensory nerve activation and movement related reafference elicited by FES.}, }
@article {pmid37713229, year = {2023}, author = {Ma, R and Chen, YF and Jiang, YC and Zhang, M}, title = {A New Compound-limbs Paradigm: Integrating upper-limb swing improves lower-limb stepping intention decoding from EEG.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3315717}, pmid = {37713229}, issn = {1558-0210}, abstract = {Brain-computer interface (BCI) systems based on spontaneous electroencephalography (EEG) hold the promise to implement human voluntary control of lower-extremity powered exoskeletons. However, current EEG-BCI paradigms do not consider the cooperation of upper and lower limbs during walking, which is inconsistent with natural human stepping patterns. To deal with this problem, this study proposed a stepping-matched human EEG-BCI paradigm that involved actions of both unilateral lower and contralateral upper limbs (also referred to as compound-limbs movement). Experiments were conducted in motor execution (ME) and motor imagery (MI) conditions to validate the feasibility. Common spatial pattern (CSP) proposed subject-specific CSP (SSCSP), and filter-bank CSP (FBCSP) methods were applied for feature extraction, respectively. The best average classification results based on SSCSP indicated that the accuracies of compound-limbs paradigms in ME and MI conditions achieved 89.02% ± 12.84% and 73.70% ± 12.47%, respectively. Although they were 2.03% and 5.68% lower than those of the single-upper-limb mode that does not match human stepping patterns, they were 24.30% and 11.02% higher than those of the single-lower-limb mode. These findings indicated that the proposed compound-limbs EEG-BCI paradigm is feasible for decoding human stepping intention and thus provides a potential way for natural human control of walking assistance devices.}, }
@article {pmid37711224, year = {2023}, author = {Alonso-Valerdi, LM}, title = {Editorial: Improving decoding of neuroinformation: towards the diversity of neural engineering applications.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1270696}, pmid = {37711224}, issn = {1662-5161}, }
@article {pmid37707990, year = {2023}, author = {}, title = {Erratum: Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke.}, journal = {Journal of visualized experiments : JoVE}, volume = {}, number = {199}, pages = {}, doi = {10.3791/6572}, pmid = {37707990}, issn = {1940-087X}, abstract = {An erratum was issued for: Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke. The Authors section was updated from: Yongchun Jiang[1,2,3] Junxiao Yin[4] Biyi Zhao[1,3,5] Yajie Zhang[1,3] Tingting Peng[1,3] Wanqi Zhuang[1,3] Siqing Wang[1,3] Siqi Huang[1,3] Meilian Zhong[1,2,3] Yanni Zhang[1,3] Guibing Tang[1,3] Bingchi Shen[6] Haining Ou[1,3] Yuxin Zheng[2,3] Qiang Lin[2,3] [1]Guangzhou Medical University [2]Department of Rehabilitation Medicine, The Seventh Affiliated Hospital of Sun Yat-sen University [3]Department of Rehabilitation Medicine, The Fifth Affiliated Hospital of Guangzhou Medical University [4]Clinical Medical College of Acupuncture and Rehabilitation, Guangzhou University of Traditional Chinese Medicine [5]School of Traditional Chinese Medicine, Jinan University [6]Department of Stomatology, Second Clinical Medical College, Dongguan Campus of Guangdong Medical University to: Yongchun Jiang[1,2,3] Junxiao Yin[4] Biyi Zhao[1,3,5] Yajie Zhang[1,3] Tingting Peng[1,3] Wanqi Zhuang[1,3] Siqing Wang[1,3] Siqi Huang[1,3] Meilian Zhong[1,3] Yanni Zhang[1,3] Guibing Tang[1,3] Bingchi Shen[6] Haining Ou[1,3] Yuxin Zheng[2,3] Qiang Lin[2,3] [1]Guangzhou Medical University [2]Department of Rehabilitation Medicine, The Seventh Affiliated Hospital of Sun Yat-sen University [3]Department of Rehabilitation Medicine, The Fifth Affiliated Hospital of Guangzhou Medical University [4]Clinical Medical College of Acupuncture and Rehabilitation, Guangzhou University of Traditional Chinese Medicine [5]School of Traditional Chinese Medicine, Jinan University [6]Department of Stomatology, Second Clinical Medical College, Dongguan Campus of Guangdong Medical University.}, }
@article {pmid37707696, year = {2023}, author = {Azizi, H and Fakhari, A and Farahbakhsh, M and Davtalab Esmaeili, E and Chattu, VK and Ali Asghari, N and Nazemipour, M and Mansournia, MA}, title = {Prevention of Re-attempt Suicide Through Brief Contact Interventions: A Systematic Review, Meta-analysis, and Meta-regression of Randomized Controlled Trials.}, journal = {Journal of prevention (2022)}, volume = {}, number = {}, pages = {}, pmid = {37707696}, issn = {2731-5541}, abstract = {Brief contact intervention (BCI) is a low-cost intervention to prevent re-attempt suicide. This meta-analysis and meta-regression study aimed to evaluate the effect of BCI on re-attempt prevention following suicide attempts (SAs). We systematically searched using defined keywords in MEDLINE, Embase, and Scopus up to April, 2023. All randomized controlled trials (RCTs) were eligible for inclusion after quality assessment. Random-effects model and subgroup analysis were used to estimate pooled risk difference (RD) and risk ratio (RR) between BCI and re-attempt prevention with 95% confidence intervals (CIs). Meta-regression analysis was carried out to explore the potential sources of heterogeneity. The pooled estimates were (RD = 4%; 95% CI 2-6%); and (RR = 0.62; 95% CI 0.48-0.77). Subgroup analysis demonstrated that more than 12 months intervention (RR = 0.46; 95% CI 0.10-0.82) versus 12 months or less (RR = 0.67; 95% CI 0.54-0.80) increased the effectiveness of BCI on re-attempt suicide reduction. Meta-regression analysis explored that BCI time (more than 12 months), BCI type, age, and female sex were the potential sources of the heterogeneity. The meta-analysis indicated that BCI could be a valuable strategy to prevent suicide re-attempts. BCI could be utilized within suicide prevention strategies as a surveillance component of mental health since BCI requires low-cost and low-educated healthcare providers.}, }
@article {pmid37706481, year = {2023}, author = {Horner, S and Burleigh, L and Traylor, Z and Greening, SG}, title = {Looking on the bright side: the impact of ambivalent images on emotion regulation choice.}, journal = {Cognition &amp; emotion}, volume = {}, number = {}, pages = {1-17}, doi = {10.1080/02699931.2023.2256056}, pmid = {37706481}, issn = {1464-0600}, abstract = {Previous research has found that people choose to reappraise low intensity images more often than high intensity images. However, this research does not account for image ambivalence, which is presence of both positive and negative cues in a stimulus. The purpose of this research was to determine differences in ambivalence in high intensity and low intensity images used in previous research (experiments 1-2), and if ambivalence played a role in emotion regulation choice in addition to intensity (experiments 3-4). Experiments 1 and 2 found that the low intensity images were more ambivalent than the high intensity images. Experiment 2 further found a positive relationship between ambivalence of an image and reappraisal affordances. Experiments 3 and 4 found that people chose to reappraise ambivalent images more often than non-ambivalent images, and they also chose to reappraise low intensity images more often than high intensity images. These experiments support the idea that ambivalence is a factor in emotion regulation choice. Future research should consider the impact ambivalent stimuli have on emotion regulation, including the potential for leveraging ambivalent stimuli to improve one's emotion regulation ability.}, }
@article {pmid37706155, year = {2023}, author = {Liu, C and You, J and Wang, K and Zhang, S and Huang, Y and Xu, M and Ming, D}, title = {Decoding the EEG patterns induced by sequential finger movement for brain-computer interfaces.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1180471}, pmid = {37706155}, issn = {1662-4548}, abstract = {OBJECTIVE: In recent years, motor imagery-based brain-computer interfaces (MI-BCIs) have developed rapidly due to their great potential in neurological rehabilitation. However, the controllable instruction set limits its application in daily life. To extend the instruction set, we proposed a novel movement-intention encoding paradigm based on sequential finger movement.<br><br>APPROACH: Ten subjects participated in the offline experiment. During the experiment, they were required to press a key sequentially [i.e., Left&#x2192;Left (LL), Right&#x2192;Right (RR), Left&#x2192;Right (LR), and Right&#x2192;Left (RL)] using the left or right index finger at about 1&#x2009;s intervals under an auditory prompt of 1&#x2009;Hz. The movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were used to investigate the electroencephalography (EEG) variation induced by the sequential finger movement tasks. Twelve subjects participated in an online experiment to verify the feasibility of the proposed paradigm.<br><br>MAIN RESULTS: As a result, both the MRCP and ERD features showed the specific temporal-spatial EEG patterns of different sequential finger movement tasks. For the offline experiment, the average classification accuracy of the four tasks was 71.69%, with the highest accuracy of 79.26%. For the online experiment, the average accuracies were 83.33% and 82.71% for LL-versus-RR and LR-versus-RL, respectively.<br><br>SIGNIFICANCE: This paper demonstrated the feasibility of the proposed sequential finger movement paradigm through offline and online experiments. This study would be helpful for optimizing the encoding method of motor-related EEG information and providing a promising approach to extending the instruction set of the movement intention-based BCIs.}, }
@article {pmid37700746, year = {2023}, author = {Fan, C and Yang, B and Li, X and Zan, P}, title = {Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1250991}, pmid = {37700746}, issn = {1662-4548}, abstract = {Recently, convolutional neural networks (CNNs) have been widely applied in brain-computer interface (BCI) based on electroencephalogram (EEG) signals. Due to the subject-specific nature of EEG signal patterns and the multi-dimensionality of EEG features, it is necessary to employ appropriate feature representation methods to enhance the decoding accuracy of EEG. In this study, we proposed a method for representing EEG temporal, frequency, and phase features, aiming to preserve the multi-domain information of EEG signals. Specifically, we generated EEG temporal segments using a sliding window strategy. Then, temporal, frequency, and phase features were extracted from different temporal segments and stacked into 3D feature maps, namely temporal-frequency-phase features (TFPF). Furthermore, we designed a compact 3D-CNN model to extract these multi-domain features efficiently. Considering the inter-individual variability in EEG data, we conducted individual testing for each subject. The proposed model achieved an average accuracy of 89.86, 78.85, and 63.55% for 2-class, 3-class, and 4-class motor imagery (MI) classification tasks, respectively, on the PhysioNet dataset. On the GigaDB dataset, the average accuracy for 2-class MI classification was 91.91%. For the comparison between MI and real movement (ME) tasks, the average accuracy for the 2-class were 87.66 and 80.13% on the PhysioNet and GigaDB datasets, respectively. Overall, the method presented in this paper have obtained good results in MI/ME tasks and have a good application prospect in the development of BCI systems based on MI/ME.}, }
@article {pmid37698960, year = {2023}, author = {Feng, X and Feng, X and Qin, B and Liu, T}, title = {Aligning Semantic in Brain and Language: A Curriculum Contrastive Method for Electroencephalography-to-Text Generation.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3314642}, pmid = {37698960}, issn = {1558-0210}, abstract = {Electroencephalography-to-Text generation (EEG-to-Text), which aims to directly generate natural text from EEG signals has drawn increasing attention in recent years due to the enormous potential for Brain-computer interfaces. However, the remarkable discrepancy between the subject-dependent EEG representation and the semantic-dependent text representation poses a great challenge to this task. To mitigate this, we devise a Curriculum Semantic-aware Contrastive Learning strategy (C-SCL), which effectively recalibrates the subject-dependent EEG representation to the semantic-dependent EEG representation, thereby reducing the discrepancy. Specifically, our C-SCL pulls semantically similar EEG representations together while pushing apart dissimilar ones. Besides, in order to introduce more meaningful contrastive pairs, we carefully employ curriculum learning to not only craft meaningful contrastive pairs but also make the learning progressively. We conduct extensive experiments on the ZuCo benchmark and our method combined with diverse models and architectures shows stable improvements across three types of metrics while achieving the new state-of-the-art. Further investigation proves not only its superiority in both the single-subject and low-resource settings but also its robust generalizability in the zero-shot setting.}, }
@article {pmid37697027, year = {2023}, author = {Marín-Medina, DS and Arenas-Vargas, PA and Arias-Botero, JC and Gómez-Vásquez, M and Jaramillo-López, MF and Gaspar-Toro, JM}, title = {New approaches to recovery after stroke.}, journal = {Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology}, volume = {}, number = {}, pages = {}, pmid = {37697027}, issn = {1590-3478}, abstract = {After a stroke, several mechanisms of neural plasticity can be activated, which may lead to significant recovery. Rehabilitation therapies aim to restore surviving tissue over time and reorganize neural connections. With more patients surviving stroke with varying degrees of neurological impairment, new technologies have emerged as a promising option for better functional outcomes. This review explores restorative therapies based on brain-computer interfaces, robot-assisted and virtual reality, brain stimulation, and cell therapies. Brain-computer interfaces allow for the translation of brain signals into motor patterns. Robot-assisted and virtual reality therapies provide interactive interfaces that simulate real-life situations and physical support to compensate for lost motor function. Brain stimulation can modify the electrical activity of neurons in the affected cortex. Cell therapy may promote regeneration in damaged brain tissue. Taken together, these new approaches could substantially benefit specific deficits such as arm-motor control and cognitive impairment after stroke, and even the chronic phase of recovery, where traditional rehabilitation methods may be limited, and the window for repair is narrow.}, }
@article {pmid37696689, year = {2023}, author = {Lu, K and Pan, Y}, title = {A collective neuroscience lens on intergroup conflict.}, journal = {Trends in cognitive sciences}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.tics.2023.08.021}, pmid = {37696689}, issn = {1879-307X}, abstract = {How do team leaders and followers synchronize their behaviors and brains to effectively manage intergroup conflicts? Zhang and colleagues offered a collective neurobehavioral narrative that delves into the intricacies of intergroup conflict. Their results underscore the importance of leaders' group-oriented actions, along with leader-follower synchronization, in intergroup conflict resolution.}, }
@article {pmid37696046, year = {2023}, author = {Pitt, KM and Cole, ZJ and Zosky, J}, title = {Promoting Simple and Engaging Brain-Computer Interface Designs for Children by Evaluating Contrasting Motion Techniques.}, journal = {Journal of speech, language, and hearing research : JSLHR}, volume = {}, number = {}, pages = {1-14}, doi = {10.1044/2023_JSLHR-23-00292}, pmid = {37696046}, issn = {1558-9102}, abstract = {PURPOSE: There is an increasing focus on using motion in augmentative and alternative communication (AAC) systems. In considering brain-computer interface access to AAC (BCI-AAC), motion may provide a simpler or more intuitive avenue for BCI-AAC control. Different motion techniques may be utilized in supporting competency with AAC devices including simple (e.g., zoom) and complex (behaviorally relevant animation) methods. However, how different pictorial symbol animation techniques impact BCI-AAC is unclear.<br><br>METHOD: Sixteen healthy children completed two experimental conditions. These conditions included highlighting of pictorial symbols via both functional (complex) and zoom (simple) animation to evaluate the effects of motion techniques on P300-based BCI-AAC signals and offline (predicted) BCI-AAC performance.<br><br>RESULTS: Functional (complex) animation significantly increased attentional-related P200/P300 event-related potential (ERP) amplitudes in the parieto-occipital area. Zoom (simple) animation significantly decreased N400 latency. N400 ERP amplitude was significantly greater, and occurred significantly earlier, on the right versus left side for the functional animation condition within the parieto-occipital bin. N200 ERP latency was significantly reduced over the left hemisphere for the zoom condition in the central bin. As hypothesized, elicitation of all targeted ERP components supported offline (predicted) BCI-AAC performance being similar between conditions.<br><br>CONCLUSION: Study findings provide continued support for the use of animation in BCI-AAC systems for children and highlight differences in neural and attentional processing between complex and simple animation techniques.<br><br>SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.24085623.}, }
@article {pmid37693482, year = {2023}, author = {Rustamov, N and Souders, L and Sheehan, L and Carter, A and Leuthardt, EC}, title = {IpsiHand Brain-Computer Interface Therapy Induces Broad Upper Extremity Motor Recovery in Chronic Stroke.}, journal = {medRxiv : the preprint server for health sciences}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.08.26.23294320}, pmid = {37693482}, abstract = {BACKGROUND AND PURPOSE: Chronic hemiparetic stroke patients have very limited benefits from current therapies. Brain-computer interface (BCI) engaging the unaffected hemisphere has emerged as a promising novel therapeutic approach for chronic stroke rehabilitation. This study investigated the effectiveness of the IpsiHand System, a contralesionally-controlled BCI therapy in chronic stroke patients with impaired upper extremity motor function. We further explored neurophysiological features of motor recovery affected by BCI. We hypothesized that BCI therapy would induce a broad motor recovery in the upper extremity (proximal and distal), and there would be corresponding changes in baseline theta and gamma oscillations, which have been shown to be associated with motor recovery.<br><br>METHODS: Thirty chronic hemiparetic stroke patients performed a therapeutic BCI task for 12 weeks. Motor function assessment data and resting state electroencephalogram (EEG) signals were acquired before initiating BCI therapy and across BCI therapy sessions. The Upper Extremity Fugl-Meyer assessment (UEFM) served as a primary motor outcome assessment tool. Theta-gamma cross-frequency coupling (CFC) was computed and correlated with motor recovery.<br><br>RESULTS: Chronic stroke patients achieved significant motor improvement with BCI therapy. We found significant improvement in both proximal and distal upper extremity motor function. Importantly, motor function improvement was independent of Botox application. Theta-gamma CFC enhanced bilaterally over the C3 and C4 motor electrodes following BCI therapy. We observed significant positive correlations between motor recovery and theta gamma CFC increase across BCI therapy sessions.<br><br>CONCLUSIONS: BCI therapy resulted in significant motor function improvement across the proximal and distal upper extremities of patients. This therapy was significantly correlated with changes in baseline cortical dynamics, specifically theta-gamma CFC increases in both the right and left motor regions. This may represent rhythm-specific cortical oscillatory mechanism for BCI-driven motor rehabilitation in chronic stroke patients.}, }
@article {pmid37690592, year = {2023}, author = {Huang, Y and Deng, Y and Kong, L and Zhang, X and Wei, X and Mao, T and Xu, Y and Jiang, C and Rao, H}, title = {Vigilant attention mediates the association between resting EEG alpha oscillations and word learning ability.}, journal = {NeuroImage}, volume = {}, number = {}, pages = {120369}, doi = {10.1016/j.neuroimage.2023.120369}, pmid = {37690592}, issn = {1095-9572}, abstract = {Individuals exhibit considerable variability in their capacity to learn and retain new information, including novel vocabulary. Prior research has established the importance of vigilance and electroencephalogram (EEG) alpha rhythm in the learning process. However, the interplay between vigilant attention, EEG alpha oscillations, and an individual's word learning ability (WLA) remains elusive. To address this knowledge gap, here we conducted two experiments with a total of 140 young and middle-aged adults who underwent resting EEG recordings prior to completing a paired-associate word learning task and a psychomotor vigilance test (PVT). The results of both experiments consistently revealed significant positive correlations between WLA and resting EEG alpha oscillations in the occipital and frontal regions. Furthermore, the association between resting EEG alpha oscillations and WLA was mediated by vigilant attention, as measured by the PVT. These findings provide compelling evidence supporting the crucial role of vigilant attention in linking EEG alpha oscillations to an individual's learning ability.}, }
@article {pmid37689832, year = {2023}, author = {Tao, R and Zhang, C and Zhao, H and Xu, S}, title = {Active vs. computer-based passive decision-making leads to discrepancies in outcome evaluation: evidence from self-reported emotional experience and brain activity.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {}, number = {}, pages = {}, doi = {10.1093/cercor/bhad317}, pmid = {37689832}, issn = {1460-2199}, support = {72171151//National Natural Science Foundation of China/ ; 21ZR1461600//Natural Science Foundation of Shanghai/ ; 2021114003//Fundamental Research Funds for the Central Universities/ ; 2021WSYS002//Neuroeconomics Laboratory of Guangzhou Huashang College/ ; }, abstract = {People prefer active decision-making and induce greater emotional feelings than computer-based passive mode, yet the modulation of decision-making mode on outcome evaluation remains unknown. The present study adopted event-related potentials to investigate the discrepancies in active and computer-based passive mode on outcome evaluation using a card gambling task. The subjective rating results showed that active mode elicited more cognitive effort and stronger emotional feelings than passive mode. For received outcomes, we observed no significant Feedback-Related Negativity (FRN) effect on difference waveshapes (d-FRN) between the 2 modes, but active decision-making elicited larger P300 amplitudes than the passive mode. For unchosen card outcomes, the results revealed larger d-FRN amplitudes of relative valences (Superior - Inferior) in responses to negative feedback in active mode than in passive mode. The averaged P300 results revealed an interplay among outcome feedback, decision-making mode, and relative valence, and the average P300 amplitude elicited by the received loss outcome in the active mode partially mediated the relationship between subjective cognitive effort and negative emotion ratings on loss. Our findings indicate discrepancies between active and computer-based passive modes, and cognitive effort and emotional experience involved in outcome evaluation.}, }
@article {pmid37688757, year = {2023}, author = {Liyanagedera, ND and Hussain, AA and Singh, A and Lal, S and Kempton, H and Guesgen, HW}, title = {Common spatial pattern for classification of loving kindness meditation EEG for single and multiple sessions.}, journal = {Brain informatics}, volume = {10}, number = {1}, pages = {24}, pmid = {37688757}, issn = {2198-4018}, abstract = {While a very few studies have been conducted on classifying loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data for a single session, there are no such studies conducted for multiple session EEG data. Thus, this study aims at classifying existing raw EEG meditation data on single and multiple sessions to come up with meaningful inferences which will be highly beneficial when developing algorithms that can support meditation practices. In this analysis, data have been collected on Pre-Resting (before-meditation), Post-Resting (after-meditation), LKM-Self and LKM-Others for 32 participants and hence allowing us to conduct six pairwise comparisons for the four mind tasks. Common Spatial Patterns (CSP) is a feature extraction method widely used in motor imaginary brain computer interface (BCI), but not in meditation EEG data. Therefore, using CSP in extracting features from meditation EEG data and classifying meditation/non-meditation instances, particularly for multiple sessions will create a new path in future meditation EEG research. The classification was done using Linear Discriminant Analysis (LDA) where both meditation techniques (LKM-Self and LKM-Others) were compared with Pre-Resting and Post-Resting instances. The results show that for a single session of 32 participants, around 99.5% accuracy was obtained for classifying meditation/Pre-Resting instances. For the 15 participants when using five sessions of EEG data, around 83.6% accuracy was obtained for classifying meditation/Pre-Resting instances. The results demonstrate the ability to classify meditation/Pre-Resting data. Most importantly, this classification is possible for multiple session data as well. In addition to this, when comparing the classification accuracies of the six mind task pairs; LKM-Self, LKM-Others and Post-Resting produced relatively lower accuracies among them than the accuracies obtained for classifying Pre-Resting with the other three. This indicates that Pre-Resting has some features giving a better classification indicating that it is different from the other three mind tasks.}, }
@article {pmid37687976, year = {2023}, author = {Siviero, I and Menegaz, G and Storti, SF}, title = {Functional Connectivity and Feature Fusion Enhance Multiclass Motor-Imagery Brain-Computer Interface Performance.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {17}, pages = {}, pmid = {37687976}, issn = {1424-8220}, support = {"Ricerca&amp;Sviluppo"//Fondazione CariVerona/ ; "Dipartimenti di Eccellenza"//Italian Ministry of Education, University and Research/ ; DM 1061/2021//REACT-EU PON "Ricerca e Innovazione" 2014-2020/ ; }, mesh = {*Brain-Computer Interfaces ; Brain ; Electroencephalography ; Imagery, Psychotherapy ; Signal Processing, Computer-Assisted ; }, abstract = {(1) Background: in the field of motor-imagery brain-computer interfaces (MI-BCIs), obtaining discriminative features among multiple MI tasks poses a significant challenge. Typically, features are extracted from single electroencephalography (EEG) channels, neglecting their interconnections, which leads to limited results. To address this limitation, there has been growing interest in leveraging functional brain connectivity (FC) as a feature in MI-BCIs. However, the high inter- and intra-subject variability has so far limited its effectiveness in this domain. (2) Methods: we propose a novel signal processing framework that addresses this challenge. We extracted translation-invariant features (TIFs) obtained from a scattering convolution network (SCN) and brain connectivity features (BCFs). Through a feature fusion approach, we combined features extracted from selected channels and functional connectivity features, capitalizing on the strength of each component. Moreover, we employed a multiclass support vector machine (SVM) model to classify the extracted features. (3) Results: using a public dataset (IIa of the BCI Competition IV), we demonstrated that the feature fusion approach outperformed existing state-of-the-art methods. Notably, we found that the best results were achieved by merging TIFs with BCFs, rather than considering TIFs alone. (4) Conclusions: our proposed framework could be the key for improving the performance of a multiclass MI-BCI system.}, }
@article {pmid37685390, year = {2023}, author = {Antony, MJ and Sankaralingam, BP and Khan, S and Almjally, A and Almujally, NA and Mahendran, RK}, title = {Brain-Computer Interface: The HOL-SSA Decomposition and Two-Phase Classification on the HGD EEG Data.}, journal = {Diagnostics (Basel, Switzerland)}, volume = {13}, number = {17}, pages = {}, pmid = {37685390}, issn = {2075-4418}, support = {PNURSP2023R410//Princess Nourah bint Abdulrahman University/ ; }, abstract = {An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain-Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL-SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method's ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity.}, }
@article {pmid37683772, year = {2023}, author = {Sun, H and Jin, J and Daly, I and Huang, Y and Zhao, X and Wang, X and Cichocki, A}, title = {Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {109969}, doi = {10.1016/j.jneumeth.2023.109969}, pmid = {37683772}, issn = {1872-678X}, abstract = {Learning distinguishable features from raw EEG signals is crucial for accurate classification of motor imagery (MI) tasks. To incorporate spatial relationships between EEG sources, we developed a feature set based on an EEG graph. In this graph, EEG channels represent the nodes, with power spectral density (PSD) features defining their properties, and the edges preserving the spatial information. We designed an EEG based graph self-attention network (EGSAN) to learn low-dimensional embedding vector for EEG graph, which can be used as distinguishable features for motor imagery task classification. We evaluated our EGSAN model on two publicly available MI EEG datasets, each containing different types of motor imagery tasks. Our experiments demonstrate that our proposed model effectively extracts distinguishable features from EEG graphs, achieving significantly higher classification accuracies than existing state-of-the-art methods.}, }
@article {pmid37683664, year = {2023}, author = {Liu, K and Yang, M and Xing, X and Yu, Z and Wu, W}, title = {SincMSNet: A Sinc filter convolutional neural network for EEG motor imagery classification.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acf7f4}, pmid = {37683664}, issn = {1741-2552}, abstract = {Motor imagery (MI) is a commonly employed experimental paradigm in brain-computer interfaces (BCIs). Nevertheless, the decoding of MI-EEG using convolutional neural networks (CNNs) is still deemed challenging due to the variability of individuals and the non-stationarity of EEG signals. Approach: We propose an end-to-end convolutional neural network (CNN) called SincMSNet for MI decoding. SincMSNet utilizes the Sinc filter to extract subject-specific frequency band information, and mixed-depth convolution to extract multi-scale temporal information for each band. Spatial convolutional blocks are then used to extract spatial features, while the temporal log-variance block is used to acquire classification features. Main results: We assessed SincMSNet on two MI datasets and compared it to several state-of-the-art MI decoding methods. Our results demonstrate that SincMSNet surpasses the benchmark methods, achieving an average accuracy of 80.70% and 71.50% in the four-class and two-class of hold-out classification, respectively. Furthermore, the acquired filter sets exhibit the network's capability to provide higher relevance to individual features. Significance: SincMSNet is a promising method to enhance the performance of MI-EEG decoding, and is available for use through the source code at https://github.com/Want2Vanish/SincMSNet.}, }
@article {pmid37683663, year = {2023}, author = {Xiao, X and Wang, L and Xu, M and Wang, K and Jung, TP and Ming, D}, title = {A data expansion technique based on training and testing sample to boost the detection of SSVEPs for brain-computer interfaces.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acf7f6}, pmid = {37683663}, issn = {1741-2552}, abstract = {Currently, steady-state visual evoked potentials (SSVEPs)-based brain-computer interfaces (BCIs) have achieved the highest interaction accuracy and speed among all BCI paradigms. However, its decoding efficacy deeply depends on the number of training samples, and the system performance would have a dramatical drop when the training dataset decreased to a small size. To date, no study has been reported to incorporate the unsupervised learning information from testing trails into the construction of supervised classification model, which is a potential way to mitigate the overfitting effect of limited samples. Approach. This study proposed a novel method for SSVEPs detection, i.e., cyclic shift trials (CST), which could combine unsupervised learning information from test trials and supervised learning information from train trials. Furthermore, since SSVEPs are time-locked and phase-locked to the onsets of specific flashes, CST could also expand training samples on the basis of its regularity and periodicity. In order to verify the effectiveness of CST, we designed an online SSVEP-BCI system, and tested this system combined CST with two common classification algorithms, i.e., extended canonical correlation analysis (eCCA) and ensemble task-related component analysis (eTRCA). Main results. CST could significantly enhance the SNRs of SSVEPs and improve the performance of systems especially for the condition of few training samples and short stimulus time. The online information transfer rate (ITR) could reach up to 236.19 bits/min using 36 seconds calibration time of only one training sample for each category. Significance. The proposed CST method can take full advantages of supervised learning information from training samples and unsupervised learning information of testing samples. Furthermore, it is a data expansion technique, which can enhance the SSVEP characteristics and reduce dependence on sample size. Above all, CST is a promising method to improve the performance of SSVEP-based BCI without any additional experimental burden. .}, }
@article {pmid37683653, year = {2023}, author = {Semenkov, I and Fedosov, N and Makarov, I and Ossadtchi, A}, title = {Real-time low latency estimation of brain rhythms with deep neural networks.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acf7f3}, pmid = {37683653}, issn = {1741-2552}, abstract = {Objective.Neurofeedback and brain-computer interfacing technology open the exciting opportunity for establishing interactive closed-loop real-time communication with the human brain. This requires interpreting brain's rhythmic activity and generating timely feedback to the brain. Lower delay between neuronal events and the appropriate feedback increase the efficacy of such interaction. Novel more efficient approaches capable of tracking brain rhythm's phase and envelope are needed for scenarios that entail instantaneous interaction with the brain circuits. Approach.Isolating narrow-band signals incurs fundamental delays. To some extent they can be compensated using forecasting models. Given the high quality of modern time series forecasting neural networks we explored their utility for low-latency extraction of brain rhythm parameters. We tested five neural networks with conceptually distinct architectures in forecasting synthetic EEG rhythms. The strongest architecture was trained to simultaneously filter and forecast EEG data. We compared it against state-of-the-art techniques using synthetic and real data from 25 subjects. Main results.The Temporal Convolutional Network (TCN) remained the strongest forecasting model that achieved in the majority of testing scenarios >90% rhythm's envelope correlation with <10 ms effective delay and <20° circular standard deviation of phase estimates. It also remained stable enough to noise level perturbations. Trained to filter and predict the TCN outperformed the cFIR, the Kalman filter based state-space estimation technique and remained on par with the larger Conv-TasNet architecture. Significance.Here we have for the first time demonstrated the utility of the neural network approach for low-latency narrow-band filtering of brain activity signals. Our proposed approach coupled with efficient implementation enhances the effectiveness of brain-state dependent paradigms across various applications. Moreover, our framework for forecasting EEG signals holds promise for investigating the predictability of brain activity, providing valuable insights into the fundamental questions surrounding the functional organization and hierarchical information processing properties of the brain.}, }
@article {pmid37682884, year = {2023}, author = {Khan, RA and Rashid, N and Shahzaib, M and Malik, UF and Arif, A and Iqbal, J and Saleem, M and Khan, US and Tiwana, M}, title = {A novel framework for classification of two-class motor imagery EEG signals using logistic regression classification algorithm.}, journal = {PloS one}, volume = {18}, number = {9}, pages = {e0276133}, pmid = {37682884}, issn = {1932-6203}, mesh = {Humans ; *Artificial Intelligence ; Bayes Theorem ; Logistic Models ; *Algorithms ; Electroencephalography ; }, abstract = {Robotics and artificial intelligence have played a significant role in developing assistive technologies for people with motor disabilities. Brain-Computer Interface (BCI) is a communication system that allows humans to communicate with their environment by detecting and quantifying control signals produced from different modalities and translating them into voluntary commands for actuating an external device. For that purpose, classification the brain signals with a very high accuracy and minimization of the errors is of profound importance to the researchers. So in this study, a novel framework has been proposed to classify the binary-class electroencephalogram (EEG) data. The proposed framework is tested on BCI Competition IV dataset 1 and BCI Competition III dataset 4a. Artifact removal from EEG data is done through preprocessing, followed by feature extraction for recognizing discriminative information in the recorded brain signals. Signal preprocessing involves the application of independent component analysis (ICA) on raw EEG data, accompanied by the employment of common spatial pattern (CSP) and log-variance for extracting useful features. Six different classification algorithms, namely support vector machine, linear discriminant analysis, k-nearest neighbor, naïve Bayes, decision trees, and logistic regression, have been compared to classify the EEG data accurately. The proposed framework achieved the best classification accuracies with logistic regression classifier for both datasets. Average classification accuracy of 90.42% has been attained on BCI Competition IV dataset 1 for seven different subjects, while for BCI Competition III dataset 4a, an average accuracy of 95.42% has been attained on five subjects. This indicates that the model can be used in real time BCI systems and provide extra-ordinary results for 2-class Motor Imagery (MI) signals classification applications and with some modifications this framework can also be made compatible for multi-class classification in the future.}, }
@article {pmid37681531, year = {2023}, author = {Zhuang, S and He, M and Feng, J and Peng, S and Jiang, H and Li, Y and Hua, N and Zheng, Y and Ye, Q and Hu, M and Nie, Y and Yu, P and Yue, X and Qian, J and Yang, W}, title = {Near-Infrared Photothermal Manipulates Cellular Excitability and Animal Behavior in Caenorhabditis elegans.}, journal = {Small methods}, volume = {}, number = {}, pages = {e2300848}, doi = {10.1002/smtd.202300848}, pmid = {37681531}, issn = {2366-9608}, support = {82030108//National Natural Science Foundation of China/ ; //MOE Frontier Science Center for Brain Science &amp; Brain-Machine Integration, Zhejiang University/ ; }, abstract = {Near-infrared (NIR) photothermal manipulation has emerged as a promising and noninvasive technology for neuroscience research and disease therapy for its deep tissue penetration. NIR stimulated techniques have been used to modulate neural activity. However, due to the lack of suitable in vivo control systems, most studies are limited to the cellular level. Here, a NIR photothermal technique is developed to modulate cellular excitability and animal behaviors in Caenorhabditis elegans in vivo via the thermosensitive transient receptor potential vanilloid 1 (TRPV1) channel with an FDA-approved photothermal agent indocyanine green (ICG). Upon NIR stimuli, exogenous expression of TRPV1 in AFD sensory neurons causes Ca[2+] influx, leading to increased neural excitability and reversal behaviors, in the presence of ICG. The GABAergic D-class motor neurons can also be activated by NIR irradiation, resulting in slower thrashing behaviors. Moreover, the photothermal manipulation is successfully applied in different types of muscle cells (striated muscles and nonstriated muscles), enhancing muscular excitability, causing muscle contractions and behavior changes in vivo. Altogether, this study demonstrates a noninvasive method to precisely regulate the excitability of different types of cells and related behaviors in vivo by NIR photothermal manipulation, which may be applied in mammals and clinical therapy.}, }
@article {pmid37680264, year = {2023}, author = {Maslova, O and Komarova, Y and Shusharina, N and Kolsanov, A and Zakharov, A and Garina, E and Pyatin, V}, title = {Non-invasive EEG-based BCI spellers from the beginning to today: a mini-review.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1216648}, pmid = {37680264}, issn = {1662-5161}, abstract = {The defeat of the central motor neuron leads to the motor disorders. Patients lose the ability to control voluntary muscles, for example, of the upper limbs, which introduces a fundamental dissonance in the possibility of daily use of a computer or smartphone. As a result, the patients lose the ability to communicate with other people. The article presents the most popular paradigms used in the brain-computer-interface speller system and designed for typing by people with severe forms of the movement disorders. Brain-computer interfaces (BCIs) have emerged as a promising technology for individuals with communication impairments. BCI-spellers are systems that enable users to spell words by selecting letters on a computer screen using their brain activity. There are three main types of BCI-spellers: P300, motor imagery (MI), and steady-state visual evoked potential (SSVEP). However, each type has its own limitations, which has led to the development of hybrid BCI-spellers that combine the strengths of multiple types. Hybrid BCI-spellers can improve accuracy and reduce the training period required for users to become proficient. Overall, hybrid BCI-spellers have the potential to improve communication for individuals with impairments by combining the strengths of multiple types of BCI-spellers. In conclusion, BCI-spellers are a promising technology for individuals with communication impairments. P300, MI, and SSVEP are the three main types of BCI-spellers, each with their own advantages and limitations. Further research is needed to improve the accuracy and usability of BCI-spellers and to explore their potential applications in other areas such as gaming and virtual reality.}, }
@article {pmid37679900, year = {2023}, author = {Ling, Y and Wen, X and Tang, J and Tao, Z and Sun, L and Xin, H and Luo, B}, title = {Effect of topographic comparison of electroencephalographic microstates on the diagnosis and prognosis prediction of patients with prolonged disorders of consciousness.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {}, number = {}, pages = {}, doi = {10.1111/cns.14421}, pmid = {37679900}, issn = {1755-5949}, support = {2021ZD0200404//China Brain Project/ ; U22A20293//The National Natural Science Foundation of China/ ; 82071173//The National Natural Science Foundation of China/ ; }, abstract = {AIMS: The electroencephalography (EEG) microstates are indicative of fundamental information processing mechanisms, which are severely damaged in patients with prolonged disorders of consciousness (pDoC). We aimed to improve the topographic analysis of EEG microstates and explore indicators available for diagnosis and prognosis prediction of patients with pDoC, which were still lacking.<br><br>METHODS: We conducted EEG recordings on 59 patients with pDoC and 32 healthy controls. We refined the microstate method to accurately estimate topographical differences, and then classify and forecast the prognosis of patients with pDoC. An independent dataset was used to validate the conclusion.<br><br>RESULTS: Through optimized topographic analysis, the global explained variance (GEV) of microstate E increased significantly in groups with reduced levels of consciousness. However, its ability to classify the VS/UWS group was poor. In addition, the optimized GEV of microstate E exhibited a statistically significant decrease in the good prognosis group as opposed to the group with a poor prognosis. Furthermore, the optimized GEV of microstate E strongly predicted a patient's prognosis.<br><br>CONCLUSION: This technique harmonizes with the existing microstate analysis and exhibits precise and comprehensive differences in microstate topography between groups. Furthermore, this method has significant potential for evaluating the clinical prognosis of pDoC patients.}, }
@article {pmid37679881, year = {2023}, author = {Schnitzer, SA and DeFilippis, DM and Aguilar, A and Bernal, B and Peréz, S and Valdés, A and Valdés, S and Bernal, F and Mendoza, A and Castro, B and Garcia-Leon, M}, title = {Maximum stem diameter predicts liana population demography.}, journal = {Ecology}, volume = {}, number = {}, pages = {e4163}, doi = {10.1002/ecy.4163}, pmid = {37679881}, issn = {1939-9170}, abstract = {Determining population demographic rates is fundamental to understanding differences in species life-history strategies and their capacity to coexist. Calculating demographic rates, however, is challenging and requires long-term, large-scale censuses. Body size may serve as a simple predictor of demographic rate; can it act as a proxy for demographic rate when those data are unavailable? We tested the hypothesis that maximum body size predicts species' demographic rate using repeated censuses of the 77 most common liana species on the Barro Colorado Island, Panama (BCI) 50-ha plot. We found that maximum stem diameter does predict species' population turnover and demography. We also found that lianas on BCI can grow to the enormous diameter of 635 mm, indicating that they can store large amounts of carbon and compete intensely with tropical canopy trees. This study is the first to show that maximum stem diameter can predict plant species' demographic rates and that lianas can attain extremely large diameters. Understanding liana demography is particularly timely because lianas are increasing rapidly in many tropical forests, yet their species-level population dynamics remain chronically understudied. Determining per-species maximum liana diameters in additional forests will enable systematic comparative analyses of liana demography and potential influence across forest types. This article is protected by copyright. All rights reserved.}, }
@article {pmid37678543, year = {2023}, author = {Zhu, Y and Xie, SZ and Peng, AB and Yu, XD and Li, CY and Fu, JY and Shen, CJ and Cao, SX and Zhang, Y and Chen, J and Li, XM}, title = {Distinct Circuits from Central Lateral Amygdala to Ventral Part of Bed Nucleus of Stria Terminalis Regulate Different Fear Memory.}, journal = {Biological psychiatry}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.biopsych.2023.08.022}, pmid = {37678543}, issn = {1873-2402}, abstract = {BACKGROUND: The ability to differentiate stimuli predicting fear is critical for survival, however, the underlying molecular and circuit mechanisms remain poorly understood.<br><br>METHODS: We combined transgenic mice, in vivo transsynaptic circuit-dissecting anatomical approaches, optogenetics, pharmacological methods and electrophysiological recording to investigate the involvement of specific extended amygdala circuits in different fear memory.<br><br>RESULTS: We identify the projections from central lateral amygdala (CeL) protein kinase C &#x3b4; (PKC&#x3b4;) positive neurons and somatostatin (SST) positive neurons to the ventral part of bed nucleus of stria terminalis (vBNST) GABAergic and glutamatergic neurons. Prolonged optogenetic activation or inhibition of PKC&#x3b4;[CeL-vBNST] pathway specifically reduced context fear memory, whereas SST[CeL-vBNST] pathway mainly reduced tone fear memory. Intriguingly, optogenetic manipulation of vBNST neurons received the projection from PKC&#x3b4;[CeL] exerted bidirectional regulation of context fear, whereas manipulation of vBNST neurons received the projection from SST[CeL] neurons could bidirectionally regulate both context and tone fear memory. We subsequently demonstrated the presence of &#x3b4; and &#x3ba; opioid receptor protein expression within the CeL-vBNST circuits, potentially accounting for the discrepancy between prolonged activation of GABAergic circuits and inhibition of downstream vBNST neurons. Finally, administration of an opioid receptor antagonist cocktail on the PKC&#x3b4;[CeL-vBNST] or SST[CeL-vBNST] pathway successfully restored context or tone fear memory reduction induced by prolonged activation of the circuits.<br><br>CONCLUSIONS: Together, these findings establish a functional role for distinct CeL-vBNST circuits in the differential regulation and appropriate maintenance of fear.}, }
@article {pmid37678229, year = {2023}, author = {Barmpas, K and Panagakis, Y and Adamos, DA and Laskaris, N and Zafeiriou, S}, title = {BrainWave-Scattering Net: A lightweight network for EEG-based motor imagery recognition.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acf78a}, pmid = {37678229}, issn = {1741-2552}, abstract = {Brain-computer interfaces (BCIs) enable a direct communication of the brain with the external world, using one's neural activity, measured by electroencephalography (EEG) signals. In recent years, Convolutional Neural Networks (CNNs) have been widely used to perform automatic feature extraction and classification in various EEG-based tasks. However, their undeniable benefits are counterbalanced by the lack of interpretability properties as well as the inability to perform sufficiently when only limited amount of training data is available. In this work, we introduce a novel, lightweight, fully-learnable neural network architecture that relies on Gabor filters to delocalize EEG signal information into scattering decomposition paths along frequency and slow-varying temporal modulations. We utilize our network in two distinct modeling settings, for building either a generic (training across subjects) or a personalized (training within a subject) classifier. In both cases, using two different publicly available datasets and one in-house collected dataset, we demonstrate high performance for our model with considerably less number of trainable parameters as well as shorter training time compared to other state-of-the-art deep architectures. Moreover, our network demonstrates enhanced interpretability properties emerging at the level of the temporal filtering operation and enables us to train efficient personalized BCI models with limited amount of training data.}, }
@article {pmid37678137, year = {2023}, author = {Zhang, L and Li, C and Zhang, R and Sun, Q}, title = {Online semi-supervised learning for motor imagery EEG classification.}, journal = {Computers in biology and medicine}, volume = {165}, number = {}, pages = {107405}, doi = {10.1016/j.compbiomed.2023.107405}, pmid = {37678137}, issn = {1879-0534}, abstract = {OBJECTIVE: Time-consuming data labeling in brain-computer interfaces (BCIs) raises many problems such as mental fatigue and is one key factor that hinders the real-world adoption of motor imagery (MI)-based BCIs. An alternative approach is to integrate readily available, as well as informative, unlabeled data online, whereas this approach is less investigated.<br><br>APPROACH: We proposed an online semi-supervised learning scheme to improve the classification performance of MI-based BCI. This scheme uses regularized weighted online sequential extreme learning machine (RWOS-ELM) as the base classifier and updates its model parameters with incoming balanced data chunk-by-chunk. In the initial stage, we designed a technique that combines the synthetic minority oversampling with the edited nearest neighbor rule for data augmentation to construct more discriminative initial classifiers. When used online, the incoming chunk of data is first pseudo-labeled by RWOS-ELM as well as an auxiliary classifier, and then balanced again by the above-mentioned technique. Initial classifiers are further updated based on these class-balanced data.<br><br>MAIN RESULTS: Offline experimental results on two publicly available MI datasets demonstrate the superiority of the proposed scheme over its counterparts. Further online experiments on six subjects show that their BCI performance gradually improved by learning from incoming unlabeled data.<br><br>SIGNIFICANCE: Our proposed online semi-supervised learning scheme has higher computation and memory usage efficiency, which is promising for online MI-based BCIs, especially in the case of insufficient labeled training data.}, }
@article {pmid37677045, year = {2023}, author = {Jiang, Y and Yin, J and Zhao, B and Zhang, Y and Peng, T and Zhuang, W and Wang, S and Huang, S and Zhong, M and Zhang, Y and Tang, G and Shen, B and Ou, H and Zheng, Y and Lin, Q}, title = {Motor Imagery Brain-Computer Interface in Rehabilitation of Upper Limb Motor Dysfunction After Stroke.}, journal = {Journal of visualized experiments : JoVE}, volume = {}, number = {199}, pages = {}, doi = {10.3791/65405}, pmid = {37677045}, issn = {1940-087X}, abstract = {The rehabilitation effect of patients with moderate or severe upper limb motor dysfunction after stroke is poor, which has been the focus of research owing to the difficulties encountered. Brain-computer interface (BCI) represents a hot frontier technology in brain neuroscience research. It refers to the direct conversion of the sensory perception, imagery, cognition, and thinking of users or subjects into actions, without reliance on peripheral nerves or muscles, to establish direct communication and control channels between the brain and external devices. Motor imagery brain-computer interface (MI-BCI) is the most common clinical application of rehabilitation as a non-invasive means of rehabilitation. Previous clinical studies have confirmed that MI-BCI positively improves motor dysfunction in patients after stroke. However, there is a lack of clinical operation demonstration. To that end, this study describes in detail the treatment of MI-BCI for patients with moderate and severe upper limb dysfunction after stroke and shows the intervention effect of MI-BCI through clinical function evaluation and brain function evaluation results, thereby providing ideas and references for clinical rehabilitation application and mechanism research.}, }
@article {pmid37676244, year = {2023}, author = {Luo, Y and Sun, C and Wei, M and Ma, H and Wu, Y and Chen, Z and Dai, H and Jian, J and Sun, B and Zhong, C and Li, J and Richardson, KA and Lin, H and Li, L}, title = {Integrated Flexible Microscale Mechanical Sensors Based on Cascaded Free Spectral Range-Free Cavities.}, journal = {Nano letters}, volume = {}, number = {}, pages = {}, doi = {10.1021/acs.nanolett.3c02239}, pmid = {37676244}, issn = {1530-6992}, abstract = {Photonic mechanical sensors offer several advantages over their electronic counterparts, including immunity to electromagnetic interference, increased sensitivity, and measurement accuracy. Exploring flexible mechanical sensors on deformable substrates provides new opportunities for strain-optical coupling operations. Nevertheless, existing flexible photonics strategies often require cumbersome signal collection and analysis with bulky setups, limiting their portability and affordability. To address these challenges, we propose a waveguide-integrated flexible mechanical sensor based on cascaded photonic crystal microcavities with inherent deformation and biaxial tensile state analysis. Leveraging the advanced multiplexing capability of the sensor, for the first time, we successfully demonstrate 2D shape reconstruction and quasi-distributed strain sensing with 110 μm spatial resolution. Our microscale mechanical sensor also exhibits exceptional sensitivity with a detected force level as low as 13.6 μN in real-time measurements. This sensing platform has potential applications in various fields, including biomedical sensing, surgical catheters, aircraft and spacecraft engineering, and robotic photonic skin development.}, }
@article {pmid37674934, year = {2023}, author = {Lugo, ZR and Cinel, C and Jeunet, C and Pichiorri, F and Riccio, A and Wriessnegger, SC}, title = {Editorial: Women in brain-computer interfaces.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1260479}, pmid = {37674934}, issn = {1662-5161}, }
@article {pmid37671039, year = {2023}, author = {Zrenner, B and Zrenner, C and Balderston, N and Blumberger, DM and Kloiber, S and Laposa, JM and Tadayonnejad, R and Trevizol, AP and Zai, G and Feusner, JD}, title = {Toward personalized circuit-based closed-loop brain-interventions in psychiatry: using symptom provocation to extract EEG-markers of brain circuit activity.}, journal = {Frontiers in neural circuits}, volume = {17}, number = {}, pages = {1208930}, pmid = {37671039}, issn = {1662-5110}, mesh = {Humans ; *Psychiatry ; Transcranial Magnetic Stimulation ; Brain ; *Brain-Computer Interfaces ; Electroencephalography ; }, abstract = {Symptom provocation is a well-established component of psychiatric research and therapy. It is hypothesized that specific activation of those brain circuits involved in the symptomatic expression of a brain pathology makes the relevant neural substrate accessible as a target for therapeutic interventions. For example, in the treatment of obsessive-compulsive disorder (OCD), symptom provocation is an important part of psychotherapy and is also performed prior to therapeutic brain stimulation with transcranial magnetic stimulation (TMS). Here, we discuss the potential of symptom provocation to isolate neurophysiological biomarkers reflecting the fluctuating activity of relevant brain networks with the goal of subsequently using these markers as targets to guide therapy. We put forward a general experimental framework based on the rapid switching between psychiatric symptom states. This enable neurophysiological measures to be derived from EEG and/or TMS-evoked EEG measures of brain activity during both states. By subtracting the data recorded during the baseline state from that recorded during the provoked state, the resulting contrast would ideally isolate the specific neural circuits differentially activated during the expression of symptoms. A similar approach enables the design of effective classifiers of brain activity from EEG data in Brain-Computer Interfaces (BCI). To obtain reliable contrast data, psychiatric state switching needs to be achieved multiple times during a continuous recording so that slow changes of brain activity affect both conditions equally. This is achieved easily for conditions that can be controlled intentionally, such as motor imagery, attention, or memory retention. With regard to psychiatric symptoms, an increase can often be provoked effectively relatively easily, however, it can be difficult to reliably and rapidly return to a baseline state. Here, we review different approaches to return from a provoked state to a baseline state and how these may be applied to different symptoms occurring in different psychiatric disorders.}, }
@article {pmid37670502, year = {2023}, author = {Kumawat, J and Yadav, A and Yadav, K and Gaur, KL}, title = {Comparison of Spectral Analysis of Gamma Band Activity During Actual and Imagined Movements as a Cognitive Tool.}, journal = {Clinical EEG and neuroscience}, volume = {}, number = {}, pages = {15500594231197100}, doi = {10.1177/15500594231197100}, pmid = {37670502}, issn = {2169-5202}, abstract = {Background. Imagined motor movement is a cognitive process in which a subject imagines a movement without doing it, which activates similar brain regions as during actual motor movement. Brain gamma band activity (GBA) is linked to cognitive functions such as perception, attention, memory, awareness, synaptic plasticity, motor control, and Imagination. Motor imagery can be used in sports to improve performance, raising the possibility of using it as a rehabilitation method through brain plasticity through mirror neurons. Method. A comparative observational study was conducted on 56 healthy male subjects after obtaining clearance from the Ethics Committee. EEG recordings for GBA were taken for resting, real, and imaginary motor movements and compared. The power spectrum of gamma waves was analyzed using the Kruskal-Wallis test; a p-value <.05 was considered significant. Results. The brain gamma rhythm amplitude was statistically increased during both actual and imaginary motor movement compared to baseline (resting stage) in most of the regions of the brain except the occipital region. There was no significant difference in GBA between real and imaginary movements. Conclusions. Increased gamma rhythm amplitude during both actual and imaginary motor movement than baseline (resting stage) indicating raised brain cognitive activity during both types of movements. There was no potential difference between real and imaginary movements suggesting that the real movement can be replaced by the imaginary movement to enhance work performance through mirror therapy.}, }
@article {pmid37670474, year = {2023}, author = {Lima, EO and Silva, LM and Melo, ALV and D'arruda, JVT and Alexandre de Albuquerque, M and Ramos de Souza Neto, JM and Araújo de Oliveira, E and Andrade, SM}, title = {Transcranial Direct Current Stimulation and Brain-Computer Interfaces for Improving Post-Stroke Recovery: A Systematic Review and Meta-Analysis.}, journal = {Clinical rehabilitation}, volume = {}, number = {}, pages = {2692155231200086}, doi = {10.1177/02692155231200086}, pmid = {37670474}, issn = {1477-0873}, abstract = {OBJECTIVE: This study aimed to evaluate the effectiveness of transcranial direct current stimulation associated with brain-computer interface in stroke patients.<br><br>DATA SOURCES: The PubMed, Central, PEDro, Web of Science, SCOPUS, PsycINFO Ovid, CINAHL EBSCO, EMBASE, and ScienceDirect databases were searched from inception to April 2023 for randomized controlled studies reporting the effects of active transcranial direct current stimulation associated with brain-computer interface to a transcranial direct current stimulation sham associated with brain-computer interface condition on the outcome measure (motor performance and functional independence).<br><br>REVIEW METHODS: We searched for full-text articles which had investigated the effect of transcranial direct current stimulation associated with brain-computer interface on motor performance in the upper extremities in stroke patients. The standardized mean differences derived from the change in scores between pretreatment and post-treatment were adopted as the effect size measure, with a 95% confidence interval. Possible sources of heterogeneity were analyzed by performing subgroup analyses in order to examine the moderating effects for one variable: the level of injury severity.<br><br>RESULTS: Nine studies were included in the qualitative synthesis and the meta-analysis. The findings of the conducted analyses indicated there is not enough evidence to suggest that active transcranial direct current stimulation associated with brain-computer interface is more efficient in motor performance and functional independence when compared to sham transcranial direct current stimulation associated with brain-computer interface or brain-computer interface alone. In addition, the quality of evidence was rated very low. A subgroup analysis was performed for the motor performance outcome considering the injury severity level.<br><br>CONCLUSION: We found evidence that transcranial direct current stimulation associated with brain-computer interface was not more beneficial than sham transcranial direct current stimulation associated with brain-computer interface or brain-computer interface alone.}, }
@article {pmid37670009, year = {2023}, author = {Dreyer, P and Roc, A and Pillette, L and Rimbert, S and Lotte, F}, title = {A large EEG database with users' profile information for motor imagery brain-computer interface research.}, journal = {Scientific data}, volume = {10}, number = {1}, pages = {580}, pmid = {37670009}, issn = {2052-4463}, mesh = {Humans ; Algorithms ; *Brain-Computer Interfaces ; Databases, Factual ; *Electroencephalography ; Hand ; }, abstract = {We present and share a large database containing electroencephalographic signals from 87 human participants, collected during a single day of brain-computer interface (BCI) experiments, organized into 3 datasets (A, B, and C) that were all recorded using the same protocol: right and left hand motor imagery (MI). Each session contains 240 trials (120 per class), which represents more than 20,800 trials, or approximately 70 hours of recording time. It includes the performance of the associated BCI users, detailed information about the demographics, personality profile as well as some cognitive traits and the experimental instructions and codes (executed in the open-source platform OpenViBE). Such database could prove useful for various studies, including but not limited to: (1) studying the relationships between BCI users' profiles and their BCI performances, (2) studying how EEG signals properties varies for different users' profiles and MI tasks, (3) using the large number of participants to design cross-user BCI machine learning algorithms or (4) incorporating users' profile information into the design of EEG signal classification algorithms.}, }
@article {pmid37669261, year = {2023}, author = {Ramirez-Nava, AG and Mercado-Gutierrez, JA and Quinzaños-Fresnedo, J and Toledo-Peral, C and Vega-Martinez, G and Gutierrez, MI and Pacheco-Gallegos, MDR and Hernández-Arenas, C and Gutiérrez-Martínez, J}, title = {Functional electrical stimulation therapy controlled by a P300-based brain-computer interface, as a therapeutic alternative for upper limb motor function recovery in chronic post-stroke patients. A non-randomized pilot study.}, journal = {Frontiers in neurology}, volume = {14}, number = {}, pages = {1221160}, pmid = {37669261}, issn = {1664-2295}, abstract = {INTRODUCTION: Up to 80% of post-stroke patients present upper-limb motor impairment (ULMI), causing functional limitations in daily activities and loss of independence. UMLI is seldom fully recovered after stroke when using conventional therapeutic approaches. Functional Electrical Stimulation Therapy (FEST) controlled by Brain-Computer Interface (BCI) is an alternative that may induce neuroplastic changes, even in chronic post-stroke patients. The purpose of this work was to evaluate the effects of a P300-based BCI-controlled FEST intervention, for ULMI recovery of chronic post-stroke patients.<br><br>METHODS: A non-randomized pilot study was conducted, including 14 patients divided into 2 groups: BCI-FEST, and Conventional Therapy. Assessments of Upper limb functionality with Action Research Arm Test (ARAT), performance impairment with Fugl-Meyer assessment (FMA), Functional Independence Measure (FIM) and spasticity through Modified Ashworth Scale (MAS) were performed at baseline and after carrying out 20 therapy sessions, and the obtained scores compared using Chi square and Mann-Whitney U statistical tests (&#x1d6fc;&#x2009;=&#x2009;0.05).<br><br>RESULTS: After training, we found statistically significant differences between groups for FMA (p&#x2009;=&#x2009;0.012), ARAT (p&#x2009;<&#x2009;0.001), and FIM (p&#x2009;=&#x2009;0.025) scales.<br><br>DISCUSSION: It has been shown that FEST controlled by a P300-based BCI, may be more effective than conventional therapy to improve ULMI after stroke, regardless of chronicity.<br><br>CONCLUSION: The results of the proposed BCI-FEST intervention are promising, even for the most chronic post-stroke patients often relegated from novel interventions, whose expected recovery with conventional therapy is very low. It is necessary to carry out a randomized controlled trial in the future with a larger sample of patients.}, }
@article {pmid37668293, year = {2023}, author = {Gomez-Andres, A and Cerda-Company, X and Cucurell, D and Cunillera, T and Rodríguez-Fornells, A}, title = {Decoding agency attribution using single trial error-related brain potentials.}, journal = {Psychophysiology}, volume = {}, number = {}, pages = {e14434}, doi = {10.1111/psyp.14434}, pmid = {37668293}, issn = {1540-5958}, support = {BES-2016-078889//Ministerio de Econom&#xed;a y Competitividad/ ; PSI2015-69178-P//Ministerio de Econom&#xed;a y Competitividad/ ; PSI2016-79678-P//Ministerio de Econom&#xed;a y Competitividad/ ; }, abstract = {Being able to distinguish between self and externally generated actions is a key factor influencing learning and adaptive behavior. Previous literature has highlighted that whenever a person makes or perceives an error, a series of error-related potentials (ErrPs) can be detected in the electroencephalographic (EEG) signal, such as the error-related negativity (ERN) component. Recently, ErrPs have gained a lot of interest for the use in brain-computer interface (BCI) applications, which give the user the ability to communicate by means of decoding his/her brain activity. Here, we explored the feasibility of employing a support vector machine classifier to accurately disentangle self-agency errors from other-agency errors from the EEG signal at a single-trial level in a sample of 23 participants. Our results confirmed the viability of correctly disentangling self/internal versus other/external agency-error attributions at different stages of brain processing based on the latency and the spatial topographical distribution of key ErrP features, namely, the ERN and P600 components, respectively. These results offer a new perspective on how to distinguish self versus externally generated errors providing new potential implementations on BCI systems.}, }
@article {pmid37668071, year = {2023}, author = {Kheirabadi, R and Omranpour, H}, title = {Learning classifiers in clustered data: BCI pattern recognition model for EEG-based human emotion recognition.}, journal = {Computer methods in biomechanics and biomedical engineering}, volume = {}, number = {}, pages = {1-15}, doi = {10.1080/10255842.2023.2252953}, pmid = {37668071}, issn = {1476-8259}, abstract = {Evidence suggests that human emotions can be detected using Electroencephalography (EEG) brain signals. Recorded EEG signals, due to their large size, may not initially perform well in classification. For this reason, various feature selection methods are used to improve the performance of classification. The nature of EEG signals is complex and unstable. This article uses the Empirical Mode Decomposition (EMD) method, which is one of the most successful methods in analyzing these signals in recent years. In the proposed model, first, the EEG signals are decomposed using EMD into the number of Intrinsic Mode Functions (IMF), and then, the statistical properties of the IMFs are extracted. To improve the performance of the proposed model, using the RBF kernel and Least Absolute Shrinkage and Selection Operator (LASSO) feature selection, an effective subset of the features that have changed the space is selected. The data are then clustered, and finally, each cluster is classified with a decision tree and random forest and KNN. The purpose of clustering is to increase the accuracy of the classification, which is achieved by focusing each cluster on a limited number of classes. This experiment was performed on the DEAP dataset. The results show that the proposed model with 99.17% accuracy could perform better than recent research such as deep learning and show good performance. In the latest years, with the development of the BCI system, the demand for recognizing emotions based on EEG has increased. We provide a method for classifying clustered data that is efficient for high accuracy.}, }
@article {pmid37666761, year = {2023}, author = {Li, Q and Zhang, T and Song, Y and Liu, Y and Sun, M}, title = {[A design and evaluation of wearable p300 brain-computer interface system based on Hololens2].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {40}, number = {4}, pages = {709-717}, doi = {10.7507/1001-5515.202207055}, pmid = {37666761}, issn = {1001-5515}, mesh = {Humans ; *Amyotrophic Lateral Sclerosis ; *Brain-Computer Interfaces ; Quality of Life ; Event-Related Potentials, P300 ; *Wearable Electronic Devices ; }, abstract = {Patients with amyotrophic lateral sclerosis (ALS) often have difficulty in expressing their intentions through language and behavior, which prevents them from communicating properly with the outside world and seriously affects their quality of life. The brain-computer interface (BCI) has received much attention as an aid for ALS patients to communicate with the outside world, but the heavy device causes inconvenience to patients in the application process. To improve the portability of the BCI system, this paper proposed a wearable P300-speller brain-computer interface system based on the augmented reality (MR-BCI). This system used Hololens2 augmented reality device to present the paradigm, an OpenBCI device to capture EEG signals, and Jetson Nano embedded computer to process the data. Meanwhile, to optimize the system's performance for character recognition, this paper proposed a convolutional neural network classification method with low computational complexity applied to the embedded system for real-time classification. The results showed that compared with the P300-speller brain-computer interface system based on the computer screen (CS-BCI), MR-BCI induced an increase in the amplitude of the P300 component, an increase in accuracy of 1.7% and 1.4% in offline and online experiments, respectively, and an increase in the information transfer rate of 0.7 bit/min. The MR-BCI proposed in this paper achieves a wearable BCI system based on guaranteed system performance. It has a positive effect on the realization of the clinical application of BCI.}, }
@article {pmid37666760, year = {2023}, author = {Li, K and Lu, J and Yu, R and Zhang, R and Chen, M}, title = {[Alterations of β-γ coupling of scalp electroencephalography during epilepsy].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {40}, number = {4}, pages = {700-708}, doi = {10.7507/1001-5515.202212024}, pmid = {37666760}, issn = {1001-5515}, mesh = {Humans ; *Scalp ; *Epilepsy/diagnosis ; Brain ; Electroencephalography ; }, abstract = {Uncovering the alterations of neural interactions within the brain during epilepsy is important for the clinical diagnosis and treatment. Previous studies have shown that the phase-amplitude coupling (PAC) can be used as a potential biomarker for locating epileptic zones and characterizing the transition of epileptic phases. However, in contrast to the θ-γ coupling widely investigated in epilepsy, few studies have paid attention to the β-γ coupling, as well as its potential applications. In the current study, we use the modulation index (MI) to calculate the scalp electroencephalography (EEG)-based β-γ coupling and investigate the corresponding changes during different epileptic phases. The results show that the β-γ coupling of each brain region changes with the evolution of epilepsy, and in several brain regions, the β-γ coupling decreases during the ictal period but increases in the post-ictal period, where the differences are statistically significant. Moreover, the alterations of β-γ coupling between different brain regions can also be observed, and the strength of β-γ coupling increases in the post-ictal period, where the differences are also significant. Taken together, these findings not only contribute to understanding neural interactions within the brain during the evolution of epilepsy, but also provide a new insight into the clinical treatment.}, }
@article {pmid37666758, year = {2023}, author = {Luo, R and Dou, X and Xiao, X and Wu, Q and Xu, M and Ming, D}, title = {[Recognition of high-frequency steady-state visual evoked potential for brain-computer interface].}, journal = {Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi}, volume = {40}, number = {4}, pages = {683-691}, doi = {10.7507/1001-5515.202302034}, pmid = {37666758}, issn = {1001-5515}, mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Algorithms ; Discriminant Analysis ; Electroencephalography ; }, abstract = {Coding with high-frequency stimuli could alleviate the visual fatigue of users generated by the brain-computer interface (BCI) based on steady-state visual evoked potential (SSVEP). It would improve the comfort and safety of the system and has promising applications. However, most of the current advanced SSVEP decoding algorithms were compared and verified on low-frequency SSVEP datasets, and their recognition performance on high-frequency SSVEPs was still unknown. To address the aforementioned issue, electroencephalogram (EEG) data from 20 subjects were collected utilizing a high-frequency SSVEP paradigm. Then, the state-of-the-art SSVEP algorithms were compared, including 2 canonical correlation analysis algorithms, 3 task-related component analysis algorithms, and 1 task discriminant component analysis algorithm. The results indicated that they all could effectively decode high-frequency SSVEPs. Besides, there were differences in the classification performance and algorithms' speed under different conditions. This paper provides a basis for the selection of algorithms for high-frequency SSVEP-BCI, demonstrating its potential utility in developing user-friendly BCI.}, }
@article {pmid37666246, year = {2023}, author = {Lim, J and Wang, PT and Bashford, L and Kellis, S and Shaw Huang, S and Gong, H and Armacost, M and Heydari, P and Do, A and Andersen, RA and Liu, CY and Nenadic, Z}, title = {Suppression of cortical electrostimulation artifacts using pre-whitening and null projection.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acf68b}, pmid = {37666246}, issn = {1741-2552}, abstract = {Invasive brain-computer interfaces (BCIs) have shown promise in restoring motor function to those paralyzed by neurological injuries. These systems also have the ability to restore sensation via cortical electrostimulation. Cortical stimulation produces strong artifacts that can obscure neural signals or saturate recording amplifiers. While front-end hardware techniques can alleviate this problem, residual artifacts generally persist and must be suppressed by back-end methods. Approach: We have developed a technique based on pre-whitening and null projection (PWNP) and tested its ability to suppress stimulation artifacts in electroencephalogram (EEG), electrocorticogram (ECoG) and microelectrode array (MEA) signals from five human subjects. Main results: In EEG signals contaminated by narrow-band stimulation artifacts, the PWNP method achieved average artifact suppression between 32 and 34 dB, as measured by an increase in signal-to-interference ratio. In ECoG and MEA signals contaminated by broadband stimulation artifacts, our method suppressed artifacts by 78-80\% and 85\%, respectively, as measured by a reduction in interference index. When compared to independent component analysis, which is considered the state-of-the art technique for artifact suppression, our method achieved superior results, while being significantly easier to implement. Significance: PWNP can potentially act as an efficient method of artifact suppression to enable simultaneous stimulation and recording in bi-directional brain-computer interfaces to biomimetically restore motor function. .}, }
@article {pmid37665696, year = {2023}, author = {Sun, Y and Shen, A and Du, C and Sun, J and Chen, X and Gao, X}, title = {A Real-Time Non-Implantation Bi-Directional Brain-Computer Interface Solution without Stimulation Artifacts.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3311750}, pmid = {37665696}, issn = {1558-0210}, abstract = {The non-implantation bi-directional brain-computer interface (BCI) is a neural interface technology that enables direct two-way communication between the brain and the external world by both "reading" neural signals and "writing" stimulation patterns to the brain. This technology has vast potential applications, such as improving the quality of life for individuals with neurological and mental illnesses and even expanding the boundaries of human capabilities. Nonetheless, non-implantation bi-directional BCIs face challenges in generating real-time feedback and achieving compatibility between stimulation and recording. These issues arise due to the considerable overlap between electrical stimulation frequencies and electrophysiological recording frequencies, as well as the impediment caused by the skull to the interaction of external and internal currents. To address those challenges, this work proposes a novel solution that combines the temporal interference stimulation paradigm and minimally invasive skull modification. A longitudinal animal experiment has preliminarily validated the feasibility of the proposed method. In signal recording experiments, the average impedance of our scheme decreased by 4.59 kΩ, about 67%, compared to the conventional technique at 18 points. The peak-to-peak value of the Somatosensory Evoked Potential increased by 8%. Meanwhile, the signal-to-noise ratio of Steady-State Visual Evoked Potential increased by 5.13 dB, and its classification accuracy increased by 44%. The maximum bandwidth of the resting state rose by 63%. In electrical stimulation experiments, the signal-to-noise ratio of the low-frequency response evoked by our scheme rose by 8.04 dB, and no stimulation artifacts were generated. The experimental results show that signal quality in acquisition has significantly improved, and frequency-band isolation eliminates stimulation artifacts at the source. The acquisition and stimulation pathways are real-time compatible in this non-implantation bi-directional BCI solution, which can provide technical support and theoretical guidance for creating closed-loop adaptive systems coupled with particular application scenarios in the future.}, }
@article {pmid37663037, year = {2023}, author = {Cui, J and Yuan, L and Wang, Z and Li, R and Jiang, T}, title = {Towards best practice of interpreting deep learning models for EEG-based brain computer interfaces.}, journal = {Frontiers in computational neuroscience}, volume = {17}, number = {}, pages = {1232925}, pmid = {37663037}, issn = {1662-5188}, abstract = {INTRODUCTION: As deep learning has achieved state-of-the-art performance for many tasks of EEG-based BCI, many efforts have been made in recent years trying to understand what have been learned by the models. This is commonly done by generating a heatmap indicating to which extent each pixel of the input contributes to the final classification for a trained model. Despite the wide use, it is not yet understood to which extent the obtained interpretation results can be trusted and how accurate they can reflect the model decisions.<br><br>METHODS: We conduct studies to quantitatively evaluate seven different deep interpretation techniques across different models and datasets for EEG-based BCI.<br><br>RESULTS: The results reveal the importance of selecting a proper interpretation technique as the initial step. In addition, we also find that the quality of the interpretation results is inconsistent for individual samples despite when a method with an overall good performance is used. Many factors, including model structure and dataset types, could potentially affect the quality of the interpretation results.<br><br>DISCUSSION: Based on the observations, we propose a set of procedures that allow the interpretation results to be presented in an understandable and trusted way. We illustrate the usefulness of our method for EEG-based BCI with instances selected from different scenarios.}, }
@article {pmid37662108, year = {2023}, author = {Huang, Y and Zheng, J and Xu, B and Li, X and Liu, Y and Wang, Z and Feng, H and Cao, S}, title = {An improved model using convolutional sliding window-attention network for motor imagery EEG classification.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1204385}, pmid = {37662108}, issn = {1662-4548}, abstract = {INTRODUCTION: The classification model of motor imagery-based electroencephalogram (MI-EEG) is a new human-computer interface pattern and a new neural rehabilitation assessment method for diseases such as Parkinson's and stroke. However, existing MI-EEG models often suffer from insufficient richness of spatiotemporal feature extraction, learning ability, and dynamic selection ability.<br><br>METHODS: To solve these problems, this work proposed a convolutional sliding window-attention network (CSANet) model composed of novel spatiotemporal convolution, sliding window, and two-stage attention blocks.<br><br>RESULTS: The model outperformed existing state-of-the-art (SOTA) models in within- and between-individual classification tasks on commonly used MI-EEG datasets BCI-2a and Physionet MI-EEG, with classification accuracies improved by 4.22 and 2.02%, respectively.<br><br>DISCUSSION: The experimental results also demonstrated that the proposed type token, sliding window, and local and global multi-head self-attention mechanisms can significantly improve the model's ability to construct, learn, and adaptively select multi-scale spatiotemporal features in MI-EEG signals, and accurately identify electroencephalogram signals in the unilateral motor area. This work provided a novel and accurate classification model for MI-EEG brain-computer interface tasks and proposed a feasible neural rehabilitation assessment scheme based on the model, which could promote the further development and application of MI-EEG methods in neural rehabilitation.}, }
@article {pmid37662099, year = {2023}, author = {Zhang, J and Li, K and Yang, B and Han, X}, title = {Local and global convolutional transformer-based motor imagery EEG classification.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1219988}, pmid = {37662099}, issn = {1662-4548}, abstract = {Transformer, a deep learning model with the self-attention mechanism, combined with the convolution neural network (CNN) has been successfully applied for decoding electroencephalogram (EEG) signals in Motor Imagery (MI) Brain-Computer Interface (BCI). However, the extremely non-linear, nonstationary characteristics of the EEG signals limits the effectiveness and efficiency of the deep learning methods. In addition, the variety of subjects and the experimental sessions impact the model adaptability. In this study, we propose a local and global convolutional transformer-based approach for MI-EEG classification. The local transformer encoder is combined to dynamically extract temporal features and make up for the shortcomings of the CNN model. The spatial features from all channels and the difference in hemispheres are obtained to improve the robustness of the model. To acquire adequate temporal-spatial feature representations, we combine the global transformer encoder and Densely Connected Network to improve the information flow and reuse. To validate the performance of the proposed model, three scenarios including within-session, cross-session and two-session are designed. In the experiments, the proposed method achieves up to 1.46%, 7.49% and 7.46% accuracy improvement respectively in the three scenarios for the public Korean dataset compared with current state-of-the-art models. For the BCI competition IV 2a dataset, the proposed model also achieves a 2.12% and 2.21% improvement for the cross-session and two-session scenarios respectively. The results confirm that the proposed approach can effectively extract much richer set of MI features from the EEG signals and improve the performance in the BCI applications.}, }
@article {pmid37659393, year = {2023}, author = {Yu, H and Qi, Y and Pan, G}, title = {NeuSort: an automatic adaptive spike sorting approach with neuromorphic models.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acf61d}, pmid = {37659393}, issn = {1741-2552}, abstract = {OBJECTIVE: Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims to develop a novel online spike sorter, NeuSort, using neuromorphic models, with the ability to adaptively adjust to changes in neural signals, including waveform deformations and the appearance of new neurons.<br><br>APPROACH: NeuSort leverages a neuromorphic model to emulate template-matching processes. This model incorporates plasticity learning mechanisms inspired by biological neural systems, facilitating real-time adjustments to online parameters.<br><br>RESULTS: Experimental findings demonstrate NeuSort's ability to track neuron activities amidst waveform deformations and identify new neurons in real-time. NeuSort excels in handling non-stationary neural signals, significantly enhancing its applicability for long-term spike sorting tasks. Moreover, its implementation on neuromorphic chips guarantees ultra-low energy consumption during computation.<br><br>SIGNIFICANCE: NeuSort caters to the demand for real-time spike sorting in brain-machine interfaces through a neuromorphic approach. Its unsupervised, automated spike sorting process makes it a plug-and-play solution for online spike sorting.}, }
@article {pmid37659115, year = {2023}, author = {Zhang, D and Li, H and Xie, J and Li, D}, title = {MI-DAGSC: A domain adaptation approach incorporating comprehensive information from MI-EEG signals.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {167}, number = {}, pages = {183-198}, doi = {10.1016/j.neunet.2023.08.008}, pmid = {37659115}, issn = {1879-2782}, abstract = {Non-stationarity of EEG signals leads to high variability between subjects, making it challenging to directly use data from other subjects (source domain) for the classifier in the current subject (target domain). In this study, we propose MI-DAGSC to address domain adaptation challenges in EEG-based motor imagery (MI) decoding. By combining domain-level information, class-level information, and inter-sample structure information, our model effectively aligns the feature distributions of source and target domains. This work is an extension of our previous domain adaptation work MI-DABAN (Li et al., 2023). Based on MI-DABAN, MI-DAGSC designs Sample-Feature Blocks (SFBs) and Graph Convolution Blocks (GCBs) to focus on intra-sample and inter-sample information. The synergistic integration of SFBs and GCBs enable the model to capture comprehensive information and understand the relationship between samples, thus improving representation learning. Furthermore, we introduce a triplet loss to enhance the alignment and compactness of feature representations. Extensive experiments on real EEG datasets demonstrate the effectiveness of MI-DAGSC, confirming that our method makes a valuable contribution to the MI-EEG decoding. Moreover, it holds great potential for various applications in brain-computer interface systems and neuroscience research. And the code of the proposed architecture in this study is available under https://github.com/zhangdx21/MI-DAGSC.}, }
@article {pmid37657190, year = {2023}, author = {Leinders, S and Vansteensel, MJ and Piantoni, G and Branco, MP and Freudenburg, ZV and Gebbink, TA and Pels, EGM and Raemaekers, MAH and Schippers, A and Aarnoutse, EJ and Ramsey, NF}, title = {Using fMRI to localize target regions for implanted brain-computer interfaces in locked-in syndrome.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {155}, number = {}, pages = {1-15}, doi = {10.1016/j.clinph.2023.08.003}, pmid = {37657190}, issn = {1872-8952}, abstract = {OBJECTIVE: Electrocorticography (ECoG)-based brain-computer interface (BCI) systems have the potential to improve quality of life of people with locked-in syndrome (LIS) by restoring their ability to communicate independently. Before implantation of such a system, it is important to localize ECoG electrode target regions. Here, we assessed the predictive value of functional magnetic resonance imaging (fMRI) for the localization of suitable target regions on the sensorimotor cortex for ECoG-based BCI in people with locked-in syndrome.<br><br>METHODS: Three people with locked-in syndrome were implanted with a chronic, fully implantable ECoG-BCI system. We compared pre-surgical fMRI activity with post-implantation ECoG activity from areas known to be active and inactive during attempted hand movement (sensorimotor hand region and dorsolateral prefrontal cortex, respectively).<br><br>RESULTS: Results showed a spatial match between fMRI activity and changes in ECoG low and high frequency band power (10 - 30 and 65 - 95&#xa0;Hz, respectively) during attempted movement. Also, we found that fMRI can be used to select a sub-set of electrodes that show strong task-related signal changes that are therefore likely to generate adequate BCI control.<br><br>CONCLUSIONS: Our findings indicate that fMRI is a useful non-invasive tool for the pre-surgical workup of BCI implant candidates.<br><br>SIGNIFICANCE: If these results are confirmed in more BCI studies, fMRI might be used for more efficient surgical BCI procedures with focused cortical coverage and lower participant burden.}, }
@article {pmid37652289, year = {2023}, author = {Gu, B and Wang, K and Chen, L and He, J and Zhang, D and Xu, M and Wang, Z and Ming, D}, title = {Study of the Correlation between the Motor Ability of the Individual Upper Limbs and Motor Imagery Induced Neural Activities.}, journal = {Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuroscience.2023.08.032}, pmid = {37652289}, issn = {1873-7544}, abstract = {Motor imagery based brain-computer interfaces (MI-BCIs) have excellent application prospects in motor enhancement and rehabilitation. However, MI-induced electroencephalogram features applied to MI-BCI usually vary from person to person. This study aimed to investigate whether the motor ability of the individual upper limbs was associated with these features, which helps understand the causes of inter-subject variability. We focused on the behavioral and psychological factors reflecting motor abilities. We first obtained the behavioral scale scores from Edinburgh Handedness Questionnaire, Maximum Grip Strength Test, and Purdue Pegboard Test assessments to evaluate the motor execution ability. We also required the subjects to complete the psychological Movement Imagery Questionnaire-3 estimate, representing MI ability. Then we recorded EEG signals from all twenty-two subjects during MI tasks. Pearson correlation coefficient and stepwise regression were used to analyze the relationships between MI-induced relative event-related desynchronization (rERD) patterns and motor abilities. Both Purdue Pegboard Test and Movement Imagery Questionnaire-3 scores had significant correlations with MI-induced neural oscillation patterns. Notably, the Purdue Pegboard Test of the left hand had the most significant correlation with the alpha rERD. The results of stepwise multiple regression analysis showed that the Purdue Pegboard Test and Movement Imagery Questionnaire-3 could best predict the MI-induced rERD. The results demonstrate that hand dexterity and fine motor coordination are significantly related to MI-induced neural activities. In addition, the method of imagining is also relevant to MI features. Therefore, this study is meaningful for understanding individual differences and the design of user-centered MI-BCI.}, }
@article {pmid37651476, year = {2023}, author = {Meng, L and Jiang, X and Huang, J and Li, W and Luo, H and Wu, D}, title = {User Identity Protection in EEG-based Brain-Computer Interfaces: Supplementary Material.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3310883}, pmid = {37651476}, issn = {1558-0210}, abstract = {A brain-computer interface (BCI) establishes a direct communication pathway between the brain and an external device. Electroencephalogram (EEG) is the most popular input signal in BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals; however, EEG signals also contain rich private information, e.g., user identity, emotion, and so on, which should be protected. This paper first exposes a serious privacy problem in EEG-based BCIs, i.e., the user identity in EEG data can be easily learned so that different sessions of EEG data from the same user can be associated together to more reliably mine private information. To address this issue, we further propose two approaches to convert the original EEG data into identity-unlearnable EEG data, i.e., removing the user identity information while maintaining the good performance on the primary BCI task. Experiments on seven EEG datasets from five different BCI paradigms showed that on average the generated identity-unlearnable EEG data can reduce the user identification accuracy from 70.01% to at most 21.36%, greatly facilitating user privacy protection in EEG-based BCIs.}, }
@article {pmid37650101, year = {2023}, author = {Vargas, G and Araya, D and Sepulveda, P and Rodriguez-Fernandez, M and Friston, KJ and Sitaram, R and El-Deredy, W}, title = {Self-regulation learning as active inference: dynamic causal modeling of an fMRI neurofeedback task.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1212549}, pmid = {37650101}, issn = {1662-4548}, abstract = {INTRODUCTION: Learning to self-regulate brain activity by neurofeedback has been shown to lead to changes in the brain and behavior, with beneficial clinical and non-clinical outcomes. Neurofeedback uses a brain-computer interface to guide participants to change some feature of their brain activity. However, the neural mechanism of self-regulation learning remains unclear, with only 50% of the participants succeeding in achieving it. To bridge this knowledge gap, our study delves into the neural mechanisms of self-regulation learning via neurofeedback and investigates the brain processes associated with successful brain self-regulation.<br><br>METHODS: We study the neural underpinnings of self-regulation learning by employing dynamical causal modeling (DCM) in conjunction with real-time functional MRI data. The study involved a cohort of 18 participants undergoing neurofeedback training targeting the supplementary motor area. A critical focus was the comparison between top-down hierarchical connectivity models proposed by Active Inference and alternative bottom-up connectivity models like reinforcement learning.<br><br>RESULTS: Our analysis revealed a crucial distinction in brain connectivity patterns between successful and non-successful learners. Particularly, successful learners evinced a significantly stronger top-down effective connectivity towards the target area implicated in self-regulation. This heightened top-down network engagement closely resembles the patterns observed in goal-oriented and cognitive control studies, shedding light on the intricate cognitive processes intertwined with self-regulation learning.<br><br>DISCUSSION: The findings from our investigation underscore the significance of cognitive mechanisms in the process of self-regulation learning through neurofeedback. The observed stronger top-down effective connectivity in successful learners indicates the involvement of hierarchical cognitive control, which aligns with the tenets of Active Inference. This study contributes to a deeper understanding of the neural dynamics behind successful self-regulation learning and provides insights into the potential cognitive architecture underpinning this process.}, }
@article {pmid37649593, year = {2023}, author = {Sun, Y and Zabihi, M and Li, Q and Li, X and Kim, BJ and Ubogu, EE and Raja, SN and Wesselmann, U and Zhao, C}, title = {Drug Permeability: From the Blood-Brain Barrier to the Peripheral Nerve Barriers.}, journal = {Advanced therapeutics}, volume = {6}, number = {4}, pages = {}, pmid = {37649593}, issn = {2366-3987}, support = {R01 GM144388/GM/NIGMS NIH HHS/United States ; R21 NS078226/NS/NINDS NIH HHS/United States ; R01 NS075212/NS/NINDS NIH HHS/United States ; R15 GM139193/GM/NIGMS NIH HHS/United States ; R01 NS026363/NS/NINDS NIH HHS/United States ; R21 NS073702/NS/NINDS NIH HHS/United States ; }, abstract = {Drug delivery into the peripheral nerves and nerve roots has important implications for effective local anesthesia and treatment of peripheral neuropathies and chronic neuropathic pain. Similar to drugs that need to cross the blood-brain barrier (BBB) and blood-spinal cord barrier (BSCB) to gain access to the central nervous system (CNS), drugs must cross the peripheral nerve barriers (PNB), formed by the perineurium and blood-nerve barrier (BNB) to modulate peripheral axons. Despite significant progress made to develop effective strategies to enhance BBB permeability in therapeutic drug design, efforts to enhance drug permeability and retention in peripheral nerves and nerve roots are relatively understudied. Guided by knowledge describing structural, molecular and functional similarities between restrictive neural barriers in the CNS and peripheral nervous system (PNS), we hypothesize that certain CNS drug delivery strategies are adaptable for peripheral nerve drug delivery. In this review, we describe the molecular, structural and functional similarities and differences between the BBB and PNB, summarize and compare existing CNS and peripheral nerve drug delivery strategies, and discuss the potential application of selected CNS delivery strategies to improve efficacious drug entry for peripheral nerve disorders.}, }
@article {pmid37647178, year = {2023}, author = {Wang, H and Qi, Y and Yao, L and Wang, Y and Farina, D and Pan, G}, title = {A Human-Machine Joint Learning Framework to Boost Endogenous BCI Training.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2023.3305621}, pmid = {37647178}, issn = {2162-2388}, abstract = {Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. Endogenous BCIs based on electroencephalogram (EEG) signals, such as motor imagery (MI) BCIs, can provide some level of control. However, mastering spontaneous BCI control requires the users to generate discriminative and stable brain signal patterns by imagery, which is challenging and is usually achieved over a very long training time (weeks/months). Here, we propose a human-machine joint learning framework to boost the learning process in endogenous BCIs, by guiding the user to generate brain signals toward an optimal distribution estimated by the decoder, given the historical brain signals of the user. To this end, we first model the human-machine joint learning process in a uniform formulation. Then a human-machine joint learning framework is proposed: 1) for the human side, we model the learning process in a sequential trial-and-error scenario and propose a novel "copy/new" feedback paradigm to help shape the signal generation of the subject toward the optimal distribution and 2) for the machine side, we propose a novel adaptive learning algorithm to learn an optimal signal distribution along with the subject's learning process. Specifically, the decoder reweighs the brain signals generated by the subject to focus more on "good" samples to cope with the learning process of the subject. Online and psuedo-online BCI experiments with 18 healthy subjects demonstrated the advantages of the proposed joint learning process over coadaptive approaches in both learning efficiency and effectiveness.}, }
@article {pmid37645922, year = {2023}, author = {Natraj, N and Seko, S and Abiri, R and Yan, H and Graham, Y and Tu-Chan, A and Chang, EF and Ganguly, K}, title = {Flexible regulation of representations on a drifting manifold enables long-term stable complex neuroprosthetic control.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {37645922}, support = {DP2 HD087955/HD/NICHD NIH HHS/United States ; }, abstract = {The nervous system needs to balance the stability of neural representations with plasticity. It is unclear what is the representational stability of simple actions, particularly those that are well-rehearsed in humans, and how it changes in new contexts. Using an electrocorticography brain-computer interface (BCI), we found that the mesoscale manifold and relative representational distances for a repertoire of simple imagined movements were remarkably stable. Interestingly, however, the manifold's absolute location demonstrated day-to-day drift. Strikingly, representational statistics, especially variance, could be flexibly regulated to increase discernability during BCI control without somatotopic changes. Discernability strengthened with practice and was specific to the BCI, demonstrating remarkable contextual specificity. Accounting for drift, and leveraging the flexibility of representations, allowed neuroprosthetic control of a robotic arm and hand for over 7 months without recalibration. Our study offers insight into how electrocorticography can both track representational statistics across long periods and allow long-term complex neuroprosthetic control.}, }
@article {pmid37645733, year = {2023}, author = {Martinez, DRQ and Rubio, GF and Bonetti, L and Achyutuni, KG and Tzovara, A and Knight, RT and Vuust, P}, title = {Decoding reveals the neural representation of held and manipulated musical thoughts.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {37645733}, abstract = {UNLABELLED: Imagine a song you know by heart. With little effort you could sing it or play it vividly in your mind. However, we are only beginning to understand how the brain represents, holds, and manipulates these musical "thoughts". Here, we decoded listened and imagined melodies from MEG brain data (N = 71) to show that auditory regions represent the sensory properties of individual sounds, whereas cognitive control (prefrontal cortex, basal nuclei, thalamus) and episodic memory areas (inferior and medial temporal lobe, posterior cingulate, precuneus) hold and manipulate the melody as an abstract unit. Furthermore, the mental manipulation of a melody systematically changes its neural representation, reflecting the volitional control of auditory images. Our work sheds light on the nature and dynamics of auditory representations and paves the way for future work on neural decoding of auditory imagination.<br><br>SIGNIFICANCE STATEMENT: Imagining vividly a sequence of sounds is a skill that most humans exert with relatively little effort. However, it is unknown how the brain achieves such an outstanding feat. Here, we used decoding techniques and non-invasive electrophysiology to investigate how sequences of sounds are represented in the brain. We report that auditory regions represent the sensory properties of individual sounds while association areas represent melodies as abstract entities. Moreover, we show that mentally manipulating a melody changes its neural representation across the brain. Understanding auditory representations and their volitional control opens the path for future work on decoding of imagined auditory objects and possible applications in cognitive brain computer interfaces.}, }
@article {pmid37645689, year = {2023}, author = {Park, HJ and Lee, B}, title = {Multiclass classification of imagined speech EEG using noise-assisted multivariate empirical mode decomposition and multireceptive field convolutional neural network.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1186594}, pmid = {37645689}, issn = {1662-5161}, abstract = {INTRODUCTION: In this study, we classified electroencephalography (EEG) data of imagined speech using signal decomposition and multireceptive convolutional neural network. The imagined speech EEG with five vowels /a/, /e/, /i/, /o/, and /u/, and mute (rest) sounds were obtained from ten study participants.<br><br>MATERIALS AND METHODS: First, two different signal decomposition methods were applied for comparison: noise-assisted multivariate empirical mode decomposition and wavelet packet decomposition. Six statistical features were calculated from the decomposed eight sub-frequency bands EEG. Next, all features obtained from each channel of the trial were vectorized and used as the input vector of classifiers. Lastly, EEG was classified using multireceptive field convolutional neural network and several other classifiers for comparison.<br><br>RESULTS: We achieved an average classification rate of 73.09 and up to 80.41% in a multiclass (six classes) setup (Chance: 16.67%). In comparison with various other classifiers, significant improvements for other classifiers were achieved (p-value < 0.05). From the frequency sub-band analysis, high-frequency band regions and the lowest-frequency band region contain more information about imagined vowel EEG data. The misclassification and classification rate of each vowel imaginary EEG was analyzed through a confusion matrix.<br><br>DISCUSSION: Imagined speech EEG can be classified successfully using the proposed signal decomposition method and a convolutional neural network. The proposed classification method for imagined speech EEG can contribute to developing a practical imagined speech-based brain-computer interfaces system.}, }
@article {pmid37643110, year = {2023}, author = {Dong, Y and Tang, X and Li, Q and Wang, Y and Jiang, N and Tian, L and Zheng, Y and Li, X and Zhao, S and Li, G and Fang, P}, title = {An Approach for EEG Denoising Based on Wasserstein Generative Adversarial Network.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3309815}, pmid = {37643110}, issn = {1558-0210}, abstract = {Electroencephalogram (EEG) recordings often contain artifacts that would lower signal quality. Many efforts have been made to eliminate or at least minimize the artifacts, and most of them rely on visual inspection and manual operations, which is time/labor-consuming, subjective, and incompatible to filter massive EEG data in real-time. In this paper, we proposed a deep learning framework named Artifact Removal Wasserstein Generative Adversarial Network (AR-WGAN), where the well-trained model can decompose input EEG, detect and delete artifacts, and then reconstruct denoised signals within a short time. The proposed approach was systematically compared with commonly used denoising methods including Denoised AutoEncoder, Wiener Filter, and Empirical Mode Decomposition, with both public and self-collected datasets. The experimental results proved the promising performance of AR-WGAN on automatic artifact removal for massive data across subjects, with correlation coefficient up to 0.726±0.033, and temporal and spatial relative root-mean-square error as low as 0.176±0.046 and 0.761±0.046, respectively. This work may demonstrate the proposed AR-WGAN as a high-performance end-to-end method for EEG denoising, with many on-line applications in clinical EEG monitoring and brain-computer interfaces.}, }
@article {pmid37640768, year = {2023}, author = {Saal, J and Ottenhoff, MC and Kubben, PL and Colon, AJ and Goulis, S and van Dijk, JP and Krusienski, DJ and Herff, C}, title = {Towards hippocampal navigation for brain-computer interfaces.}, journal = {Scientific reports}, volume = {13}, number = {1}, pages = {14021}, pmid = {37640768}, issn = {2045-2322}, mesh = {Humans ; *Brain-Computer Interfaces ; Hand ; Hippocampus ; Intention ; Movement ; }, abstract = {Automatic wheelchairs directly controlled by brain activity could provide autonomy to severely paralyzed individuals. Current approaches mostly rely on non-invasive measures of brain activity and translate individual commands into wheelchair movements. For example, an imagined movement of the right hand would steer the wheelchair to the right. No research has investigated decoding higher-order cognitive processes to accomplish wheelchair control. We envision an invasive neural prosthetic that could provide input for wheelchair control by decoding navigational intent from hippocampal signals. Navigation has been extensively investigated in hippocampal recordings, but not for the development of neural prostheses. Here we show that it is possible to train a decoder to classify virtual-movement speeds from hippocampal signals recorded during a virtual-navigation task. These results represent the first step toward exploring the feasibility of an invasive hippocampal BCI for wheelchair control.}, }
@article {pmid37639501, year = {2023}, author = {Cui, Z and Wu, B and Blank, I and Yu, Y and Gu, J and Zhou, T and Zhang, Y and Wang, W and Liu, Y}, title = {TastePeptides-EEG: An Ensemble Model for Umami Taste Evaluation Based on Electroencephalogram and Machine Learning.}, journal = {Journal of agricultural and food chemistry}, volume = {}, number = {}, pages = {}, doi = {10.1021/acs.jafc.3c04611}, pmid = {37639501}, issn = {1520-5118}, abstract = {In the field of food, the sensory evaluation of food still relies on the results of manual sensory evaluation, but the results of human sensory evaluation are not universal, and there is a problem of speech fraud. This work proposed an electroencephalography (EEG)-based analysis method that effectively enables the identification of umami/non-umami substances. First, the key features were extracted using percentage conversion, standardization, and significance screening, and based on these features, the top four models were selected from 19 common binary classification algorithms as submodels. Then, the support vector machine (SVM) algorithm was used to fit the outputs of these four submodels to establish TastePeptides-EEG. The validation set of the model achieved a judgment accuracy of 90.2%, and the test set achieved a judgment accuracy of 77.8%. This study discovered the frequency change of α wave in umami taste perception and found the frequency response delay phenomenon of the F/RT/C area under umami taste stimulation for the first time. The model is published at www.tastepeptides-meta.com/TastePeptides-EEG, which is convenient for relevant researchers to speed up the analysis of umami perception and provide help for the development of the next generation of brain-computer interfaces for flavor perception.}, }
@article {pmid37639414, year = {2023}, author = {Lan, W and Wang, R and He, Y and Zong, Y and Leng, Y and Iramina, K and Zheng, W and Ge, S}, title = {Cross Domain Correlation Maximization for Enhancing the Target Recognition of SSVEP-Based Brain-Computer Interfaces.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3309543}, pmid = {37639414}, issn = {1558-0210}, abstract = {The target recognition performance of steady-state visual evoked potential (SSVEP)-based brain-computer interfaces can be significantly improved with a training-based approach. However, the training procedure is time consuming and often causes fatigue. Consequently, the number of training data should be limited, which may reduce the classification performance. Thus, how to improve classification accuracy without increasing the training time is crucial to SSVEP-based BCI system. This study proposes a transfer-related component analysis (TransRCA) method for addressing the above issue. In this method, the SSVEP-related components are extracted from a small number of training data of the current individual and combined with those extracted from a large number of existing training data of other individuals. The TransRCA method maximizes not only the inter-trial covariances between the source and target subjects, but also the correlation between the reference signals and SSVEP signals from the source and target subjects. The proposed method was validated on the SSVEP public Benchmark and BETA datasets, and the classification accuracy and information transmission rate of the ensemble version of the proposed TransRCA method were compared with those of the state-of-the-art eCCA, eTRCA, ttCCA, LSTeTRCA, and eIISMC methods on both datasets. The comparison results indicate that the proposed method provides a superior performance compared with these state-of-the-art methods, and thus has high potential for the development of a SSVEP-based brain-computer interface system with high classification performance that only uses a small number of training data.}, }
@article {pmid37636067, year = {2023}, author = {Iwane, F and Sobolewski, A and Chavarriaga, R and Millán, JDR}, title = {EEG error-related potentials encode magnitude of errors and individual perceptual thresholds.}, journal = {iScience}, volume = {26}, number = {9}, pages = {107524}, pmid = {37636067}, issn = {2589-0042}, abstract = {Error-related potentials (ErrPs) are a prominent electroencephalogram (EEG) correlate of performance monitoring, and so crucial for learning and adapting our behavior. It is poorly understood whether ErrPs encode further information beyond error awareness. We report an experiment with sixteen participants over three sessions in which occasional visual rotations of varying magnitude occurred during a cursor reaching task. We designed a brain-computer interface (BCI) to detect ErrPs that provided real-time feedback. The individual ErrP-BCI decoders exhibited good transfer across sessions and scalability over the magnitude of errors. A non-linear relationship between the ErrP-BCI output and the magnitude of errors predicts individual perceptual thresholds to detect errors. We also reveal theta-gamma oscillatory coupling that co-varied with the magnitude of the required adjustment. Our findings open new avenues to probe and extend current theories of performance monitoring by incorporating continuous human interaction tasks and analysis of the ErrP complex rather than individual peaks.}, }
@article {pmid37635251, year = {2023}, author = {Prescott, RA and Pankow, AP and de Vries, M and Crosse, KM and Patel, RS and Alu, M and Loomis, C and Torres, V and Koralov, S and Ivanova, E and Dittmann, M and Rosenberg, BR}, title = {A comparative study of in vitro air-liquid interface culture models of the human airway epithelium evaluating cellular heterogeneity and gene expression at single cell resolution.}, journal = {Respiratory research}, volume = {24}, number = {1}, pages = {213}, pmid = {37635251}, issn = {1465-993X}, support = {R01 AI151029/AI/NIAID NIH HHS/United States ; }, mesh = {Humans ; Epithelium ; *Epithelial Cells ; Cell Differentiation ; *Interferons ; Gene Expression ; }, abstract = {BACKGROUND: The airway epithelium is composed of diverse cell types with specialized functions that mediate homeostasis and protect against respiratory pathogens. Human airway epithelial (HAE) cultures at air-liquid interface are a physiologically relevant in vitro model of this heterogeneous tissue and have enabled numerous studies of airway disease. HAE cultures are classically derived from primary epithelial cells, the relatively limited passage capacity of which can limit experimental methods and study designs. BCi-NS1.1, a previously described and widely used basal cell line engineered to express hTERT, exhibits extended passage lifespan while retaining the capacity for differentiation to HAE. However, gene expression and innate immune function in BCi-NS1.1-derived versus primary-derived HAE cultures have not been fully characterized.<br><br>METHODS: BCi-NS1.1-derived HAE cultures (n&#x2009;=&#x2009;3 independent differentiations) and primary-derived HAE cultures (n&#x2009;=&#x2009;3 distinct donors) were characterized by immunofluorescence and single cell RNA-Seq (scRNA-Seq). Innate immune functions were evaluated in response to interferon stimulation and to infection with viral and bacterial respiratory pathogens.<br><br>RESULTS: We confirm at high resolution that BCi-NS1.1- and primary-derived HAE cultures are largely similar in morphology, cell type composition, and overall gene expression patterns. While we observed cell-type specific expression differences of several interferon stimulated genes in BCi-NS1.1-derived HAE cultures, we did not observe significant differences in susceptibility to infection with influenza A virus and Staphylococcus aureus.<br><br>CONCLUSIONS: Taken together, our results further support BCi-NS1.1-derived HAE cultures as a valuable tool for the study of airway infectious disease.}, }
@article {pmid37629532, year = {2023}, author = {Vatrano, M and Nemirovsky, IE and Tonin, P and Riganello, F}, title = {Assessing Consciousness through Neurofeedback and Neuromodulation: Possibilities and Challenges.}, journal = {Life (Basel, Switzerland)}, volume = {13}, number = {8}, pages = {}, pmid = {37629532}, issn = {2075-1729}, abstract = {Neurofeedback is a non-invasive therapeutic approach that has gained traction in recent years, showing promising results for various neurological and psychiatric conditions. It involves real-time monitoring of brain activity, allowing individuals to gain control over their own brainwaves and improve cognitive performance or alleviate symptoms. The use of electroencephalography (EEG), such as brain-computer interface (BCI), transcranial direct current stimulation (tDCS), and transcranial magnetic stimulation (TMS), has been instrumental in developing neurofeedback techniques. However, the application of these tools in patients with disorders of consciousness (DoC) presents unique challenges. In this narrative review, we explore the use of neurofeedback in treating patients with DoC. More specifically, we discuss the advantages and challenges of using tools such as EEG neurofeedback, tDCS, TMS, and BCI for these conditions. Ultimately, we hope to provide the neuroscientific community with a comprehensive overview of neurofeedback and emphasize its potential therapeutic applications in severe cases of impaired consciousness levels.}, }
@article {pmid37628493, year = {2023}, author = {Moreno Escobar, JJ and Morales Matamoros, O and Aguilar Del Villar, EY and Quintana Espinosa, H and Chanona Hernández, L}, title = {DS-CNN: Deep Convolutional Neural Networks for Facial Emotion Detection in Children with Down Syndrome during Dolphin-Assisted Therapy.}, journal = {Healthcare (Basel, Switzerland)}, volume = {11}, number = {16}, pages = {}, pmid = {37628493}, issn = {2227-9032}, support = {20230629//Instituto Polit&#xe9;cnico Nacional/ ; }, abstract = {In Mexico, according to data from the General Directorate of Health Information (2018), there is an annual incidence of 689 newborns with Trisomy 21, well-known as Down Syndrome. Worldwide, this incidence is estimated between 1 in every 1000 newborns, approximately. That is why this work focuses on the detection and analysis of facial emotions in children with Down Syndrome in order to predict their emotions throughout a dolphin-assisted therapy. In this work, two databases are used: Exploratory Data Analysis, with a total of 20,214 images, and the Down's Syndrome Dataset database, with 1445 images for training, validation, and testing of the neural network models. The construction of two architectures based on a Deep Convolutional Neural Network manages an efficiency of 79%, when these architectures are tested with a large reference image database. Then, the architecture that achieves better results is trained, validated, and tested in a small-image database with the facial emotions of children with Down Syndrome, obtaining an efficiency of 72%. However, this increases by 9% when the brain activity of the child is included in the training, resulting in an average precision of 81%. Using electroencephalogram (EEG) signals in a Convolutional Neural Network (CNN) along with the Down's Syndrome Dataset (DSDS) has promising advantages in the field of brain-computer interfaces. EEG provides direct access to the electrical activity of the brain, allowing for real-time monitoring and analysis of cognitive states. Integrating EEG signals into a CNN architecture can enhance learning and decision-making capabilities. It is important to note that this work has the primary objective of addressing a doubly vulnerable population, as these children also have a disability.}, }
@article {pmid37627797, year = {2023}, author = {Li, M and Qi, Y and Pan, G}, title = {Encrypt with Your Mind: Reliable and Revocable Brain Biometrics via Multidimensional Gaussian Fitted Bit Allocation.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {10}, number = {8}, pages = {}, pmid = {37627797}, issn = {2306-5354}, support = {2021ZD0200400//China Brain Project/ ; U1909202 and 61925603//National Natural Science Foundation of China/ ; 2020C03004//Key Research and Development Program of Zhejiang Province/ ; }, abstract = {Biometric features, e.g., fingerprints, the iris, and the face, have been widely used to authenticate individuals. However, most biometrics are not cancellable, i.e., once these biometric features are cloned or stolen, they cannot be replaced easily. Unlike traditional biometrics, brain biometrics are extremely difficult to clone or forge due to the natural randomness across different individuals, which makes them an ideal option for identity authentication. Most existing brain biometrics are based on electroencephalogram (EEG), which is usually demonstrated unstable performance due to the low signal-to-noise ratio (SNR). For the first time, we propose the use of intracortical brain signals, which have higher resolution and SNR, to realize the construction of the high-performance brain biometrics. Specifically, we put forward a novel brain-based key generation approach called multidimensional Gaussian fitted bit allocation (MGFBA). The proposed MGFBA method extracts keys from the local field potential of ten rats with high reliability and high entropy. We found that with the proposed MGFBA, the average effective key length of the brain biometrics was 938 bits, while achieving high authentication accuracy of 88.1% at a false acceptance rate of 1.9%, which is significantly improved compared to conventional EEG-based approaches. In addition, the proposed MGFBA-based keys can be conveniently revoked using different motor behaviors with high entropy. Experimental results demonstrate the potential of using intracortical brain signals for reliable authentication and other security applications.}, }
@article {pmid37626528, year = {2023}, author = {Cervantes, JA and López, S and Cervantes, S and Hernández, A and Duarte, H}, title = {Social Robots and Brain-Computer Interface Video Games for Dealing with Attention Deficit Hyperactivity Disorder: A Systematic Review.}, journal = {Brain sciences}, volume = {13}, number = {8}, pages = {}, pmid = {37626528}, issn = {2076-3425}, abstract = {Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity that affects a large number of young people in the world. The current treatments for children living with ADHD combine different approaches, such as pharmacological, behavioral, cognitive, and psychological treatment. However, the computer science research community has been working on developing non-pharmacological treatments based on novel technologies for dealing with ADHD. For instance, social robots are physically embodied agents with some autonomy and social interaction capabilities. Nowadays, these social robots are used in therapy sessions as a mediator between therapists and children living with ADHD. Another novel technology for dealing with ADHD is serious video games based on a brain-computer interface (BCI). These BCI video games can offer cognitive and neurofeedback training to children living with ADHD. This paper presents a systematic review of the current state of the art of these two technologies. As a result of this review, we identified the maturation level of systems based on these technologies and how they have been evaluated. Additionally, we have highlighted ethical and technological challenges that must be faced to improve these recently introduced technologies in healthcare.}, }
@article {pmid37626506, year = {2023}, author = {Canale, A and Urbanelli, A and Gragnano, M and Bordino, V and Albera, A}, title = {Comparison of Active Bone Conduction Hearing Implant Systems in Unilateral and Bilateral Conductive or Mixed Hearing Loss.}, journal = {Brain sciences}, volume = {13}, number = {8}, pages = {}, pmid = {37626506}, issn = {2076-3425}, abstract = {BACKGROUND: To assess and compare binaural benefits and subjective satisfaction of active bone conduction implant (BCI) in patients with bilateral conductive or mixed hearing loss fitted with bilateral BCI and patients with monaural conductive hearing loss fitted with monaural BCI.<br><br>METHODS: ITA Matrix test was performed both on patients affected by bilateral conductive or mixed hearing loss fitted with monaural bone conduction hearing implant (Bonebridge, Med-El) before and after implantation of contralateral bone conduction hearing implant and on patients with monaural conductive or mixed hearing loss before and after implantation of monaural BCI. The Abbreviated Profile of Hearing Aid Benefit (APHAB) questionnaire was administered to both groups of subjects and the results were compared with each other.<br><br>RESULTS: Patients of group 1 reported a difference of 4.66 dB in the summation setting compared to 0.79 dB of group 2 (p < 0.05). In the squelch setting, group 1 showed a difference of 2.42 dB compared to 1.53 dB of group 2 (p = 0.85). In the head shadow setting, patients of group 1 reported a difference of 7.5 dB, compared to 4.61 dB of group 2 (p = 0.34). As for the APHAB questionnaire, group 1 reported a mean global score difference of 11.10% while group 2 showed a difference of -4.00%.<br><br>CONCLUSIONS: Bilateral BCI in patients affected by bilateral conductive or mixed hearing loss might show more advantages in terms of sound localisation, speech perception in noise and subjective satisfaction if compared to unilateral BCI fitting in patients affected by unilateral conductive hearing impairment. This may be explained by the different individual transcranial attenuation of each subject, which might lead to different outcomes in terms of binaural hearing achievement. On the other hand, patients with unilateral conductive or mixed hearing loss fitted with monaural BCI achieved good results in terms of binaural hearing and for this reason, there is no absolute contraindication to implantation in those patients.}, }
@article {pmid37626499, year = {2023}, author = {Cai, J and Xu, M and Cai, H and Jiang, Y and Zheng, X and Sun, H and Sun, Y and Sun, Y}, title = {Task Cortical Connectivity Reveals Different Network Reorganizations between Mild Stroke Patients with Cortical and Subcortical Lesions.}, journal = {Brain sciences}, volume = {13}, number = {8}, pages = {}, pmid = {37626499}, issn = {2076-3425}, support = {LR23F010003//Zhejiang Provincial Natural Science Foundation/ ; 82172056//National Natural Science Foundation of China/ ; }, abstract = {Accumulating efforts have been made to investigate cognitive impairment in stroke patients, but little has been focused on mild stroke. Research on the impact of mild stroke and different lesion locations on cognitive impairment is still limited. To investigate the underlying mechanisms of cognitive dysfunction in mild stroke at different lesion locations, electroencephalograms (EEGs) were recorded in three groups (40 patients with cortical stroke (CS), 40 patients with subcortical stroke (SS), and 40 healthy controls (HC)) during a visual oddball task. Power envelope connectivity (PEC) was constructed based on EEG source signals, followed by graph theory analysis to quantitatively assess functional brain network properties. A classification framework was further applied to explore the feasibility of PEC in the identification of mild stroke. The results showed worse behavioral performance in the patient groups, and PECs with significant differences among three groups showed complex distribution patterns in frequency bands and the cortex. In the delta band, the global efficiency was significantly higher in HC than in CS (p = 0.011), while local efficiency was significantly increased in SS than in CS (p = 0.038). In the beta band, the small-worldness was significantly increased in HC compared to CS (p = 0.004). Moreover, the satisfactory classification results (76.25% in HC vs. CS, and 80.00% in HC vs. SS) validate the potential of PECs as a biomarker in the detection of mild stroke. Our findings offer some new quantitative insights into the complex mechanisms of cognitive impairment in mild stroke at different lesion locations, which may facilitate post-stroke cognitive rehabilitation.}, }
@article {pmid37625688, year = {2023}, author = {Zapała, D and Augustynowicz, P and Tokovarov, M and Iwanowicz, P and Droździel, P}, title = {Brief visual deprivation effects on brain oscillations during kinesthetic and visual-motor imagery.}, journal = {Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuroscience.2023.08.022}, pmid = {37625688}, issn = {1873-7544}, abstract = {It is widely recognized that opening and closing the eyes can direct attention to external or internal stimuli processing. This has been confirmed by studies showing the effects of changes in visual stimulation changes on cerebral activity during different tasks, e.g., motor imagery and execution. However, an essential aspect of creating a mental representation of motion, such as imagery perspective, has not yet been investigated in the present context. Our study aimed to verify the effect of brief visual deprivation (under eyes open [EO] and eyes closed [EC] conditions) on brain wave oscillations and behavioral performance during kinesthetic imagery (KMI) and visual-motor imagery (VMI) tasks. We focused on the alpha and beta rhythms from visual- and motor-related EEG activity sources. Additionally, we used machine learning algorithms to establish whether the registered differences in brain oscillations might affect motor imagery brain-computer interface (MI-BCI) performance. The results showed that the occipital areas in the EC condition presented significantly stronger desynchronization during VMI tasks, which is typical for enhanced visual stimuli processing. Furthermore, the stronger desynchronization of alpha rhythms from motor areas in the EO, than EC condition confirmed previous effects obtained during real movements. It was also found that simulating movement under EC/EO conditions affected signal classification accuracy, which has practical implications for MI-BCI effectiveness. These findings suggest that shifting processing toward external or internal stimuli modulates brain rhythm oscillations associated with different perspectives on the mental representation of movement.}, }
@article {pmid37624718, year = {2023}, author = {Zhang, Y and Qian, K and Xie, SQ and Shi, C and Li, J and Zhang, ZQ}, title = {SSVEP-based Brain-Computer Interface Controlled Robotic Platform with Velocity Modulation.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3308778}, pmid = {37624718}, issn = {1558-0210}, abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have been extensively studied due to many benefits, such as non-invasiveness, high information transfer rate, and ease of use. SSVEP-based BCI has been investigated in various applications by projecting brain signals to robot control commands. However, the movement direction and speed are generally fixed and prescribed, neglecting the user's requirement for velocity changes during practical implementations. In this study, we proposed a velocity modulation method based on stimulus brightness for controlling the robotic arm in the SSVEP-based BCI system. A stimulation interface was designed, incorporating flickers, target and a cursor workspace. The synchronization of the cursor and robotic arm does not require the subject's eye switch between the stimuli and the robot. The feature vector consists of the characteristics of the signal and the classification result. Subsequently, the Gaussian mixture model (GMM) and Bayesian inference were used to calculate the posterior probabilities that the signal came from a high or low brightness flicker. A brain-actuated speed function was designed by incorporating the posterior probability difference. Finally, the historical velocity was considered to determine the final velocity. To demonstrate the effectiveness of the proposed method, online experiments, including single- and multi-target reaching tasks, were conducted. The extensive experimental results validated the feasibility of the proposed method in reducing reaching time and achieving proximity to the target.}, }
@article {pmid37624717, year = {2023}, author = {Zhang, R and Dong, G and Li, M and Tang, Z and Chen, X and Cui, H}, title = {A calibration-free hybrid BCI speller system based on high-frequency SSVEP and sEMG.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3308779}, pmid = {37624717}, issn = {1558-0210}, abstract = {Hybrid brain-computer interface (hBCI) systems that combine steady-state visual evoked potential (SSVEP) and surface electromyography (sEMG) signals have attracted attention of researchers due to the advantage of exhibiting significantly improved system performance. However, almost all existing studies adopt low-frequency SSVEP to build hBCI. It produces much more visual fatigue than high-frequency SSVEP. Therefore, the current study attempts to build a hBCI based on high-frequency SSVEP and sEMG. With these two signals, this study designed and realized a 32-target hBCI speller system. Thirty-two targets were separated from the middle into two groups. Each side contained 16 sets of targets with different high-frequency visual stimuli (i.e., 31-34.75 Hz with an interval of 0.25 Hz). sEMG was utilized to choose the group and SSVEP was adopted to identify intra-group targets. The filter bank canonical correlation analysis (FBCCA) and the root mean square value (RMS) methods were used to identify signals. Therefore, the proposed system allowed users to operate it without system calibration. A total of 12 healthy subjects participated in online experiment, with an average accuracy of 93.52 ± 1.66% and the average information transfer rate (ITR) reached 93.50 ± 3.10 bits/min. Furthermore, 12 participants perfectly completed the free-spelling tasks. These results of the experiments indicated feasibility and practicality of the proposed hybrid BCI speller system.}, }
@article {pmid37623163, year = {2023}, author = {Blom, C and Reis, A and Lencastre, L}, title = {Caregiver Quality of Life: Satisfaction and Burnout.}, journal = {International journal of environmental research and public health}, volume = {20}, number = {16}, pages = {}, pmid = {37623163}, issn = {1660-4601}, support = {UIDB/00050/2020//Funda&#xe7;&#xe3;o para a Ci&#xea;ncia e Tecnologia/ ; }, mesh = {Adult ; Humans ; *Caregivers ; *Quality of Life ; Cross-Sectional Studies ; Burnout, Psychological ; Health Personnel ; }, abstract = {Informal caregivers (ICs) of cancer patients play a crucial role in health care. Several of the challenges they face can affect their quality of life (QoL). This cross-sectional study explored role of burnout and caregiving satisfaction in their relationship to QoL. Portuguese ICs of adult cancer patients (N = 92) answered a sociodemographic and caregiving questionnaire, the WHOQOL-SRPB BREF, assessing physical, psychological, social, environmental, and spiritual QoL domains; the Maslach Burnout Interview, assessing the dimensions of depersonalization, emotional exhaustion, and personal accomplishment; and a Visual Analogic Scale on caregiving satisfaction. We tested correlations and a parallel mediation model for each domain of QoL, considering burnout dimensions as possible mediators between satisfaction and QoL domains. Our results show that satisfaction, burnout dimensions, and almost all QoL domains are correlated. Together, burnout dimensions seem to mediate the relationship between caregiving satisfaction and psychological, environmental, and spiritual QoL. Satisfaction had a significant indirect effect solely through emotional exhaustion on psychological QoL (β = 1.615, 95% BCI [0.590; 2.849]), environmental QoL (β = 0.904, 95% BCI [0.164; 1.876]), and spiritual QoL (β = 0.816, 95% BCI [0.019; 1.792]). It seems essential for mental health professionals to address these dimensions when providing support to an IC.}, }
@article {pmid37621168, year = {2023}, author = {Tesch, ME}, title = {Precision medicine in extended adjuvant endocrine therapy for breast cancer.}, journal = {Current opinion in oncology}, volume = {}, number = {}, pages = {}, doi = {10.1097/CCO.0000000000000985}, pmid = {37621168}, issn = {1531-703X}, abstract = {PURPOSE OF REVIEW: In this review, the evolving role of currently available genomic assays for hormone receptor-positive, early-stage breast cancer in the selection of patients for extended adjuvant endocrine therapy will be discussed.<br><br>RECENT FINDINGS: Several studies have investigated the prognostic performance of the Oncotype DX, Breast Cancer Index (BCI), Prosigna, and EndoPredict genomic assays in the late recurrence setting (>5&#x200a;years after diagnosis), beyond standardly used clinicopathologic parameters, with mixed results. Recently, BCI has also been validated to predict the likelihood of benefit from extended endocrine therapy, though certain data limitations may need to be addressed to justify routine use in clinical practice.<br><br>SUMMARY: Even after 5&#x200a;years of adjuvant endocrine therapy, patients with hormone receptor-positive breast cancer have a significant risk for late recurrence, including distant metastases, that might be prevented with longer durations of endocrine therapy. However, the added toxicity and variable benefit derived from extended endocrine therapy make optimal patient selection crucial. Genomic assays are in development to risk-stratify patients for late recurrence and determine efficacy of extended endocrine therapy, with the aim to help guide extended endocrine therapy decisions for clinicians and individualize treatment strategies for patients.}, }
@article {pmid37619325, year = {2023}, author = {Borra, D and Mondini, V and Magosso, E and Müller-Putz, GR}, title = {Decoding movement kinematics from EEG using an interpretable convolutional neural network.}, journal = {Computers in biology and medicine}, volume = {165}, number = {}, pages = {107323}, doi = {10.1016/j.compbiomed.2023.107323}, pmid = {37619325}, issn = {1879-0534}, abstract = {Continuous decoding of hand kinematics has been recently explored for the intuitive control of electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs). Deep neural networks (DNNs) are emerging as powerful decoders, for their ability to automatically learn features from lightly pre-processed signals. However, DNNs for kinematics decoding lack in the interpretability of the learned features and are only used to realize within-subject decoders without testing other training approaches potentially beneficial for reducing calibration time, such as transfer learning. Here, we aim to overcome these limitations by using an interpretable convolutional neural network (ICNN) to decode 2-D hand kinematics (position and velocity) from EEG in a pursuit tracking task performed by 13 participants. The ICNN is trained using both within-subject and cross-subject strategies, and also testing the feasibility of transferring the knowledge learned on other subjects on a new one. Moreover, the network eases the interpretation of learned spectral and spatial EEG features. Our ICNN outperformed most of the other state-of-the-art decoders, showing the best trade-off between performance, size, and training time. Furthermore, transfer learning improved kinematics prediction in the low data regime. The network attributed the highest relevance for decoding to the delta-band across all subjects, and to higher frequencies (alpha, beta, low-gamma) for a cluster of them; contralateral central and parieto-occipital sites were the most relevant, reflecting the involvement of sensorimotor, visual and visuo-motor processing. The approach improved the quality of kinematics prediction from the EEG, at the same time allowing interpretation of the most relevant spectral and spatial features.}, }
@article {pmid37616245, year = {2023}, author = {Porr, B and Bohollo, LM}, title = {BCI-Walls: A robust methodology to predict if conscious EEG changes can be detected in the presence of artefacts.}, journal = {PloS one}, volume = {18}, number = {8}, pages = {e0290446}, pmid = {37616245}, issn = {1932-6203}, abstract = {Brain computer interfaces (BCI) depend on reliable realtime detection of conscious EEG changes for example to control a video game. However, scalp recordings are contaminated with non-stationary noise, such as facial muscle activity and eye movements. This interferes with the detection process making it potentially unreliable or even impossible. We have developed a new methodology which provides a hard and measurable criterion if conscious EEG changes can be detected in the presence of non-stationary noise by requiring the signal-to-noise ratio of a scalp recording to be greater than the SNR-wall which in turn is based on the highest and lowest noise variances of the recording. As an instructional example, we have recorded signals from the central electrode Cz during eight different activities causing non-stationary noise such as playing a video game or reading out loud. The results show that facial muscle activity and eye-movements have a strong impact on the detectability of EEG and that minimising both eye-movement artefacts and muscle noise is essential to be able to detect conscious EEG changes.}, }
@article {pmid37616137, year = {2023}, author = {Hu, L and Zhu, J and Chen, S and Zhou, Y and Song, Z and Li, Y}, title = {A Wearable Asynchronous Brain-Computer Interface Based on EEG - EOG Signals with Fewer Channels.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2023.3308371}, pmid = {37616137}, issn = {1558-2531}, abstract = {OBJECTIVE: Brain-computer interfaces (BCIs) have tremendous application potential in communication, mechatronic control and rehabilitation. However, existing BCI systems are bulky, expensive and require laborious preparation before use. This study proposes a practical and user-friendly BCI system without compromising performance.<br><br>METHODS: A hybrid asynchronous BCI system was developed based on an elaborately designed wearable electroencephalography (EEG) amplifier that is compact, easy to use and offers a high signal-to-noise ratio (SNR). The wearable BCI system can detect P300 signals by processing EEG signals from three channels and operates asynchronously by integrating blink detection.<br><br>RESULT: The wearable EEG amplifier obtains high quality EEG signals and introduces preprocessing capabilities to BCI systems. The wearable BCI system achieves an average accuracy of 94.03&#xb1;4.65%, an average information transfer rate (ITR) of 31.42&#xb1;7.39 bits/min and an average false-positive rate (FPR) of 1.78%.<br><br>CONCLUSION: The experimental results demonstrate the feasibility and practicality of the developed wearable EEG amplifier and BCI system.<br><br>SIGNIFICANCE: Wearable asynchronous BCI systems with fewer channels are possible, indicating that BCI applications can be transferred from the laboratory to real-world scenarios.}, }
@article {pmid37614938, year = {2023}, author = {Zohny, H and Lyreskog, DM and Singh, I and Savulescu, J}, title = {The Mystery of Mental Integrity: Clarifying Its Relevance to Neurotechnologies.}, journal = {Neuroethics}, volume = {16}, number = {3}, pages = {20}, pmid = {37614938}, issn = {1874-5490}, abstract = {The concept of mental integrity is currently a significant topic in discussions concerning the regulation of neurotechnologies. Technologies such as deep brain stimulation and brain-computer interfaces are believed to pose a unique threat to mental integrity, and some authors have advocated for a legal right to protect it. Despite this, there remains uncertainty about what mental integrity entails and why it is important. Various interpretations of the concept have been proposed, but the literature on the subject is inconclusive. Here we consider a number of possible interpretations and argue that the most plausible one concerns neurotechnologies that bypass one's reasoning capacities, and do so specifically in ways that reliably lead to alienation from one's mental states. This narrows the scope of what constitutes a threat to mental integrity and offers a more precise role for the concept to play in the ethical evaluation of neurotechnologies.}, }
@article {pmid37612500, year = {2023}, author = {Willett, FR and Kunz, EM and Fan, C and Avansino, DT and Wilson, GH and Choi, EY and Kamdar, F and Glasser, MF and Hochberg, LR and Druckmann, S and Shenoy, KV and Henderson, JM}, title = {A high-performance speech neuroprosthesis.}, journal = {Nature}, volume = {}, number = {}, pages = {}, pmid = {37612500}, issn = {1476-4687}, abstract = {Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speech into text[1,2] or sound[3,4]. Early demonstrations, although promising, have not yet achieved accuracies sufficiently high for communication of unconstrained sentences from a large vocabulary[1-7]. Here we demonstrate a speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant-who can no longer speak intelligibly owing to amyotrophic lateral sclerosis-achieved a 9.1% word error rate on a 50-word vocabulary (2.7 times fewer errors than the previous state-of-the-art speech BCI[2]) and a 23.8% word error rate on a 125,000-word vocabulary (the first successful demonstration, to our knowledge, of large-vocabulary decoding). Our participant's attempted speech was decoded at 62 words per minute, which is 3.4 times as fast as the previous record[8] and begins to approach the speed of natural conversation (160 words per minute[9]). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for restoring rapid communication to people with paralysis who can no longer speak.}, }
@article {pmid37611877, year = {2023}, author = {Zhang, R and Liu, G and Wen, Y and Zhou, W}, title = {Self-attention-based Convolutional Neural Network AND Time-frequency Common Spatial Pattern for enhanced Motor Imagery Classification.}, journal = {Journal of neuroscience methods}, volume = {}, number = {}, pages = {109953}, doi = {10.1016/j.jneumeth.2023.109953}, pmid = {37611877}, issn = {1872-678X}, abstract = {BACKGROUND: Motor imagery (MI) based brain-computer interfaces (BCIs) have promising potentials in the field of neuro-rehabilitation. However, due to individual variations in active brain regions during MI tasks, the challenge of decoding MI EEG signals necessitates improved classification performance for practical application.<br><br>NEW METHOD: This study proposes a self-attention-based Convolutional Neural Network (CNN) in conjunction with a time-frequency common spatial pattern (TFCSP) for enhanced MI classification. Due to the limited availability of training data, a data augmentation strategy is employed to expand the scale of MI EEG datasets. The self-attention-based CNN is trained to automatically extract the temporal and spatial information from EEG signals, allowing the self-attention module to select active channels by calculating EEG channel weights. TFCSP is further implemented to extract multiscale time-frequency-space features from EEG data. Finally, the EEG features derived from TFCSP are concatenated with those from the self-attention-based CNN for MI classification.<br><br>RESULTS: The proposed method is evaluated on two publicly accessible datasets, BCI Competition IV IIa and BCI Competition III IIIa, yielding mean accuracies of 79.28% and 86.39%, respectively.<br><br>CONCLUSIONS: Compared with state-of-the-art methods, our approach achieves superior classification results in accuracy. Self-attention-based CNN combining with TFCSP can make full use of the time-frequency-space information of EEG, and enhance the classification performance.}, }
@article {pmid37611567, year = {2023}, author = {Wang, K and Qiu, S and Wei, W and Yi, W and He, H and Xu, M and Jung, TP and Ming, D}, title = {Investigating EEG-based cross-session and cross-task vigilance estimation in BCI systems.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acf345}, pmid = {37611567}, issn = {1741-2552}, abstract = {OBJECTIVE: The state of vigilance is crucial for effective performance in brain-computer interface (BCI) tasks, and therefore, it is essential to investigate vigilance levels in BCI tasks. Despite this, most studies have focused on vigilance levels in driving tasks rather than on BCI tasks, and the electroencephalogram (EEG) patterns of vigilance states in different BCI tasks remain unclear. This study aimed to identify similarities and differences in EEG patterns and performances of vigilance estimation in different BCI tasks and sessions.<br><br>APPROACH: To achieve this, we built a steady-state visual evoked potential (SSVEP)-based BCI system and a rapid serial visual presentation (RSVP)-based BCI system and recruited 18 participants to carry out four BCI experimental sessions over four days.<br><br>MAIN RESULTS: Our findings demonstrate that specific neural patterns for high and low vigilance levels are relatively stable across sessions. Differential entropy (DE) features significantly differ between different vigilance levels in all frequency bands and between BCI tasks in the delta and theta frequency bands, with the theta frequency band features playing a critical role in vigilance estimation. Additionally, prefrontal, temporal, and occipital regions are more relevant to the vigilance state in BCI tasks. Our results suggest that cross-session vigilance estimation is more accurate than cross-task estimation.<br><br>SIGNIFICANCE: Our study clarifies the underlying mechanisms of vigilance state in two BCI tasks and provides a foundation for further research in vigilance estimation in BCI applications.}, }
@article {pmid37611109, year = {2023}, author = {Sheng, F and Wang, R and Liang, Z and Wang, X and Platt, ML}, title = {The art of the deal: Deciphering the endowment effect from traders' eyes.}, journal = {Science advances}, volume = {9}, number = {34}, pages = {eadf2115}, pmid = {37611109}, issn = {2375-2548}, mesh = {Humans ; *Arousal ; *Emotions ; }, abstract = {People are often reluctant to trade, a reticence attributed to the endowment effect. The prevailing account attributes the endowment effect to valuation-related bias, manifesting as sellers valuing goods more than buyers, whereas an alternative account attributes it to response-related bias, manifesting as both buyers and sellers tending to stick to the status quo. Here, by tracking and modeling eye activity of buyers and sellers during trading, we accommodate both views within an evidence-accumulation framework. We find that valuation-related bias is indexed by asymmetric attentional allocation between buyers and sellers, whereas response-related bias is indexed by arousal-linked pupillary reactivity. A deal emerges when both buyers and sellers attend to their potential gains and dilate their pupils. Our study provides preliminary evidence for our computational framework of the dynamic processes mediating the endowment effect and identifies physiological biomarkers of deal-making.}, }
@article {pmid37610901, year = {2023}, author = {Mai, X and Sheng, X and Shu, X and Ding, Y and Zhu, X and Meng, J}, title = {A Calibration-free Hybrid Approach Combining SSVEP and EOG for Continuous Control.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3307814}, pmid = {37610901}, issn = {1558-0210}, abstract = {OBJECTIVE: While SSVEP-BCI has been widely developed to control external devices, most of them rely on the discrete control strategy. The continuous SSVEP-BCI enables users to continuously deliver commands and receive real-time feedback from the devices, but it suffers from the transition state problem, a period the erroneous recognition, when users shift their gazes between targets.<br><br>METHODS: To resolve this issue, we proposed a novel calibration-free Bayesian approach by hybridizing SSVEP and electrooculography (EOG). First, canonical correlation analysis (CCA) was applied to detect the evoked SSVEPs, and saccade during the gaze shift was detected by EOG data using an adaptive threshold method. Then, the new target after the gaze shift was recognized based on a Bayesian optimization approach, which combined the detection of SSVEP and saccade together and calculated the optimized probability distribution of the targets.<br><br>RESULTS: Eighteen healthy subjects participated in the offline and online experiments. The offline experiments showed that the proposed hybrid BCI had significantly higher overall continuous accuracy and shorter gaze-shifting time compared to FBCCA, CCA, MEC, and PSDA. In online experiments, the proposed hybrid BCI significantly outperformed CCA-based SSVEP-BCI in terms of continuous accuracy (77.61 &#xb1; 1.36% vs. 68.86 &#xb1; 1.08%) and gaze-shifting time (0.93 &#xb1; 0.06 s vs. 1.94 &#xb1;0.08 s). Additionally, participants also perceived a significant improvement over the CCA-based SSVEP-BCI when the newly proposed decoding approach was used.<br><br>CONCLUSION: These results validated the efficacy of the proposed hybrid Bayesian approach for the BCI continuous control without any calibration.<br><br>SIGNIFICANCE: This study provides an effective framework for combining SSVEP and EOG, and promotes the potential applications of plug-and-play BCIs in continuous control.}, }
@article {pmid37610705, year = {2023}, author = {Li, X and Bi, R and Ou, X and Han, S and Sheng, Y and Chen, G and Xie, Z and Liu, C and Yue, W and Wang, Y and Hu, W and Guo, SZ}, title = {3D-Printed Intrinsically Stretchable Organic Electrochemical Synaptic Transistor Array.}, journal = {ACS applied materials &amp; interfaces}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsami.3c07169}, pmid = {37610705}, issn = {1944-8252}, abstract = {Organic electrochemical transistors (OECTs) for skin-like bioelectronics require mechanical stretchability, softness, and cost-effective large-scale manufacturing. However, developing intrinsically stretchable OECTs using a simple and fast-response technique is challenging due to limitations in functional materials, substrate wettability, and integrated processing of multiple materials. In this regard, we propose a fabrication method devised by combining the preparation of a microstructured hydrophilic substrate, multi-material printing of functional inks with varying viscosities, and optimization of the device channel geometries. The resulting intrinsically stretchable OECT array with synaptic properties was successfully manufactured. These devices demonstrated high transconductance (22.5 mS), excellent mechanical softness (Young's modulus ∼ 2.2 MPa), and stretchability (∼30%). Notably, the device also exhibited artificial synapse functionality, mimicking the biological synapse with features such as paired-pulse depression, short-term plasticity, and long-term plasticity. This study showcases a promising strategy for fabricating intrinsically stretchable OECTs and provides valuable insights for the development of brain-computer interfaces.}, }
@article {pmid37610645, year = {2023}, author = {Xue, R and Li, X and Chen, J and Liang, S and Yu, H and Zhang, Y and Wei, W and Xu, Y and Deng, W and Guo, W and Li, T}, title = {Shared and Distinct Topographic Alterations of Alpha-Range Resting EEG Activity in Schizophrenia, Bipolar Disorder, and Depression.}, journal = {Neuroscience bulletin}, volume = {}, number = {}, pages = {}, pmid = {37610645}, issn = {1995-8218}, }
@article {pmid37610441, year = {2023}, author = {Zhang, X and Wang, Y and Jiao, B and Wang, Z and Shi, J and Zhang, Y and Bai, X and Li, Z and Li, S and Bai, R and Sui, B}, title = {Glymphatic system impairment in Alzheimer's disease: associations with perivascular space volume and cognitive function.}, journal = {European radiology}, volume = {}, number = {}, pages = {}, pmid = {37610441}, issn = {1432-1084}, support = {XDB39000000//Strategic Priority Research Program of the Chinese Academy of Sciences/ ; Z181100001518005//Beijing Municipal Science and Technology Commission/ ; }, abstract = {OBJECTIVES: To investigate glymphatic function in Alzheimer's disease (AD) using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) method and to explore the associations between DTI-ALPS index and perivascular space (PVS) volume, as well as between DTI-ALPS index and cognitive function.<br><br>METHODS: Thirty patients with PET-CT-confirmed AD (15 AD dementia; 15 mild cognitive impairment due to AD) and 26 age- and sex-matched cognitively normal controls (NCs) were included in this study. All participants underwent neurological MRI and cognitive assessments. Bilateral DTI-ALPS indices were calculated. PVS volume fractions were quantitatively measured at three locations: basal ganglia (BG), centrum semiovale, and lateral ventricle body level. DTI-ALPS index and PVS volume fractions were compared among three groups; correlations among the DTI-ALPS index, PVS volume fraction, and cognitive scales were analyzed.<br><br>RESULTS: Patients with AD dementia showed a significantly lower DTI-ALPS index in the whole brain (p&#x2009;=&#x2009;0.009) and in the left hemisphere (p&#x2009;=&#x2009;0.012) compared with NCs. The BG-PVS volume fraction in patients with AD was significantly larger than the fraction in NCs (p&#x2009;=&#x2009;0.045); it was also negatively correlated with the DTI-ALPS index (r&#x2009;=&#x2009;&#x2009;-&#x2009;0.433, p&#x2009;=&#x2009;0.021). Lower DTI-ALPS index was correlated with worse performance in the Boston Naming Test (&#x3b2;&#x2009;=&#x2009;0.515, p&#x2009;=&#x2009;0.008), Trail Making Test A (&#x3b2;&#x2009;=&#x2009;&#x2009;-&#x2009;0.391, p&#x2009;=&#x2009;0.048), and Digit Span Test (&#x3b2;&#x2009;=&#x2009;0.408, p&#x2009;=&#x2009;0.038).<br><br>CONCLUSIONS: The lower DTI-ALPS index was found in patients with AD dementia, which may suggest impaired glymphatic system function. DTI-ALPS index was correlated with BG-PVS enlargement and worse cognitive performance in certain cognitive domains.<br><br>CLINICAL RELEVANCE STATEMENT: Diffusion tensor image analysis along the perivascular space index may be applied as a useful indicator to evaluate the glymphatic system function. The impaired glymphatic system in patients with Alzheimer's disease (AD) dementia may provide a new perspective for understanding the pathophysiology of AD.<br><br>KEY POINTS: &#x2022; Patients with Alzheimer's disease dementia displayed a lower diffusion tensor image analysis along the perivascular space (DTI-ALPS) index, possibly indicating glymphatic impairment. &#x2022; A lower DTI-ALPS index was associated with the enlargement of perivascular space and cognitive impairment. &#x2022; DTI-ALPS index could be a promising biomarker of the glymphatic system in Alzheimer's disease dementia.}, }
@article {pmid37610305, year = {2023}, author = {Ma, X and Rizzoglio, F and Bodkin, KL and Perreault, E and Miller, LE and Kennedy, A}, title = {Using adversarial networks to extend brain computer interface decoding accuracy over time.}, journal = {eLife}, volume = {12}, number = {}, pages = {}, pmid = {37610305}, issn = {2050-084X}, support = {R01 NS053603/NS/NINDS NIH HHS/United States ; R01 NS074044/NS/NINDS NIH HHS/United States ; }, mesh = {Animals ; *Brain-Computer Interfaces ; Acclimatization ; Brain ; *Coleoptera ; Heart ; }, abstract = {Existing intracortical brain computer interfaces (iBCIs) transform neural activity into control signals capable of restoring movement to persons with paralysis. However, the accuracy of the 'decoder' at the heart of the iBCI typically degrades over time due to turnover of recorded neurons. To compensate, decoders can be recalibrated, but this requires the user to spend extra time and effort to provide the necessary data, then learn the new dynamics. As the recorded neurons change, one can think of the underlying movement intent signal being expressed in changing coordinates. If a mapping can be computed between the different coordinate systems, it may be possible to stabilize the original decoder's mapping from brain to behavior without recalibration. We previously proposed a method based on Generalized Adversarial Networks (GANs), called 'Adversarial Domain Adaptation Network' (ADAN), which aligns the distributions of latent signals within underlying low-dimensional neural manifolds. However, we tested ADAN on only a very limited dataset. Here we propose a method based on Cycle-Consistent Adversarial Networks (Cycle-GAN), which aligns the distributions of the full-dimensional neural recordings. We tested both Cycle-GAN and ADAN on data from multiple monkeys and behaviors and compared them to a third, quite different method based on Procrustes alignment of axes provided by Factor Analysis. All three methods are unsupervised and require little data, making them practical in real life. Overall, Cycle-GAN had the best performance and was easier to train and more robust than ADAN, making it ideal for stabilizing iBCI systems over time.}, }
@article {pmid37609450, year = {2023}, author = {Gruenwald, J and Sieghartsleitner, S and Kapeller, C and Scharinger, J and Kamada, K and Brunner, P and Guger, C}, title = {Characterization of High-Gamma Activity in Electrocorticographic signals.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1206120}, pmid = {37609450}, issn = {1662-4548}, abstract = {INTRODUCTION: Electrocorticographic (ECoG) high-gamma activity (HGA) is a widely recognized and robust neural correlate of cognition and behavior. However, fundamental signal properties of HGA, such as the high-gamma frequency band or temporal dynamics of HGA, have never been systematically characterized. As a result, HGA estimators are often poorly adjusted, such that they miss valuable physiological information.<br><br>METHODS: To address these issues, we conducted a thorough qualitative and quantitative characterization of HGA in ECoG signals. Our study is based on ECoG signals recorded from 18 epilepsy patients while performing motor control, listening, and visual perception tasks. In this study, we first categorize HGA into HGA types based on the cognitive/behavioral task. For each HGA type, we then systematically quantify three fundamental signal properties of HGA: the high-gamma frequency band, the HGA bandwidth, and the temporal dynamics of HGA.<br><br>RESULTS: The high-gamma frequency band strongly varies across subjects and across cognitive/behavioral tasks. In addition, HGA time courses have lowpass character, with transients limited to 10 Hz. The task-related rise time and duration of these HGA time courses depend on the individual subject and cognitive/behavioral task. Task-related HGA amplitudes are comparable across the investigated tasks.<br><br>DISCUSSION: This study is of high practical relevance because it provides a systematic basis for optimizing experiment design, ECoG acquisition and processing, and HGA estimation. Our results reveal previously unknown characteristics of HGA, the physiological principles of which need to be investigated in further studies.}, }
@article {pmid37609258, year = {2023}, author = {Ruszala, B and Mazurek, KA and Schieber, MH}, title = {Somatosensory cortex microstimulation modulates primary motor and premotor cortex neurons with extensive spatial convergence and divergence.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {37609258}, abstract = {UNLABELLED: Although intracortical microstimulation (ICMS) can affect distant neurons transynaptically, the extent to which ICMS pulses delivered in one cortical area actually modulate neurons in other cortical areas during voluntary behavior remains largely unknown. Here, we assessed how the individual pulses of multi-channel ICMS trains delivered in the primary somatosensory cortex (S1) modulate neuron firing in the primary motor (M1) and premotor (PM) cortex. S1-ICMS pulses modulated the majority of units recorded in both M1 and PM, producing more inhibition than excitation. Effects converged on individual neurons in both M1 and PM from extensive S1 territories. Conversely, effects of ICMS in a small region of S1 diverged to wide territories in both M1 and PM. Our findings may have ramifications for development of bidirectional brain-computer interfaces, where ICMS used to deliver artificial feedback in S1 could modulate the activity of neurons in M1 and PM, thereby hindering decoding of motor intent.<br><br>HIGHLIGHTS: Somatosensory cortex microstimulation modulated motor and premotor cortex neurons.Inhibitory effects were more common than excitatory effects.Effects from stimulation on each &#x223c;2x2 mm multielectrode array diverged widely.Effects in individual neurons converged from multiple somatosensory arrays.<br><br>ETOC BLURB: Ruszala and colleagues show that multi-channel intracortical microstimulation in a small patch of somatosensory cortex modulates neurons distributed widely in both the primary motor and premotor cortex, with more effects being inhibitory than excitatory. Such modulation may complicate decoding motor intent when artificial sensation is delivered in bidirectional brain-computer interfaces.}, }
@article {pmid37609167, year = {2023}, author = {Ali, YH and Bodkin, K and Rigotti-Thompson, M and Patel, K and Card, NS and Bhaduri, B and Nason-Tomaszewski, SR and Mifsud, DM and Hou, X and Nicolas, C and Allcroft, S and Hochberg, LR and Yong, NA and Stavisky, SD and Miller, LE and Brandman, DM and Pandarinath, C}, title = {BRAND: A platform for closed-loop experiments with deep network models.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.08.08.552473}, pmid = {37609167}, abstract = {Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g., Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g., C and C++). To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termed nodes , which communicate with each other in a graph via streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes. In our tests, BRAND achieved <600 microsecond latency between processes when sending large quantities of data (1024 channels of 30 kHz neural data in 1-millisecond chunks). BRAND runs a brain-computer interface with a recurrent neural network (RNN) decoder with less than 8 milliseconds of latency from neural data input to decoder prediction. In a real-world demonstration of the system, participant T11 in the BrainGate2 clinical trial performed a standard cursor control task, in which 30 kHz signal processing, RNN decoding, task control, and graphics were all executed in BRAND. This system also supports real-time inference with complex latent variable models like Latent Factor Analysis via Dynamical Systems. By providing a framework that is fast, modular, and language-agnostic, BRAND lowers the barriers to integrating the latest tools in neuroscience and machine learning into closed-loop experiments.}, }
@article {pmid37607973, year = {2023}, author = {Yeom, HG and Kim, JS and Chung, CK}, title = {A magnetoencephalography dataset during three-dimensional reaching movements for brain-computer interfaces.}, journal = {Scientific data}, volume = {10}, number = {1}, pages = {552}, pmid = {37607973}, issn = {2052-4463}, support = {2019R1A2C1009674//National Research Foundation of Korea (NRF)/ ; 2019R1A2C1009674//National Research Foundation of Korea (NRF)/ ; }, mesh = {*Magnetoencephalography ; *Brain-Computer Interfaces ; Brain ; Algorithms ; Knowledge ; }, abstract = {Studying the motor-control mechanisms of the brain is critical in academia and also has practical implications because techniques such as brain-computer interfaces (BCIs) can be developed based on brain mechanisms. Magnetoencephalography (MEG) signals have the highest spatial resolution (~3 mm) and temporal resolution (~1 ms) among the non-invasive methods. Therefore, the MEG is an excellent modality for investigating brain mechanisms. However, publicly available MEG data remains scarce due to expensive MEG equipment, requiring a magnetically shielded room, and high maintenance costs for the helium gas supply. In this study, we share the 306-channel MEG and 3-axis accelerometer signals acquired during three-dimensional reaching movements. Additionally, we provide analysis results and MATLAB codes for time-frequency analysis, F-value time-frequency analysis, and topography analysis. These shared MEG datasets offer valuable resources for investigating brain activities or evaluating the accuracy of prediction algorithms. To the best of our knowledge, this data is the only publicly available MEG data measured during reaching movements.}, }
@article {pmid37606742, year = {2023}, author = {Kancheva, I and van der Salm, SMA and Ramsey, NF and Vansteensel, MJ}, title = {Association between lesion location and sensorimotor rhythms in stroke - a systematic review with narrative synthesis.}, journal = {Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology}, volume = {}, number = {}, pages = {}, pmid = {37606742}, issn = {1590-3478}, support = {U01DC016686/DC/NIDCD NIH HHS/United States ; }, abstract = {BACKGROUND: Stroke causes alterations in the sensorimotor rhythms (SMRs) of the brain. However, little is known about the influence of lesion location on the SMRs. Understanding this relationship is relevant for the use of SMRs in assistive and rehabilitative therapies, such as Brain-Computer Interfaces (BCIs)..<br><br>METHODS: We reviewed current evidence on the association between stroke lesion location and SMRs through systematically searching PubMed and Embase and generated a narrative synthesis of findings.<br><br>RESULTS: We included 12 articles reporting on 161 patients. In resting-state studies, cortical and pontine damage were related to an overall decrease in alpha (&#x223c;8-12&#xa0;Hz) and increase in delta (&#x223c;1-4&#xa0;Hz) power. In movement paradigm studies, attenuated alpha and beta (&#x223c;15-25&#xa0;Hz) event-related desynchronization (ERD) was shown in stroke patients during (attempted) paretic hand movement, compared to controls. Stronger reductions in alpha and beta ERD in the ipsilesional, compared to contralesional hemisphere, were observed for cortical lesions. Subcortical stroke was found to affect bilateral ERD and ERS, but results were highly variable.<br><br>CONCLUSIONS: Findings suggest a link between stroke lesion location and SMR alterations, but heterogeneity across studies and limited lesion location descriptions precluded a meta-analysis.<br><br>SIGNIFICANCE: Future research would benefit from more uniformly defined outcome measures, homogeneous methodologies, and improved lesion location reporting.}, }
@article {pmid37604750, year = {2023}, author = {Biffl, WL and Castelo, M and Dandan, IS and Lu, N and Rivera, P and Bayat, D}, title = {Exploring the role of endovascular interventions in blunt carotid and vertebral artery trauma.}, journal = {American journal of surgery}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.amjsurg.2023.07.030}, pmid = {37604750}, issn = {1879-1883}, abstract = {BACKGROUND: The role of endovascular interventions (EI) for blunt carotid and vertebral artery injuries (BCI and BVI) is poorly defined. The purpose of this study was to assess the efficacy of EI compared with antithrombotic therapy (AT) to inform future prospective study.<br><br>METHODS: Retrospective review (2017-2022) of records at a Level I trauma center to determine injury, treatment, and outcome information. Primary outcome was stroke.<br><br>RESULTS: 96 patients suffered 106 injuries (74 BVI, 32 BCI). 12 patients underwent 13 EI- 4 therapeutic, 9 prophylactic. Stroke occurred in 12 patients- 6 who had EI. In grade IV BVI, stroke rates are low with both EI and AT. Thrombectomy after stroke improved neurologic function in 4 (100%) of 4 patients.<br><br>CONCLUSIONS: Most strokes occur prior to preventive therapy. Neither AT nor EI is 100% effective in preventing stroke. Thrombectomy may improve neurologic outcomes after stroke. Prospective multicenter study is imperative.}, }
@article {pmid37604228, year = {2023}, author = {Selvam, A and Aggarwal, T and Mukherjee, M and Verma, YK}, title = {Humans and robots: Friends of the future? A bird's eye view of biomanufacturing industry 5.0.}, journal = {Biotechnology advances}, volume = {}, number = {}, pages = {108237}, doi = {10.1016/j.biotechadv.2023.108237}, pmid = {37604228}, issn = {1873-1899}, abstract = {The evolution of industries has introduced versatile technologies, motivating limitless possibilities of tackling pivotal global predicaments in the arenas of medicine, environment, defence, and national security. In this direction, ardently emerges the new era of Industry 5.0 in the eyes of biomanufacturing, which integrates the most advanced systems 21st century has to offer by means of integrating artificial systems to mimic and nativize the natural milieu to substitute the deficits of nature, thence leading to a new meta world. Albeit, it questions the natural order of the living world, which necessitates certain paramount stipulations to be addressed for a successful expansion of biomanufacturing Industry 5.0. Can humans live in synergism with artificial beings? How can humans establish dominance of hierarchy with artificial counterparts? This perspective provides a bird's eye view on the plausible direction of a new meta world inquisitively. For this purpose, we propose the influence of internet of things (IoT) via new generation interfacial systems, such as, human-machine interface (HMI) and brain-computer interface (BCI) in the domain of tissue engineering and regenerative medicine to target modern warfare and smart healthcare.}, }
@article {pmid37604119, year = {2023}, author = {Liu, K and Yao, Z and Zheng, L and Wei, Q and Pei, W and Gao, X and Wang, Y}, title = {A high-frequency SSVEP-BCI system based on a 360 Hz refresh rate.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acf242}, pmid = {37604119}, issn = {1741-2552}, abstract = {Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) often struggle to balance user experience and system performance. To address this challenge, this study employed stimuli in the 55 - 62.8 Hz frequency range to implement a 40-target BCI speller that offered both high-performance and user-friendliness. Approach. This study proposed a method that presents stable multi-target stimuli on a monitor with a 360 Hz refresh rate. Real-time generation of stimulus matrix and stimulus rendering was used to ensure stable presentation while reducing the computational load. The 40 targets were encoded using the joint frequency and phase modulation method. Offline and online BCI experiments were conducted on 16 subjects using the task discriminant component analysis algorithm for feature extraction and classification. Main Results. The online BCI system achieved an average accuracy of 88.87±3.05% and an average information transfer rate of 51.83±2.77 bits/min under the low flickering perception condition. Significance. These findings suggest the feasibility and significant practical value of the proposed high-frequency SSVEP BCI system in advancing the visual BCI technology. .}, }
@article {pmid37603133, year = {2023}, author = {Al-Qaysi, ZT and Albahri, AS and Ahmed, MA and Mohammed, SM}, title = {Development of hybrid feature learner model integrating FDOSM for golden subject identification in motor imagery.}, journal = {Physical and engineering sciences in medicine}, volume = {}, number = {}, pages = {}, pmid = {37603133}, issn = {2662-4737}, abstract = {Brain-computer interfaces (BCIs) based on motor imagery (MI) face challenges due to the complex nature of brain activity, nonstationary and high-dimensional properties, and individual variations in motor behaviour. The identification of a consistent "golden subject" in MI-based BCIs remains an open challenge, complicated by multiple evaluation metrics and conflicting trade-offs, presenting complex Multi-Criteria Decision Making (MCDM) problems. This study proposes a hybrid brain signal decoding model called Hybrid Adaboost Feature Learner (HAFL), which combines feature extraction and classification using VGG-19, STFT, and Adaboost classifier. The model is validated using a pre-recorded MI-EEG dataset from the BCI competition at Graz University. The fuzzy decision-making framework is integrated with HAFL to allocate a golden subject for MI-BCI applications through the Golden Subject Decision Matrix (GSDM) and the Fuzzy Decision by Opinion Score Method (FDOSM). The effectiveness of the HAFL model in addressing inter-subject variability in EEG-based MI-BCI is evaluated using an MI-EEG dataset involving nine subjects. Comparing subject performance fairly is challenging due to complexity variations, but the FDOSM method provides valuable insights. Through FDOSM-based External Group Aggregation (EGA), subject S5 achieves the highest score of 2.900, identified as the most promising golden subject for subject-to-subject transfer learning. The proposed methodology is compared against other benchmark studies from various key perspectives and exhibits significant novelty in several aspects. The findings contribute to the development of more robust and effective BCI systems, paving the way for advancements in subject-to-subject transfer learning for BCI-MI applications.}, }
@article {pmid37602829, year = {2023}, author = {Li, T}, title = {Methods And Protocols For Live Imaging In Development.}, journal = {Journal of visualized experiments : JoVE}, volume = {}, number = {191}, pages = {}, doi = {10.3791/64642}, pmid = {37602829}, issn = {1940-087X}, mesh = {Animals ; *Zebrafish ; *Brain ; Caenorhabditis elegans ; Drosophila ; Time-Lapse Imaging ; }, abstract = {Januschke, J., Loyer, N. Applications of immobilization of Drosophila tissues with fibrin clots for live imaging. Journal of Visualized Experiments. (166), 10.3791/61954 (2020). Li, T., Luo, L. An explant system for time-lapse imaging studies of olfactory circuit assembly in Drosophila. Journal of Visualized Experiments. (176), 10.3791/62983 (2021). Schramm, P., Hetsch, F., Meier, J. C., Koster, R. W. In vivo imaging of fully active brain tissue in awake zebrafish larvae and juveniles by skull and skin removal. Journal of Visualized Experiments. (168), 10.3791/62166 (2021). Ratke, J., Kramer, F., Strobl, F. Simultaneous live imaging of multiple insect embryos in sample chamber-based light sheet fluorescence microscopes. Journal of Visualized Experiments. (163), 10.3791/61713 (2020). Terzi, A., Alam, S. M. S., Suter, D. M. ROS live cell imaging during neuronal development. Journal of Visualized Experiments. (168), 10.3791/62165 (2021). Mutlu, A. S., Chen, T., Deng, D., Wang, M. C. Label-Free imaging of lipid storage dynamics in Caenorhabditis elegans using stimulated Raman scattering microscopy. Journal of Visualized Experiments. (171), 10.3791/61870 (2021). Boutillon, A., Escot, S., David, N. B. Deep and spatially controlled volume ablations using a two-photon microscope in the zebrafish gastrula. Journal of Visualized Experiments. (173), 10.3791/62815 (2021).}, }
@article {pmid37602262, year = {2023}, author = {Cui, Y and Cong, F and Huang, F and Zeng, M and Yan, R}, title = {Cortical activation of neuromuscular electrical stimulation synchronized mirror neuron rehabilitation strategies: an fNIRS study.}, journal = {Frontiers in neurology}, volume = {14}, number = {}, pages = {1232436}, pmid = {37602262}, issn = {1664-2295}, abstract = {BACKGROUND: The mirror neuron system (MNS) plays a key role in the neural mechanism underlying motor learning and neural plasticity. Action observation (AO), action execution (AE), and a combination of both, known as action imitation (AI), are the most commonly used rehabilitation strategies based on MNS. It is possible to enhance the cortical activation area and amplitude by combining traditional neuromuscular electrical stimulation (NMES) with other top-down and active rehabilitation strategies based on the MNS theory.<br><br>OBJECTIVE: This study aimed to explore the cortical activation patterns induced by NMES synchronized with rehabilitation strategies based on MNS, namely NMES+AO, NMES+AE, and NMES+AI. In addition, the study aimed to assess the feasibility of these three novel rehabilitative treatments in order to provide insights and evidence for the design, implementation, and application of brain-computer interfaces.<br><br>METHODS: A total of 70 healthy adults were recruited from July 2022 to February 2023, and 66 of them were finally included in the analysis. The cortical activation patterns during NMES+AO, NMES+AE, and NMES+AI were detected using the functional Near-Infrared Spectroscopy (fNIRS) technique. The action to be observed, executed, or imitated was right wrist and hand extension, and two square-shaped NMES electrodes were placed on the right extensor digitorum communis. A block design was adopted to evaluate the activation intensity of the left MNS brain regions.<br><br>RESULTS: General linear model results showed that compared with the control condition, the number of channels significantly activated (PFDR&#x2009;<&#x2009;0.05) in the NMES+AO, NMES+AE, and NMES+AI conditions were 3, 9, and 9, respectively. Region of interest (ROI) analysis showed that 2 ROIs were significantly activated (PFDR&#x2009;<&#x2009;0.05) in the NMES+AO condition, including BA6 and BA44; 5 ROIs were significantly activated in the NMES+AE condition, including BA6, BA40, BA44, BA45, and BA46; and 6 ROIs were significantly activated in the NMES+AI condition, including BA6, BA7, BA40, BA44, BA45, and BA46.<br><br>CONCLUSION: The MNS was activated during neuromuscular electrical stimulation combined with an AO, AE, and AI intervention. The synchronous application of NMES and mirror neuron rehabilitation strategies is feasible in clinical rehabilitation. The fNIRS signal patterns observed in this study could be used to develop brain-computer interface and neurofeedback therapy rehabilitation devices.}, }
@article {pmid37600556, year = {2023}, author = {Moreno-Calderón, S and Martínez-Cagigal, V and Santamaría-Vázquez, E and Pérez-Velasco, S and Marcos-Martínez, D and Hornero, R}, title = {Combining brain-computer interfaces and multiplayer video games: an application based on c-VEPs.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1227727}, pmid = {37600556}, issn = {1662-5161}, abstract = {INTRODUCTION AND OBJECTIVE: Video games are crucial to the entertainment industry, nonetheless they can be challenging to access for those with severe motor disabilities. Brain-computer interfaces (BCI) systems have the potential to help these individuals by allowing them to control video games using their brain signals. Furthermore, multiplayer BCI-based video games may provide valuable insights into how competitiveness or motivation affects the control of these interfaces. Despite the recent advancement in the development of code-modulated visual evoked potentials (c-VEPs) as control signals for high-performance BCIs, to the best of our knowledge, no studies have been conducted to develop a BCI-driven video game utilizing c-VEPs. However, c-VEPs could enhance user experience as an alternative method. Thus, the main goal of this work was to design, develop, and evaluate a version of the well-known 'Connect 4' video game using a c-VEP-based BCI, allowing 2 users to compete by aligning 4 same-colored coins vertically, horizontally or diagonally.<br><br>METHODS: The proposed application consists of a multiplayer video game controlled by a real-time BCI system processing 2 electroencephalograms (EEGs) sequentially. To detect user intention, columns in which the coin can be placed was encoded with shifted versions of a pseudorandom binary code, following a traditional circular shifting c-VEP paradigm. To analyze the usability of our application, the experimental protocol comprised an evaluation session by 22 healthy users. Firstly, each user had to perform individual tasks. Afterward, users were matched and the application was used in competitive mode. This was done to assess the accuracy and speed of selection. On the other hand, qualitative data on satisfaction and usability were collected through questionnaires.<br><br>RESULTS: The average accuracy achieved was 93.74% &#xb1; 1.71%, using 5.25 seconds per selection. The questionnaires showed that users felt a minimal workload. Likewise, high satisfaction values were obtained, highlighting that the application was intuitive and responds quickly and smoothly.<br><br>CONCLUSIONS: This c-VEP based multiplayer video game has reached suitable performance on 22 users, supported by high motivation and minimal workload. Consequently, compared to other versions of "Connect 4" that utilized different control signals, this version has exhibited superior performance.}, }
@article {pmid37600142, year = {2023}, author = {Xu, B and Liu, D and Xue, M and Miao, M and Hu, C and Song, A}, title = {Continuous shared control of a mobile robot with brain-computer interface and autonomous navigation for daily assistance.}, journal = {Computational and structural biotechnology journal}, volume = {22}, number = {}, pages = {3-16}, pmid = {37600142}, issn = {2001-0370}, abstract = {Although the electroencephalography (EEG) based brain-computer interface (BCI) has been successfully developed for rehabilitation and assistance, it is still challenging to achieve continuous control of a brain-actuated mobile robot system. In this study, we propose a continuous shared control strategy combining continuous BCI and autonomous navigation for a mobile robot system. The weight of shared control is designed to dynamically adjust the fusion of continuous BCI control and autonomous navigation. During this process, the system uses the visual-based simultaneous localization and mapping (SLAM) method to construct environmental maps. After obtaining the global optimal path, the system utilizes the brain-based shared control dynamic window approach (BSC-DWA) to evaluate safe and reachable trajectories while considering shared control velocity. Eight subjects participated in two-stage training, and six of these eight subjects participated in online shared control experiments. The training results demonstrated that naïve subjects could achieve continuous control performance with an average percent valid correct rate of approximately 97 % and an average total correct rate of over 80 %. The results of online shared control experiments showed that all of the subjects could complete navigation tasks in an unknown corridor with continuous shared control. Therefore, our experiments verified the feasibility and effectiveness of the proposed system combining continuous BCI, shared control, autonomous navigation, and visual SLAM. The proposed continuous shared control framework shows great promise in BCI-driven tasks, especially navigation tasks for brain-driven assistive mobile robots and wheelchairs in daily applications.}, }
@article {pmid37600011, year = {2023}, author = {Yan, W and He, B and Zhao, J}, title = {SSVEP unsupervised adaptive feature recognition method based on self-similarity of same-frequency signals.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1161511}, pmid = {37600011}, issn = {1662-4548}, abstract = {INTRODUCTION: As an important human-computer interaction technology, steady-state visual evoked potential (SSVEP) plays a key role in the application of brain computer interface (BCI) systems by accurately decoding SSVEP signals. Currently, the majority SSVEP feature recognition methods use a static classifier. However, electroencephalogram (EEG) signals are non-stationary and time-varying. Hence, an adaptive classification method would be an alternative option to a static classifier for tracking the changes in EEG feature distribution, as its parameters can be re-estimated and updated with the input of new EEG data.<br><br>METHODS: In this study, an unsupervised adaptive classification algorithm is designed based on the self-similarity of same-frequency signals. The proposed classification algorithm saves the EEG data that has undergone feature recognition as a template signal in accordance with its estimated label, and the new testing signal is superimposed with the template signals at each stimulus frequency as the new test signals to be analyzed. With the continuous input of EEG data, the template signals are continuously updated.<br><br>RESULTS: By comparing the classification accuracy of the original testing signal and the testing signal superimposed with the template signals, this study demonstrates the effectiveness of using the self-similarity of same-frequency signals in the adaptive classification algorithm. The experimental results also show that the longer the SSVEP-BCI system is used, the better the responses of users on SSVEP are, and the more significantly the adaptive classification algorithm performs in terms of feature recognition. The testing results of two public datasets show that the adaptive classification algorithm outperforms the static classification method in terms of feature recognition.<br><br>DISCUSSION: The proposed adaptive classification algorithm can update the parameters with the input of new EEG data, which is of favorable impact for the accurate analysis of EEG data with time-varying characteristics.}, }
@article {pmid37599998, year = {2023}, author = {Tsoneva, T and Garcia-Molina, G and Desain, P}, title = {Electrophysiological model of human temporal contrast sensitivity based on SSVEP.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1180829}, pmid = {37599998}, issn = {1662-4548}, abstract = {The present study aims to connect the psychophysical research on the human visual perception of flicker with the neurophysiological research on steady-state visual evoked potentials (SSVEPs) in the context of their application needs and current technological developments. In four experiments, we investigated whether a temporal contrast sensitivity model could be established based on the electrophysiological responses to repetitive visual stimulation and, if so, how this model compares to the psychophysical models of flicker visibility. We used data from 62 observers viewing periodic flicker at a range of frequencies and modulation depths sampled around the perceptual visibility thresholds. The resulting temporal contrast sensitivity curve (TCSC) was similar in shape to its psychophysical counterpart, confirming that the human visual system is most sensitive to repetitive visual stimulation at frequencies between 10 and 20 Hz. The electrophysiological TCSC, however, was below the psychophysical TCSC measured in our experiments for lower frequencies (1-50 Hz), crossed it when the frequency was 50 Hz, and stayed above while decreasing at a slower rate for frequencies in the gamma range (40-60 Hz). This finding provides evidence that SSVEPs could be measured even without the conscious perception of flicker, particularly at frequencies above 50 Hz. The cortical and perceptual mechanisms that apply at higher temporal frequencies, however, do not seem to directly translate to lower frequencies. The presence of harmonics, which show better response for many frequencies, suggests non-linear processing in the visual system. These findings are important for the potential applications of SSVEPs in studying, assisting, or augmenting human cognitive and sensorimotor functions.}, }
@article {pmid37593487, year = {2023}, author = {Healthcare Engineering, JO}, title = {Retracted: Effects of a Brain-Computer Interface-Operated Lower Limb Rehabilitation Robot on Motor Function Recovery in Patients with Stroke.}, journal = {Journal of healthcare engineering}, volume = {2023}, number = {}, pages = {9851304}, pmid = {37593487}, issn = {2040-2309}, abstract = {[This retracts the article DOI: 10.1155/2021/4710044.].}, }
@article {pmid37552978, year = {2023}, author = {Liang, T and Yu, X and Liu, X and Wang, H and Liu, X and Dong, B}, title = {EEG-CDILNet: a lightweight and accurate CNN network using circular dilated convolution for motor imagery classification.}, journal = {Journal of neural engineering}, volume = {20}, number = {4}, pages = {}, doi = {10.1088/1741-2552/acee1f}, pmid = {37552978}, issn = {1741-2552}, mesh = {*Algorithms ; *Brain-Computer Interfaces ; Movement ; Neural Networks, Computer ; Electroencephalography/methods ; Imagination ; }, abstract = {Objective.The combination of the motor imagery (MI) electroencephalography (EEG) signals and deep learning-based methods is an effective way to improve MI classification accuracy. However, deep learning-based methods often need too many trainable parameters. As a result, the trade-off between the network decoding performance and computational cost has always been an important challenge in the MI classification research.Approach.In the present study, we proposed a new end-to-end convolutional neural network (CNN) model called the EEG-circular dilated convolution (CDIL) network, which takes into account both the lightweight model and the classification accuracy. Specifically, the depth-separable convolution was used to reduce the number of network parameters and extract the temporal and spatial features from the EEG signals. CDIL was used to extract the time-varying deep features that were generated in the previous stage. Finally, we combined the features extracted from the two stages and used the global average pooling to further reduce the number of parameters, in order to achieve an accurate MI classification. The performance of the proposed model was verified using three publicly available datasets.Main results.The proposed model achieved an average classification accuracy of 79.63% and 94.53% for the BCIIV2a and HGD four-classification task, respectively, and 87.82% for the BCIIV2b two-classification task. In particular, by comparing the number of parameters, computation and classification accuracy with other lightweight models, it was confirmed that the proposed model achieved a better balance between the decoding performance and computational cost. Furthermore, the structural feasibility of the proposed model was confirmed by ablation experiments and feature visualization.Significance.The results indicated that the proposed CNN model presented high classification accuracy with less computing resources, and can be applied in the MI classification research.}, }
@article {pmid37588651, year = {2023}, author = {Hatton, SL and Rathore, S and Vilinsky, I and Stowasser, A}, title = {Quantitative and Qualitative Representation of Introductory and Advanced EEG Concepts: An Exploration of Different EEG Setups.}, journal = {Journal of undergraduate neuroscience education : JUNE : a publication of FUN, Faculty for Undergraduate Neuroscience}, volume = {21}, number = {2}, pages = {A142-A150}, pmid = {37588651}, issn = {1544-2896}, abstract = {Electroencephalograms (EEGs) are the gold standard test used in the medical field to diagnose epilepsy and aid in the diagnosis of many other neurological and mental disorders. Growing in popularity in terms of nonmedical applications, the EEG is also used in research, neurofeedback, and brain-computer interface, making it increasingly relevant to student learning. Recent innovations have made EEG setups more accessible and affordable, thus allowing their integration into neuroscience educational settings. Introducing students to EEGs, however, can be daunting due to intricate setup protocols, individual variation, and potentially expensive equipment. This paper aims to provide guidance for introducing students and educators to fundamental beginning and advanced level EEG concepts. Specifically, this paper tested the potential of three different setups, with varying channel number and wired or wireless connectivity, for introducing students to qualitative and quantitative exploration of alpha enhancement when eyes are closed, and observation of the alpha/beta anterior to posterior gradient. The setups were compared to determine their relative advantages and their robustness in detecting these well-established parameters. The basic 1- or 2-channel setups are sufficient for observing alpha and beta waves, while more advanced systems containing 8 or 16 channels are required for consistent observation of an anterior-posterior gradient. In terms of localization, the 16-channel setup, in principle, was more adept. The 8-channel setup, however, was more effective than the 16-channel setup with regards to displaying the anterior to posterior gradient. Thus, an 8-channel setup is sufficient in an education setting to display these known trends. Modification of the 16-channel setup may provide a better observation of the anterior to posterior gradient.}, }
@article {pmid37588619, year = {2023}, author = {Zheng, H and Niu, L and Qiu, W and Liang, D and Long, X and Li, G and Liu, Z and Meng, L}, title = {The Emergence of Functional Ultrasound for Noninvasive Brain-Computer Interface.}, journal = {Research (Washington, D.C.)}, volume = {6}, number = {}, pages = {0200}, pmid = {37588619}, issn = {2639-5274}, abstract = {A noninvasive brain-computer interface is a central task in the comprehensive analysis and understanding of the brain and is an important challenge in international brain-science research. Current implanted brain-computer interfaces are cranial and invasive, which considerably limits their applications. The development of new noninvasive reading and writing technologies will advance substantial innovations and breakthroughs in the field of brain-computer interfaces. Here, we review the theory and development of the ultrasound brain functional imaging and its applications. Furthermore, we introduce latest advancements in ultrasound brain modulation and its applications in rodents, primates, and human; its mechanism and closed-loop ultrasound neuromodulation based on electroencephalograph are also presented. Finally, high-frequency acoustic noninvasive brain-computer interface is prospected based on ultrasound super-resolution imaging and acoustic tweezers.}, }
@article {pmid37588309, year = {2023}, author = {Kumar, J and Patel, T and Sugandh, F and Dev, J and Kumar, U and Adeeb, M and Kachhadia, MP and Puri, P and Prachi, F and Zaman, MU and Kumar, S and Varrassi, G and Syed, ARS}, title = {Innovative Approaches and Therapies to Enhance Neuroplasticity and Promote Recovery in Patients With Neurological Disorders: A Narrative Review.}, journal = {Cureus}, volume = {15}, number = {7}, pages = {e41914}, pmid = {37588309}, issn = {2168-8184}, abstract = {Brain rehabilitation and recovery for people with neurological disorders, such as stroke, traumatic brain injury (TBI), and neurodegenerative diseases, depend mainly on neuroplasticity, the brain's capacity to restructure and adapt. This literature review aims to look into cutting-edge methods and treatments that support neuroplasticity and recovery in these groups. A thorough search of electronic databases revealed a wide range of research and papers investigating several neuroplasticity-targeting methods, such as cognitive training, physical activity, non-invasive brain stimulation, and pharmaceutical interventions. The results indicate that these therapies can control neuroplasticity and improve motor, mental, and sensory function. In addition, cutting-edge approaches, such as virtual reality (VR) and brain-computer interfaces (BCIs), promise to increase neuroplasticity and foster rehabilitation. However, many issues and restrictions still need to be resolved, including the demand for individualized treatments and the absence of defined standards. In conclusion, this review emphasizes the significance of neuroplasticity in brain rehabilitation. It identifies novel strategies and treatments that promise to enhance recovery in patients with neurological illnesses. Future studies should concentrate on improving these therapies and developing evidence-based standards to direct clinical practice and enhance outcomes for this vulnerable population.}, }
@article {pmid37585625, year = {2023}, author = {Li, L and Wang, S and Duan, X and Wang, Z and Chang, KC}, title = {Targeted Chemical Processing Initiating Biosome Action-Potential-Matched Artificial Synapses for the Brain-Machine Interface.}, journal = {ACS applied materials &amp; interfaces}, volume = {}, number = {}, pages = {}, doi = {10.1021/acsami.3c07684}, pmid = {37585625}, issn = {1944-8252}, abstract = {A great gap still exists between artificial synapses and their biological counterparts in operation voltage or stimulation duration. Here, an artificial synaptic device based on a thin-film transistor with an operating voltage (-50-50 mV) analogous to biological action potential is developed by targeted chemical processing with the help of supercritical fluids. Chemical molecules [hexamethyldisilazane (HMDS)] are elaborately chosen and brought into the target interface to form charge receptors through supercritical processing. These charge receptors with the ability of capturing electrons mimic neurotransmitter receptors in terms of mechanism and constitute key players accounting for the synaptic behaviors. The relatively lower electrical barrier height contributes to an action-potential-matched operating voltage and considerably low power consumption (∼1 pJ/synaptic event), minimizing the divide with biological synapse for a seamless linkage to the biosystem or brain-machine interface. The stable synaptic behaviors also lead to near-ideal accuracy in pattern recognition. Moreover, this methodology that introduces chemical groups into a target interface can be viewed as a platform technology that could be adapted to other conventional devices with suitable chemical molecules to reach designed synaptic behaviors. This environmentally friendly and low-temperature processing method, which can be performed even after device fabrication, has the potential to play an important role in the future development of bionic devices.}, }
@article {pmid37583792, year = {2023}, author = {Zhang, P and Pan, Y and Zha, R and Song, H and Yuan, C and Zhao, Q and Piao, Y and Ren, J and Chen, Y and Liang, P and Tao, R and Wei, Z and Zhang, X}, title = {Impulsivity-related right superior frontal gyrus as a biomarker of internet gaming disorder.}, journal = {General psychiatry}, volume = {36}, number = {4}, pages = {e100985}, pmid = {37583792}, issn = {2517-729X}, abstract = {BACKGROUND: Internet gaming disorder (IGD) is a mental health issue that affects individuals worldwide. However, the lack of knowledge about the biomarkers associated with the development of IGD has restricted the diagnosis and treatment of this disorder.<br><br>AIMS: We aimed to reveal the biomarkers associated with the development of IGD through resting-state brain network analysis and provide clues for the diagnosis and treatment of IGD.<br><br>METHODS: Twenty-six patients with IGD, 23 excessive internet game users (EIUs) who recurrently played internet games but were not diagnosed with IGD and 29 healthy controls (HCs) performed delay discounting task (DDT) and Iowa gambling task (IGT). Resting-state functional magnetic resonance imaging (fMRI) data were also collected.<br><br>RESULTS: Patients with IGD exhibited significantly lower hubness in the right medial orbital part of the superior frontal gyrus (ORBsupmed) than both the EIU and the HC groups. Additionally, the hubness of the right ORBsupmed was found to be positively correlated with the highest excessive internet gaming degree during the past year in the EIU group but not the IGD group; this might be the protective mechanism that prevents EIUs from becoming addicted to internet games. Moreover, the hubness of the right ORBsupmed was found to be related to the treatment outcome of patients with IGD, with higher hubness of this region indicating better recovery when undergoing forced abstinence. Further modelling analysis of the DDT and IGT showed that patients with IGD displayed higher impulsivity during the decision-making process, and impulsivity-related parameters were negatively correlated with the hubness of right ORBsupmed.<br><br>CONCLUSIONS: Our findings revealed that the impulsivity-related right ORBsupmed hubness could serve as a potential biomarker of IGD and provide clues for the diagnosis and treatment of IGD.}, }
@article {pmid37582062, year = {2023}, author = {Bellier, L and Llorens, A and Marciano, D and Gunduz, A and Schalk, G and Brunner, P and Knight, RT}, title = {Music can be reconstructed from human auditory cortex activity using nonlinear decoding models.}, journal = {PLoS biology}, volume = {21}, number = {8}, pages = {e3002176}, pmid = {37582062}, issn = {1545-7885}, support = {R01 EB026439/EB/NIBIB NIH HHS/United States ; P41 EB018783/EB/NIBIB NIH HHS/United States ; U24 NS109103/NS/NINDS NIH HHS/United States ; U01 NS108916/NS/NINDS NIH HHS/United States ; R13 NS118932/NS/NINDS NIH HHS/United States ; R01 NS021135/NS/NINDS NIH HHS/United States ; }, mesh = {Humans ; *Auditory Cortex/physiology ; *Music ; Brain Mapping ; Auditory Perception/physiology ; Temporal Lobe/physiology ; Acoustic Stimulation ; }, abstract = {Music is core to human experience, yet the precise neural dynamics underlying music perception remain unknown. We analyzed a unique intracranial electroencephalography (iEEG) dataset of 29 patients who listened to a Pink Floyd song and applied a stimulus reconstruction approach previously used in the speech domain. We successfully reconstructed a recognizable song from direct neural recordings and quantified the impact of different factors on decoding accuracy. Combining encoding and decoding analyses, we found a right-hemisphere dominance for music perception with a primary role of the superior temporal gyrus (STG), evidenced a new STG subregion tuned to musical rhythm, and defined an anterior-posterior STG organization exhibiting sustained and onset responses to musical elements. Our findings show the feasibility of applying predictive modeling on short datasets acquired in single patients, paving the way for adding musical elements to brain-computer interface (BCI) applications.}, }
@article {pmid37581962, year = {2023}, author = {Shao, Z and Dou, W and Ma, D and Zhai, X and Xu, Q and Pan, Y}, title = {A Novel Neurorehabilitation Prognosis Prediction Modeling on Separated Left-Right Hemiplegia Based on Brain-computer Interfaces Assisted Rehabilitation.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3305474}, pmid = {37581962}, issn = {1558-0210}, abstract = {It is essential for neuroscience and clinic to estimate the influence of neuro-intervention after brain damage. Most related studies have used Mirrored Contralesional-Ipsilesional hemispheres (MCI) methods flipping the axial neuroimaging on the x-axis in prognosis prediction. But left-right hemispheric asymmetry in the brain has become a consensus. MCI confounds the intrinsic brain asymmetry with the asymmetry caused by unilateral damage, leading to questions about the reliability of the results and difficulties in physiological explanations. We proposed the Separated Left-Right hemiplegia (SLR) method to model left and right hemiplegia separately. Two pipelines have been designed in contradistinction to demonstrate the validity of the SLR method, including MCI and removing intrinsic asymmetry (RIA) pipelines. A patient dataset with 18 left-hemiplegic and 22 right-hemiplegic stroke patients and a healthy dataset with 40 subjects, age- and sex-matched with the patients, were selected in the experiment. Blood-Oxygen Level-Dependent MRI and Diffusion Tensor Imaging were used to build brain networks whose nodes were defined by the Automated Anatomical Labeling atlas. We applied the same statistical and machine learning framework for all pipelines, logistic regression, artificial neural network, and support vector machine for classifying the patients who are significant or non-significant responders to brain-computer interfaces assisted training and optimal subset regression, support vector regression for predicting post-intervention outcomes. The SLR pipeline showed 5-15% improvement in accuracy and at least 0.1 upgrades in R[2], revealing common and unique recovery mechanisms after left and right strokes and helping clinicians make rehabilitation plans.}, }
@article {pmid37581121, year = {2023}, author = {Lee, JW and Song, KH}, title = {Fibrous hydrogels by electrospinning: Novel platforms for biomedical applications.}, journal = {Journal of tissue engineering}, volume = {14}, number = {}, pages = {20417314231191881}, pmid = {37581121}, issn = {2041-7314}, abstract = {Hydrogels, hydrophilic and biocompatible polymeric networks, have been used for numerous biomedical applications because they have exhibited abilities to mimic features of extracellular matrix (ECM). In particular, the hydrogels engineered with electrospinning techniques have shown great performances in biomedical applications. Electrospinning techniques are to generate polymeric micro/nanofibers that can mimic geometries of natural ECM by drawing micro/nanofibers from polymer precursors with electrical forces, followed by structural stabilization of them. By exploiting the electrospinning techniques, the fibrous hydrogels have been fabricated and utilized as 2D/3D cell culture platforms, implantable scaffolds, and wound dressings. In addition, some hydrogels that respond to external stimuli have been used to develop biosensors. For comprehensive understanding, this review covers electrospinning processes, hydrogel precursors used for electrospinning, characteristics of fibrous hydrogels and specific biomedical applications of electrospun fibrous hydrogels and highlight their potential to promote use in biomedical applications.}, }
@article {pmid37578926, year = {2023}, author = {Huang, J and Zhang, ZQ and Xiong, B and Wang, Q and Wan, B and Li, F and Yang, P}, title = {Cross-Subject Transfer Method Based on Domain Generalization for Facilitating Calibration of SSVEP-Based BCIs.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {31}, number = {}, pages = {3307-3319}, doi = {10.1109/TNSRE.2023.3305202}, pmid = {37578926}, issn = {1558-0210}, abstract = {In steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs), various spatial filtering methods based on individual calibration data have been proposed to alleviate the interference of spontaneous activities in SSVEP signals for enhancing the SSVEP detection performance. However, the time-consuming calibration session would increase the visual fatigue of subjects and reduce the usability of the BCI system. The key idea of this study is to propose a cross-subject transfer method based on domain generalization, which transfers the domain-invariant spatial filters and templates learned from source subjects to the target subject with no access to the EEG data from the target subject. The transferred spatial filters and templates are obtained by maximizing the intra- and inter-subject correlations using the SSVEP data corresponding to the target and its neighboring stimuli. For SSVEP detection of the target subject, four types of correlation coefficients are calculated to construct the feature vector. Experimental results estimated with three SSVEP datasets show that the proposed cross-subject transfer method improves the SSVEP detection performance compared to state-of-art methods. The satisfactory results demonstrate that the proposed method provides an effective transfer learning strategy requiring no tedious data collection process for new users, holding the potential of promoting practical applications of SSVEP-based BCI.}, }
@article {pmid37578918, year = {2023}, author = {Deny, P and Cheon, S and Son, H and Choi, KW}, title = {Hierarchical Transformer for Motor Imagery-Based Brain Computer Interface.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2023.3304646}, pmid = {37578918}, issn = {2168-2208}, abstract = {In this paper, we propose a novel transformer-based classification algorithm for the brain computer interface (BCI) using a motor imagery (MI) electroencephalogram (EEG) signal. To design the MI classification algorithm, we apply an up-to-date deep learning model, the transformer, that has revolutionized the natural language processing (NLP) and successfully widened its application to many other domains such as the computer vision. Within a long MI trial spanning a few seconds, the classification algorithm should give more attention to the time periods during which the intended motor task is imagined by the subject without any artifact. To achieve this goal, we propose a hierarchical transformer architecture that consists of a high-level transformer (HLT) and a low-level transformer (LLT). We break down a long MI trial into a number of short-term intervals. The LLT extracts a feature from each short-term interval, and the HLT pays more attention to the features from more relevant short-term intervals by using the self-attention mechanism of the transformer. We have done extensive tests of the proposed scheme on four open MI datasets, and shown that the proposed hierarchical transformer excels in both the subject-dependent and subject-independent tests.}, }
@article {pmid37577389, year = {2023}, author = {Huang, M and Hua, N and Zhuang, S and Fang, Q and Shang, J and Wang, Z and Tao, X and Niu, J and Li, X and Yu, P and Yang, W}, title = {Cux1[+] proliferative basal cells promote epidermal hyperplasia in chronic dry skin disease identified by single-cell RNA transcriptomics.}, journal = {Journal of pharmaceutical analysis}, volume = {13}, number = {7}, pages = {745-759}, pmid = {37577389}, issn = {2214-0883}, abstract = {Pathological dry skin is a disturbing and intractable healthcare burden, characterized by epithelial hyperplasia and severe itch. Atopic dermatitis (AD) and psoriasis models with complications of dry skin have been studied using single-cell RNA sequencing (scRNA-seq). However, scRNA-seq analysis of the dry skin mouse model (acetone/ether/water (AEW)-treated model) is still lacking. Here, we used scRNA-seq and in situ hybridization to identify a novel proliferative basal cell (PBC) state that exclusively expresses transcription factor CUT-like homeobox 1 (Cux1). Further in vitro study demonstrated that Cux1 is vital for keratinocyte proliferation by regulating a series of cyclin-dependent kinases (CDKs) and cyclins. Clinically, Cux1[+] PBCs were increased in patients with psoriasis, suggesting that Cux1[+] PBCs play an important part in epidermal hyperplasia. This study presents a systematic knowledge of the transcriptomic changes in a chronic dry skin mouse model, as well as a potential therapeutic target against dry skin-related dermatoses.}, }
@article {pmid37576014, year = {2023}, author = {Raizen, D and Bhavsar, R and Keenan, BT and Liu, PZ and Kegelman, TP and Chao, HH and Vapiwala, N and Rao, H}, title = {Increased posterior cingulate cortex blood flow in cancer-related fatigue.}, journal = {Frontiers in neurology}, volume = {14}, number = {}, pages = {1135462}, pmid = {37576014}, issn = {1664-2295}, abstract = {Fatigue is a common symptom associated with cancer treatments. Brain mechanisms underlying cancer-related fatigue (CRF) and its progression following therapy are poorly understood. Previous studies have suggested a role of the default mode network (DMN) in fatigue. In this study we used arterial spin labeling (ASL) perfusion functional magnetic resonance imaging (fMRI) and compared resting cerebral blood flow (CBF) differences in the posterior cingulate cortex (PCC), a core hub of the DMN, between 16 patients treated with radiation therapy (RAT) for prostate (9 males) or breast (7 females) cancer and 18 healthy controls (HC). Resting CBF in patients was also measured immediately after the performance of a fatiguing 20-min psychomotor vigilance task (PVT). Twelve of 16 cancer patients were further followed between 3 and 7 months after completion of the RAT (post-RAT). Patients reported elevated fatigue on RAT in comparison to post-RAT, but no change in sleepiness, suggesting that the underlying neural mechanisms of CRF progression are distinct from those regulating sleep drive progression. Compared to HC, patients showed significantly increased resting CBF in the PCC and the elevated PCC CBF persisted during the follow up visit. Post-PVT, but not pre-PVT, resting CBF changes in the PCC correlated with fatigue changes after therapy in patients with CRF, suggesting that PCC CBF following a fatiguing cognitive task may be a biomarker for CRF recovery.}, }
@article {pmid37569786, year = {2023}, author = {Géraudie, A and Riche, M and Lestra, T and Trotier, A and Dupuis, L and Mathon, B and Carpentier, A and Delatour, B}, title = {Effects of Low-Intensity Pulsed Ultrasound-Induced Blood-Brain Barrier Opening in P301S Mice Modeling Alzheimer's Disease Tauopathies.}, journal = {International journal of molecular sciences}, volume = {24}, number = {15}, pages = {}, pmid = {37569786}, issn = {1422-0067}, support = {ANR-10-IAIHU-06//Agence Nationale de la Recherche/ ; ANR-11-INBS-0011-NeurATRIS//Agence Nationale de la Recherche/ ; LRTCA grant to B.D. (no grant number)//Laboratoire de Recherche en Technologies Chirurgicales Avanc&#xe9;es/ ; NeurATRIS grant to B.D. and A.C. (no grant number)//NeurATRIS/ ; }, mesh = {Mice ; Animals ; *Alzheimer Disease/genetics/therapy/pathology ; Blood-Brain Barrier/pathology ; *Tauopathies/therapy/pathology ; Mice, Transgenic ; Ultrasonic Waves ; }, abstract = {Alzheimer's disease (AD) is the leading cause of dementia. No treatments have led to clinically meaningful impacts. A major obstacle for peripherally administered therapeutics targeting the central nervous system is related to the blood-brain barrier (BBB). Ultrasounds associated with microbubbles have been shown to transiently and safely open the BBB. In AD mouse models, the sole BBB opening with no adjunct drugs may be sufficient to reduce lesions and mitigate cognitive decline. However, these therapeutic effects are for now mainly assessed in preclinical mouse models of amyloidosis and remain less documented in tau lesions. The aim of the present study was therefore to evaluate the effects of repeated BBB opening using low-intensity pulsed ultrasounds (LIPU) in tau transgenic P301S mice with two main readouts: tau-positive lesions and microglial cells. Our results show that LIPU-induced BBB opening does not decrease tau pathology and may even potentiate the accumulation of pathological tau in selected brain regions. In addition, LIPU-BBB opening in P301S mice strongly reduced microglia densities in brain parenchyma, suggesting an anti-inflammatory action. These results provide a baseline for future studies using LIPU-BBB opening, such as adjunct drug therapies, in animal models and in AD patients.}, }
@article {pmid37567915, year = {2023}, author = {Pan, Y and Hao, N and Liu, N and Zhao, Y and Cheng, X and Ku, Y and Hu, Y}, title = {Mnemonic-trained brain tuning to a regular odd-even pattern subserves digit memory in children.}, journal = {NPJ science of learning}, volume = {8}, number = {1}, pages = {27}, pmid = {37567915}, issn = {2056-7936}, support = {71942001//National Natural Science Foundation of China (National Science Foundation of China)/ ; 62207025//National Natural Science Foundation of China (National Science Foundation of China)/ ; 32171082//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, abstract = {It is said that our species use mnemonics - that "magic of memorization" - to engrave an enormous amount of information in the brain. Yet, it is unclear how mnemonics affect memory and what the neural underpinnings are. In this electroencephalography study, we examined the hypotheses whether mnemonic training improved processing-efficiency and/or altered encoding-pattern to support memory enhancement. By 22-day training of a digit-image mnemonic (a custom memory technique used by world-class mnemonists), a group of children showed increased short-term memory after training, but with limited gain generalization. This training resulted in regular odd-even neural patterns (i.e., enhanced P200 and theta power during the encoding of digits at even- versus odd- positions in a sequence). Critically, the P200 and theta power effects predicted the training-induced memory improvement. These findings provide evidence of how mnemonics alter encoding pattern, as reflected in functional brain organization, to support memory enhancement.}, }
@article {pmid37567222, year = {2023}, author = {Wallace, DM and Benyamini, M and Nason-Tomaszewski, S and Costello, JT and Cubillos, LH and Mender, MJ and Temmar, H and Willsey, MS and Patil, PG and Chestek, CA and Zacksenhouse, M}, title = {Error detection and correction in intracortical brain-machine interfaces controlling two finger groups.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acef95}, pmid = {37567222}, issn = {1741-2552}, abstract = {OBJECTIVE: While brain machine interfaces (BMIs) are promising technologies that could provide direct pathways for controlling the external world and thus regaining motor capabilities, their effectiveness is hampered by decoding errors. Previous research has demonstrated the detection and correction of BMI outcome errors, which occur at the end of trials. Here we focus on continuous detection and correction of BMI execution errors, which occur during real-time movements.<br><br>APPROACH: Two adult male rhesus macaques were implanted with Utah arrays in the motor cortex. The monkeys performed single or two-finger group BMI tasks where a Kalman filter decoded binned spiking-band power into intended finger kinematics. Neural activity was analyzed to determine how it depends not only on the kinematics of the fingers, but also on the distance of each finger-group to its target. We developed a method to detect erroneous movements, i.e., consistent movements away from the target, from the same neural activity used by the Kalman filter. Detected errors were corrected by a simple stopping strategy, and the effect on performance was evaluated.<br><br>RESULTS: First we show that including distance to target explains significantly more variance of the recorded neural activity. Then, for the first time, we demonstrate that neural activity in motor cortex can be used to detect execution errors during BMI controlled movements. Keeping false positive rate below 5%, it was possible to achieve mean true positive rate of 28.1% online. Despite requiring 200 ms to detect and react to suspected errors, we were able to achieve a significant improvement in task performance via reduced orbiting time of one finger group.<br><br>SIGNIFICANCE: Neural activity recorded in motor cortex for BMI control can be used to detect and correct BMI errors and thus to improve performance. Further improvements in detection and correction can come from improving classification and correction strategies.}, }
@article {pmid37564400, year = {2023}, author = {Lee, I and Kim, D and Kim, S and Kim, HJ and Chung, US and Lee, JJ}, title = {Cognitive training based on functional near-infrared spectroscopy neurofeedback for the elderly with mild cognitive impairment: a preliminary study.}, journal = {Frontiers in aging neuroscience}, volume = {15}, number = {}, pages = {1168815}, pmid = {37564400}, issn = {1663-4365}, abstract = {INTRODUCTION: Mild cognitive impairment (MCI) is often described as an intermediate stage of the normal cognitive decline associated with aging and dementia. There is a growing interest in various non-pharmacological interventions for MCI to delay the onset and inhibit the progressive deterioration of daily life functions. Previous studies suggest that cognitive training (CT) contributes to the restoration of working memory and that the brain-computer-interface technique can be applied to elicit a more effective treatment response. However, these techniques have certain limitations. Thus, in this preliminary study, we applied the neurofeedback paradigm during CT to increase the working memory function of patients with MCI.<br><br>METHODS: Near-infrared spectroscopy (NIRS) was used to provide neurofeedback by measuring the changes in oxygenated hemoglobin in the prefrontal cortex. Thirteen elderly MCI patients who received CT-neurofeedback sessions four times on the left dorsolateral prefrontal cortex (dlPFC) once a week were recruited as participants.<br><br>RESULTS: Compared with pre-intervention, the activity of the targeted brain region increased when the participants first engaged in the training; after 4 weeks of training, oxygen saturation was significantly decreased in the left dlPFC. The participants demonstrated significantly improved working memory compared with pre-intervention and decreased activity significantly correlated with improved cognitive performance.<br><br>CONCLUSION: Our results suggest that the applications for evaluating brain-computer interfaces can aid in elucidation of the subjective mental workload that may create additional or decreased task workloads due to CT.}, }
@article {pmid37563528, year = {2023}, author = {Lin, CT and Wang, Y and Chen, SF and Huang, KC and Liao, LD}, title = {Design and verification of a wearable wireless 64-channel high-resolution EEG acquisition system with wi-fi transmission.}, journal = {Medical &amp; biological engineering &amp; computing}, volume = {}, number = {}, pages = {}, pmid = {37563528}, issn = {1741-0444}, abstract = {Brain-computer interfaces (BCIs) allow communication between the brain and the external world. This type of technology has been extensively studied. However, BCI instruments with high signal quality are typically heavy and large. Thus, recording electroencephalography (EEG) signals is an inconvenient task. In recent years, system-on-chip (SoC) approaches have been integrated into BCI research, and sensors for wireless portable devices have been developed; however, there is still considerable work to be done. As neuroscience research has advanced, EEG signal analyses have come to require more accurate data. Due to the limited bandwidth of Bluetooth wireless transmission technology, EEG measurement systems with more than 16 channels must be used to reduce the sampling rate and prevent data loss. Therefore, the goal of this study was to develop a multichannel, high-resolution (24-bit), high-sampling-rate EEG BCI device that transmits signals via Wi-Fi. We believe that this system can be used in neuroscience research. The EEG acquisition system proposed in this work is based on a Cortex-M4 microcontroller with a Wi-Fi subsystem, providing a multichannel design and improved signal quality. This system is compatible with wet sensors, Ag/AgCl electrodes, and dry sensors. A LabVIEW-based user interface receives EEG data via Wi-Fi transmission and saves the raw data for offline analysis. In previous cognitive experiments, event tags have been communicated using Recommended Standard 232 (RS-232). The developed system was validated through event-related potential (ERP) and steady-state visually evoked potential (SSVEP) experiments. Our experimental results demonstrate that this system is suitable for recording EEG measurements and has potential in practical applications. The advantages of the developed system include its high sampling rate and high amplification. Additionally, in the future, Internet of Things (IoT) technology can be integrated into the system for remote real-time analysis or edge computing.}, }
@article {pmid37558464, year = {2023}, author = {Kim, B and Erickson, BA and Fernandez-Nunez, G and Rich, R and Mentzelopoulos, G and Vitale, F and Medaglia, J}, title = {EEG Phase Can Be Predicted With Similar Accuracy Across Cognitive States After Accounting For Power and SNR.}, journal = {eNeuro}, volume = {}, number = {}, pages = {}, doi = {10.1523/ENEURO.0050-23.2023}, pmid = {37558464}, issn = {2373-2822}, abstract = {EEG phase is increasingly used in cognitive neuroscience, brain-computer interfaces (BCIs) and closed-loop stimulation devices. However, it is unknown how accurate EEG phase prediction is across cognitive states. We determined the EEG phase prediction accuracy of parieto-occipital alpha waves across rest and task states in 484 participants over 11 public datasets. We were able to track EEG phase accurately across various cognitive conditions and datasets, especially during periods of high instantaneous alpha power and signal-to-noise ratio (SNR). Although resting states generally have higher accuracies than task states, absolute accuracy differences were small, with most of these differences attributable to EEG power and SNR. These results suggest that experiments and technologies using EEG phase should focus more on minimizing external noise and waiting for periods of high power, rather than inducing a particular cognitive state.Significance StatementEEG phase is a neural signal related to many moment-to-moment behaviors and has consequently been used to inform brain-computer interfaces and closed-loop stimulation devices. However, prior research and demonstrations have forced the user to be in a single cognitive state, such as rest, making it unclear how EEG phase can apply to the varied contexts that real individuals are placed under. The current study showed that EEG phase can be consistently well predicted across different cognitive contexts after accounting for EEG power and signal-to-noise ratio. These findings represent an important next step for both understanding the cognitive and neurobiological correlates of EEG phase and optimizing EEG-based devices to administer more effective interventions.}, }
@article {pmid37556336, year = {2023}, author = {Jung, J and Moon, H and Yu, G and Hwang, H}, title = {Generative Perturbation Network for Universal Adversarial Attacks on Brain-Computer Interfaces.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2023.3303494}, pmid = {37556336}, issn = {2168-2208}, abstract = {Deep neural networks (DNNs) have successfully classified EEG-based brain-computer interface (BCI) systems. However, recent studies have found that well-designed input samples, known as adversarial examples, can easily fool well-performed deep neural networks model with minor perturbations undetectable by a human. This paper proposes an efficient generative model named generative perturbation network (GPN), which can generate universal adversarial examples with the same architecture for non-targeted and targeted attacks. Furthermore, the proposed model can be efficiently extended to conditionally or simultaneously generate perturbations for various targets and victim models. Our experimental evaluation demonstrates that perturbations generated by the proposed model outperform previous approaches for crafting signal-agnostic perturbations. We demonstrate that the extended network for signal-specific methods also significantly reduces generation time while performing similarly. The transferability across classification networks of the proposed method is superior to the other methods, which shows our perturbations' high level of generality. Our code is available for download on https://github.com/AIRLABkhu/Generative-Perturbation-Networks.}, }
@article {pmid37554408, year = {2023}, author = {Ma, H and Li, C and Zhu, Y and Peng, Y and Sun, L}, title = {Gait parameter fitting and adaptive enhancement based on cerebral blood oxygen information.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1205858}, pmid = {37554408}, issn = {1662-5161}, abstract = {Accurate recognition of patients' movement intentions and real-time adjustments are crucial in rehabilitation exoskeleton robots. However, some patients are unable to utilize electromyography (EMG) signals for this purpose due to poor or missing signals in their lower limbs. In order to address this issue, we propose a novel method that fits gait parameters using cerebral blood oxygen signals. Two types of walking experiments were conducted to collect brain blood oxygen signals and gait parameters from volunteers. Time domain, frequency domain, and spatial domain features were extracted from brain hemoglobin. The AutoEncoder-Decoder method is used for feature dimension reduction. A regression model based on the long short-term memory (LSTM) model was established to fit the gait parameters and perform incremental learning for new individual data. Cross-validation was performed on the model to enhance individual adaptivity and reduce the need for individual pre-training. The coefficient of determination (R2) for the gait parameter fit was 71.544%, with a mean square error (RMSE) of less than 3.321%. Following adaptive enhancement, the coefficient of R2 increased by 6.985%, while the RMSE decreased by 0.303%. These preliminary results indicate the feasibility of fitting gait parameters using cerebral blood oxygen information. Our research offers a new perspective on assisted locomotion control for patients who lack effective myoelectricity, thereby expanding the clinical application of rehabilitation exoskeleton robots. This work establishes a foundation for promoting the application of Brain-Computer Interface (BCI) technology in the field of sports rehabilitation.}, }
@article {pmid37552980, year = {2023}, author = {Gordon, SM and McDaniel, J and King, KW and Lawhern, V and Touryan, J}, title = {Decoding neural activity to assess individual latent state in ecologically valid contexts.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acee20}, pmid = {37552980}, issn = {1741-2552}, abstract = {OBJECTIVE: Currently, there exists very few ways to isolate cognitive processes, historically defined via highly controlled laboratory studies, in more ecologically valid contexts. Specifically, it remains unclear as to what extent associated patterns of neural activity observed under such constraints actually manifest outside the laboratory in a manner that can be used to make accurate inferences about latent states, associated cognitive processes, or proximal behavior. Improving our understanding of when and how specific patterns of neural activity manifest in ecologically valid scenarios would provide validation for laboratory-based approaches that study similar neural phenomena in isolation and meaningful insight into the latent states that occur during complex tasks.<br><br>APPROACH: Domain generalization methods, borrowed from the work of the brain-computer interface community, have the potential to capture high-dimensional patterns of neural activity in a way that can be reliably applied across experimental datasets in order to address this specific challenge. We previously used such an approach to decode phasic neural responses associated with visual target discrimination. Here, we extend that work to more tonic phenomena such as internal latent states. We use data from two highly controlled laboratory paradigms to train two separate domain-generalized models. We apply the trained models to an ecologically valid paradigm in which participants performed multiple, concurrent driving-related tasks while perched atop a six-degree-of-freedom ride-motion simulator.<br><br>MAIN RESULTS: Using the pretrained models, we estimate latent state and the associated patterns of neural activity. As the patterns of neural activity become more similar to those patterns observed in the training data, we find changes in behavior and task performance that are consistent with the observations from the original, laboratory-based paradigms.<br><br>SIGNIFICANCE: These results lend ecological validity to the original, highly controlled, experimental designs and provide a methodology for understanding the relationship between neural activity and behavior during complex tasks.}, }
@article {pmid37552589, year = {2023}, author = {Li, S and Wang, Z and Luo, H and Ding, L and Wu, D}, title = {T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2023.3303289}, pmid = {37552589}, issn = {1558-2531}, abstract = {OBJECTIVE: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming and user-unfriendly. Transfer learning (TL) has been proposed to shorten or eliminate this calibration, but existing TL approaches mainly consider offline settings, where all unlabeled EEG trials from the new user are available.<br><br>METHODS: This paper proposes Test-Time Information Maximization Ensemble (T-TIME) to accommodate the most challenging online TL scenario, where unlabeled EEG data from the new user arrive in a stream, and immediate classification is performed. T-TIME initializes multiple classifiers from the aligned source data. When an unlabeled test EEG trial arrives, T-TIME first predicts its labels using ensemble learning, and then updates each classifier by conditional entropy minimization and adaptive marginal distribution regularization. Our code is publicized.<br><br>RESULTS: Extensive experiments on three public motor imagery based BCI datasets demonstrated that T-TIME outperformed about 20 classical and state-of-the-art TL approaches.<br><br>SIGNIFICANCE: To our knowledge, this is the first work on test time adaptation for calibration-free EEG-based BCIs, making plug-and-play BCIs possible.}, }
@article {pmid37550947, year = {2023}, author = {Zhang, J and Xu, B and Lou, X and Wu, Y and Shen, X}, title = {MI-based BCI with accurate real-time three-class classification processing and light control application.}, journal = {Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine}, volume = {}, number = {}, pages = {9544119231187287}, doi = {10.1177/09544119231187287}, pmid = {37550947}, issn = {2041-3033}, abstract = {The use of brain-computer interfaces (BCIs) to control intelligent devices is a current and future research direction. However, the challenges of low accuracy of real-time recognition and the need for multiple electroencephalographic channels are yet to be overcome. While a number of research teams have proposed many ways to improve offline classification accuracy, the potential problems in real-time experiments are often overlooked. In this study, we proposed a label-based channel diversion preprocessing to solve the problem of low real-time classification accuracy. The Tikhonov regularised common spatial-pattern algorithm (TRCSP) and one vs rest support vector machine (OVR-SVM) were used for feature extraction and pattern classification. High accuracy was achieved in real-time three-class classification using only three channels (average real-time accuracy of 87.46%, with a maximum of 90.33%). In addition, the stability and reliability of the system were verified through lighting control experiments in a real environment. Using the autonomy of MI and real-time feedback of light brightness, we have built a fully autonomous interactive system. The improvement in the real-time classification accuracy in this study is of great significance to the industrialisation of BCI.}, }
@article {pmid37550747, year = {2023}, author = {Fu, Z and Tian, Z and Chen, Y and Jia, Z and Wang, C and Zhang, X and Zhang, W and Li, G and Wei, X and Huang, Y}, title = {Analysis of the efficacy of a single subumbilical stoma for bilateral cutaneous ureterostomy after radical cystectomy.}, journal = {European journal of medical research}, volume = {28}, number = {1}, pages = {273}, pmid = {37550747}, issn = {2047-783X}, support = {BE2020654//Key Research and Development Program of Jiangsu Province under Grant Agreement/ ; }, mesh = {Humans ; Ureterostomy/methods ; Cystectomy/methods ; Quality of Life ; Retrospective Studies ; *Urinary Diversion/methods ; *Urinary Bladder Neoplasms/surgery ; }, abstract = {BACKGROUND: Radical cystectomy and urinary diversion are the standard surgical treatments for patients with muscle-invasive or high-risk, or recurrent non-muscle-invasive bladder cancer. Although this approach significantly prolongs patient survival, it can lead to postoperative complications. This study aims to compare the efficacy and complications of bilateral cutaneous ureterostomy with a single subumbilical stoma to those of cutaneous ureterostomy with two stomas and an ileal conduit as a means of urinary diversion after radical cystectomy. The findings of this study will provide valuable information for healthcare providers in selecting the appropriate urinary diversion method for their patients.<br><br>METHODS: The clinical data for 108 patients who received bilateral cutaneous ureterostomy with a single subumbilical stoma (ureterostomy with a single stoma group), cutaneous ureterostomy with two stomas (ureterostomy with two stomas group), or an ileal conduit (ileal conduit group) after radical cystectomy were retrospectively analysed. The operative time, pathological stage, survival status, perioperative complication rate, rate of successful first extubation, rehospitalization rate at 6&#xa0;months after surgery,ostomy-related medical costs,and postoperative quality of life were compared between the three groups of patients.<br><br>RESULTS: A significant difference in the operative time was found between the three groups (P&#x2009;=&#x2009;0.001). No significant differences in pathological stage, survival status, perioperative complication rate, rehospitalization rate at 6&#xa0;months after surgery, or bladder cancer index (BCI) score were identified among the three groups. The difference in the successful first extubation rate between the three groups of patients was significant (P&#x2009;=&#x2009;0.001). Significant differences in ostomy-related medical costs were observed among the three groups of patients (P&#x2009;=&#x2009;0.006).<br><br>CONCLUSION: A single subumbilical stoma for bilateral cutaneous ureterostomy after radical cystectomy may result in shorter surgery time, increased success rates for initial catheter removal, and lower medical expenses. However, to confirm these findings, further prospective randomized clinical trials are necessary.}, }
@article {pmid37548304, year = {2023}, author = {Cheng, Z and Hu, S and Han, G and Fang, K and Jin, X and Ordinola, A and Özarslan, E and Bai, R}, title = {Using deep learning to accelerate magnetic resonance measurements of molecular exchange.}, journal = {The Journal of chemical physics}, volume = {159}, number = {5}, pages = {}, doi = {10.1063/5.0159343}, pmid = {37548304}, issn = {1089-7690}, abstract = {Real-time monitoring and quantitative measurement of molecular exchange between different microdomains are useful to characterize the local dynamics in porous media and biomedical applications of magnetic resonance. Diffusion exchange spectroscopy (DEXSY) is a noninvasive technique for such measurements. However, its application is largely limited by the involved long acquisition time and complex parameter estimation. In this study, we introduce a physics-guided deep neural network that accelerates DEXSY acquisition in a data-driven manner. The proposed method combines sampling pattern optimization and physical parameter estimation into a unified framework. Comprehensive simulations and experiments based on a two-site exchange system are conducted to demonstrate this new sampling optimization method in terms of accuracy, repeatability, and efficiency. This general framework can be adapted for other molecular exchange magnetic resonance measurements.}, }
@article {pmid37547152, year = {2023}, author = {Wang, F and Wan, Y and Li, Z and Qi, F and Li, J}, title = {A cross-subject decoding algorithm for patients with disorder of consciousness based on P300 brain computer interface.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1167125}, pmid = {37547152}, issn = {1662-4548}, abstract = {BACKGROUND: Brain computer interface (BCI) technology may provide a new way of communication for some patients with disorder of consciousness (DOC), which can directly connect the brain and external devices. However, the DOC patients' EEG differ significantly from that of the normal person and are difficult to collected, the decoding algorithm currently only is trained based on a small amount of the patient's own data and performs poorly.<br><br>METHODS: In this study, a decoding algorithm called WD-ADSTCN based on domain adaptation is proposed to improve the DOC patients' P300 signal detection. We used the Wasserstein distance to filter the normal population data to increase the training data. Furthermore, an adversarial approach is adopted to resolve the differences between the normal and patient data.<br><br>RESULTS: The results showed that in the cross-subject P300 detection of DOC patients, 7 of 11 patients achieved an average accuracy of over 70%. Furthermore, their clinical diagnosis changed and CRS-R scores improved three months after the experiment.<br><br>CONCLUSION: These results demonstrated that the proposed method could be employed in the P300 BCI system for the DOC patients, which has important implications for the clinical diagnosis and prognosis of these patients.}, }
@article {pmid37547144, year = {2023}, author = {Li, JW and Lin, D and Che, Y and Lv, JJ and Chen, RJ and Wang, LJ and Zeng, XX and Ren, JC and Zhao, HM and Lu, X}, title = {An innovative EEG-based emotion recognition using a single channel-specific feature from the brain rhythm code method.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1221512}, pmid = {37547144}, issn = {1662-4548}, abstract = {INTRODUCTION: Efficiently recognizing emotions is a critical pursuit in brain-computer interface (BCI), as it has many applications for intelligent healthcare services. In this work, an innovative approach inspired by the genetic code in bioinformatics, which utilizes brain rhythm code features consisting of δ, θ, α, β, or γ, is proposed for electroencephalography (EEG)-based emotion recognition.<br><br>METHODS: These features are first extracted from the sequencing technique. After evaluating them using four conventional machine learning classifiers, an optimal channel-specific feature that produces the highest accuracy in each emotional case is identified, so emotion recognition through minimal data is realized. By doing so, the complexity of emotion recognition can be significantly reduced, making it more achievable for practical hardware setups.<br><br>RESULTS: The best classification accuracies achieved for the DEAP and MAHNOB datasets range from 83-92%, and for the SEED dataset, it is 78%. The experimental results are impressive, considering the minimal data employed. Further investigation of the optimal features shows that their representative channels are primarily on the frontal region, and associated rhythmic characteristics are typical of multiple kinds. Additionally, individual differences are found, as the optimal feature varies with subjects.<br><br>DISCUSSION: Compared to previous studies, this work provides insights into designing portable devices, as only one electrode is appropriate to generate satisfactory performances. Consequently, it would advance the understanding of brain rhythms, which offers an innovative solution for classifying EEG signals in diverse BCI applications, including emotion recognition.}, }
@article {pmid37547006, year = {2023}, author = {Noel, JP and Bockbrader, M and Colachis, S and Solca, M and Orepic, P and Ganzer, PD and Haggard, P and Rezai, A and Blanke, O and Serino, A}, title = {Human primary motor cortex indexes the onset of subjective intention in brain-machine-interface mediated actions.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, pmid = {37547006}, abstract = {Self-initiated behavior is accompanied by the experience of willing our actions. Here, we leverage the unique opportunity to examine the full intentional chain - from will (W) to action (A) to environmental effects (E) - in a tetraplegic person fitted with a primary motor cortex (M1) brain machine interface (BMI) generating hand movements via neuromuscular electrical stimulation (NMES). This combined BMI-NMES approach allowed us to selectively manipulate each element of the intentional chain (W, A, and E) while performing extra-cellular recordings and probing subjective experience. Our results reveal single-cell, multi-unit, and population-level dynamics in human M1 that encode W and may predict its subjective onset. Further, we show that the proficiency of a neural decoder in M1 reflects the degree of W-A binding, tracking the participant's subjective experience of intention in (near) real time. These results point to M1 as a critical node in forming the subjective experience of intention and demonstrate the relevance of intention-related signals for translational neuroprosthetics.}, }
@article {pmid37546532, year = {2023}, author = {Huang, Z and Ma, Y and Su, J and Shi, H and Jia, S and Yuan, B and Li, W and Geng, J and Yang, T}, title = {CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition.}, journal = {Frontiers in physiology}, volume = {14}, number = {}, pages = {1200656}, pmid = {37546532}, issn = {1664-042X}, abstract = {EEG-based emotion recognition through artificial intelligence is one of the major areas of biomedical and machine learning, which plays a key role in understanding brain activity and developing decision-making systems. However, the traditional EEG-based emotion recognition is a single feature input mode, which cannot obtain multiple feature information, and cannot meet the requirements of intelligent and high real-time brain computer interface. And because the EEG signal is nonlinear, the traditional methods of time domain or frequency domain are not suitable. In this paper, a CNN-DSC-Bi-LSTM-Attention (CDBA) model based on EEG signals for automatic emotion recognition is presented, which contains three feature-extracted channels. The normalized EEG signals are used as an input, the feature of which is extracted by multi-branching and then concatenated, and each channel feature weight is assigned through the attention mechanism layer. Finally, Softmax was used to classify EEG signals. To evaluate the performance of the proposed CDBA model, experiments were performed on SEED and DREAMER datasets, separately. The validation experimental results show that the proposed CDBA model is effective in classifying EEG emotions. For triple-category (positive, neutral and negative) and four-category (happiness, sadness, fear and neutrality), the classification accuracies were respectively 99.44% and 99.99% on SEED datasets. For five classification (Valence 1-Valence 5) on DREAMER datasets, the accuracy is 84.49%. To further verify and evaluate the model accuracy and credibility, the multi-classification experiments based on ten-fold cross-validation were conducted, the elevation indexes of which are all higher than other models. The results show that the multi-branch feature fusion deep learning model based on attention mechanism has strong fitting and generalization ability and can solve nonlinear modeling problems, so it is an effective emotion recognition method. Therefore, it is helpful to the diagnosis and treatment of nervous system diseases, and it is expected to be applied to emotion-based brain computer interface systems.}, }
@article {pmid37543245, year = {2023}, author = {Ullah, R and Shen, Y and Zhou, YD and Fu, J}, title = {Perinatal metabolic inflammation in the hypothalamus impairs the development of homeostatic feeding circuitry.}, journal = {Metabolism: clinical and experimental}, volume = {}, number = {}, pages = {155677}, doi = {10.1016/j.metabol.2023.155677}, pmid = {37543245}, issn = {1532-8600}, abstract = {Over the past few decades, there has been a global increase in childhood obesity. This rise in childhood obesity contributes to the susceptibility of impaired metabolism during both childhood and adulthood. The hypothalamus, specifically the arcuate nucleus (ARC), houses crucial neurons involved in regulating homeostatic feeding. These neurons include proopiomelanocortin (POMC) and agouti-related peptide (AGRP) secreting neurons. They play a vital role in sensing nutrients and metabolic hormones like insulin, leptin, and ghrelin. The neurogenesis of AGRP and POMC neurons completes at birth; however, axon development and synapse formation occur during the postnatal stages in rodents. Insulin, leptin, and ghrelin are the essential regulators of POMC and AGRP neurons. Maternal obesity and postnatal overfeeding or a high-fat diet (HFD) feeding cause metabolic inflammation, disrupted signaling of metabolic hormones, netrin-1, and neurogenic factors, neonatal obesity, and defective neuronal development in animal models; however, the mechanism is unclear. Within the hypothalamus and other brain areas, there exists a wide range of interconnected neuronal populations that regulate various aspects of feeding. However, this review aims to discuss how perinatal metabolic inflammation influences the development of POMC and AGRP neurons within the hypothalamus.}, }
@article {pmid37540396, year = {2023}, author = {Mahrooz, MH and Fattahzadeh, F and Gharibzadeh, S}, title = {Decoding the Debate: A Comparative Study of Brain-Computer Interface and Neurofeedback.}, journal = {Applied psychophysiology and biofeedback}, volume = {}, number = {}, pages = {}, pmid = {37540396}, issn = {1573-3270}, abstract = {Brain-Computer Interface (BCI) and Neurofeedback (NF) both rely on the technology to capture brain activity. However, the literature lacks a clear distinction between the two, with some scholars categorizing NF as a special case of BCI while others view BCI as a natural extension of NF, or classify them as fundamentally different entities. This ambiguity hinders the flow of information and expertise among scholars and can cause confusion. To address this issue, we conducted a study comparing BCI and NF from two perspectives: the background and context within which BCI and NF developed, and their system design. We utilized Functional Flow Block Diagram (FFBD) as a system modelling approach to visualize inputs, functions, and outputs to compare BCI and NF at a conceptual level. Our analysis revealed that while NF is a subset of the biofeedback method that requires data from the brain to be extracted and processed, the device performing these tasks is a BCI system by definition. Therefore, we conclude that NF should be considered a specific application of BCI technology. By clarifying the relationship between BCI and NF, we hope to facilitate better communication and collaboration among scholars in these fields.}, }
@article {pmid37540385, year = {2023}, author = {Maiseli, B and Abdalla, AT and Massawe, LV and Mbise, M and Mkocha, K and Nassor, NA and Ismail, M and Michael, J and Kimambo, S}, title = {Brain-computer interface: trend, challenges, and threats.}, journal = {Brain informatics}, volume = {10}, number = {1}, pages = {20}, pmid = {37540385}, issn = {2198-4018}, abstract = {Brain-computer interface (BCI), an emerging technology that facilitates communication between brain and computer, has attracted a great deal of research in recent years. Researchers provide experimental results demonstrating that BCI can restore the capabilities of physically challenged people, hence improving the quality of their lives. BCI has revolutionized and positively impacted several industries, including entertainment and gaming, automation and control, education, neuromarketing, and neuroergonomics. Notwithstanding its broad range of applications, the global trend of BCI remains lightly discussed in the literature. Understanding the trend may inform researchers and practitioners on the direction of the field, and on where they should invest their efforts more. Noting this significance, we have analyzed 25,336 metadata of BCI publications from Scopus to determine advancement of the field. The analysis shows an exponential growth of BCI publications in China from 2019 onwards, exceeding those from the United States that started to decline during the same period. Implications and reasons for this trend are discussed. Furthermore, we have extensively discussed challenges and threats limiting exploitation of BCI capabilities. A typical BCI architecture is hypothesized to address two prominent BCI threats, privacy and security, as an attempt to make the technology commercially viable to the society.}, }
@article {pmid37539380, year = {2023}, author = {Huang, Y and Huan, Y and Zou, Z and Pei, W and Gao, X and Wang, Y and Zheng, L}, title = {A wearable group-synchronized EEG system for multi-subject brain-computer interfaces.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1176344}, pmid = {37539380}, issn = {1662-4548}, abstract = {OBJECTIVE: The multi-subject brain-computer interface (mBCI) is becoming a key tool for the analysis of group behaviors. It is necessary to adopt a neural recording system for collaborative brain signal acquisition, which is usually in the form of a fixed wire.<br><br>APPROACH: In this study, we designed a wireless group-synchronized neural recording system that supports real-time mBCI and event-related potential (ERP) analysis. This system uses a wireless synchronizer to broadcast events to multiple wearable EEG amplifiers. The simultaneously received broadcast signals are marked in data packets to achieve real-time event correlation analysis of multiple targets in a group.<br><br>MAIN RESULTS: To evaluate the performance of the proposed real-time group-synchronized neural recording system, we conducted collaborative signal sampling on 10 wireless mBCI devices. The average signal correlation reached 99.8%, the amplitude of average noise was 0.87 &#x3bc;V, and the average common mode rejection ratio (CMRR) reached 109.02 dB. The minimum synchronization error is 237 &#x3bc;s. We also tested the system in real-time processing of the steady-state visual-evoked potential (SSVEP) ranging from 8 to 15.8 Hz. Under 40 target stimulators, with 2 s data length, the average information transfer rate (ITR) reached 150 &#xb1; 20 bits/min, and the highest reached 260 bits/min, which was comparable to the marketing leading EEG system (the average: 150 &#xb1; 15 bits/min; the highest: 280 bits/min). The accuracy of target recognition in 2 s was 98%, similar to that of the Synamps2 (99%), but a higher signal-to-noise ratio (SNR) of 5.08 dB was achieved. We designed a group EEG cognitive experiment; to verify, this system can be used in noisy settings.<br><br>SIGNIFICANCE: The evaluation results revealed that the proposed real-time group-synchronized neural recording system is a high-performance tool for real-time mBCI research. It is an enabler for a wide range of future applications in collaborative intelligence, cognitive neurology, and rehabilitation.}, }
@article {pmid37537987, year = {2023}, author = {Potter, SJ and Erdody, ML and Bamford, NJ and Knowles, EJ and Menzies-Gow, N and Morrison, PK and Argo, CM and McIntosh, BJ and Kaufman, K and Harris, PA and Bailey, SR}, title = {Development of a body condition index to estimate adiposity in ponies and horses from morphometric measurements.}, journal = {Equine veterinary journal}, volume = {}, number = {}, pages = {}, doi = {10.1111/evj.13975}, pmid = {37537987}, issn = {2042-3306}, support = {LP100200224//Australian Research Council/ ; //MARS Petcare UK/ ; }, abstract = {BACKGROUND: There is a high prevalence of obesity in ponies and pleasure horses. This may be associated with equine metabolic syndrome and an increased risk of laminitis. Body condition scoring (BCS) systems are widely used but are subjective and not very sensitive.<br><br>OBJECTIVES: To derive a body condition index (BCI), based on objective morphometric measurements, that correlates with % body fat.<br><br>STUDY DESIGN: Retrospective cohort study.<br><br>METHODS: Morphometric measurements were obtained from 21 ponies and horses in obese and moderate body condition. Percentage body fat was determined using the deuterium dilution method and the BCI was derived to give the optimal correlation with body fat, applying appropriate weightings. The index was then validated by assessing inter-observer variation and correlation with % body fat in a separate population of Welsh ponies; and finally, the correlation between BCI and BCS was evaluated in larger populations from studies undertaken in Australia, the United Kingdom and the United States.<br><br>RESULTS: The BCI correlated well with adiposity in the ponies and horses, giving a Pearson r value of 0.74 (P&#x2009;<&#x2009;0.001); however, it was found to slightly overestimate the % body fat in leaner animals and underestimate in more obese animals. In field studies, the correlation between BCI and BCS varied particularly in Shetlands and miniature ponies, presumably due to differences in body shape.<br><br>MAIN LIMITATIONS: Further work may be required to adapt the BCI to a method that is more applicable for Shetlands and miniature ponies.<br><br>CONCLUSIONS: This BCI was able to provide an index of adiposity which compared favourably with condition scoring in terms of accuracy of estimating adiposity; and was more consistent and repeatable when used by inexperienced assessors. Therefore, this may be a useful tool for assessing adiposity; and may be more sensitive than condition scoring for tracking weight gain or weight loss in individual animals.}, }
@article {pmid37536094, year = {2023}, author = {Li, S and Tang, Z and Yang, L and Li, M and Shang, Z}, title = {Application of deep reinforcement learning for spike sorting under multi-class imbalance.}, journal = {Computers in biology and medicine}, volume = {164}, number = {}, pages = {107253}, doi = {10.1016/j.compbiomed.2023.107253}, pmid = {37536094}, issn = {1879-0534}, abstract = {Spike sorting is the basis for analyzing spike firing patterns encoded in high-dimensional information spaces. With the fact that high-density microelectrode arrays record multiple neurons simultaneously, the data collected often suffers from two problems: a few overlapping spikes and different neuronal firing rates, which both belong to the multi-class imbalance problem. Since deep reinforcement learning (DRL) assign targeted attention to categories through reward functions, we propose ImbSorter to implement spike sorting under multi-class imbalance. We describe spike sorting as a Markov sequence decision and construct a dynamic reward function (DRF) to improve the sensitivity of the agent to minor classes based on the inter-class imbalance ratios. The agent is eventually guided by the optimal strategy to classify spikes. We consider the Wave_Clus dataset, which contains overlapping spikes and diverse noise levels, and the macaque dataset, which has a multi-scale imbalance. ImbSorter is compared with classical DRL architectures, traditional machine learning algorithms, and advanced overlapping spike sorting techniques on these two above datasets. ImbSorter obtained improved results on the Macro_F1. The results show ImbSorter has a promising ability to resist overlapping and noise interference. It has high stability and promising performance in processing spikes with different degrees of skewed distribution.}, }
@article {pmid37535483, year = {2023}, author = {Leoni, J and Strada, SC and Tanelli, M and Proverbio, AM}, title = {MIRACLE: MInd ReAding CLassification Engine.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3301507}, pmid = {37535483}, issn = {1558-0210}, abstract = {Brain-computer interfaces (BCIs) have revolutionized the way humans interact with machines, particularly for patients with severe motor impairments. EEG-based BCIs have limited functionality due to the restricted pool of stimuli that they can distinguish, while those elaborating event-related potentials up to now employ paradigms that require the patient's perception of the eliciting stimulus. In this work, we propose MIRACLE: a novel BCI system that combines functional data analysis and machine-learning techniques to decode patients' minds from the elicited potentials. MIRACLE relies on a hierarchical ensemble classifier recognizing 10 different semantic categories of imagined stimuli. We validated MIRACLE on an extensive dataset collected from 20 volunteers, with both imagined and perceived stimuli, to compare the system performance on the two. Furthermore, we quantify the importance of each EEG channel in the decision-making process of the classifier, which can help reduce the number of electrodes required for data acquisition, enhancing patients' comfort.}, }
@article {pmid37533980, year = {2023}, author = {Sarhan, SM and Al-Faiz, MZ and Takhakh, AM}, title = {A review on EMG/EEG based control scheme of upper limb rehabilitation robots for stroke patients.}, journal = {Heliyon}, volume = {9}, number = {8}, pages = {e18308}, pmid = {37533980}, issn = {2405-8440}, abstract = {Stroke is a common worldwide health problem and a crucial contributor to gained disability. The abilities of people, who are subjected to stroke, to live independently are significantly affected since affected upper limbs' functions are essential for our daily life. This review article focuses on emerging trends in BCI-controlled rehabilitation techniques based on EMG, EEG, or EGM + EEG signals in the last few years. Working on developing rehabilitation robotics, is considered a wealthy scientific area for researchers in the last period. There is a significant advantage that the human acquires from the interaction between the machine and his body, rehabilitation for a patient's limb is very important to get the body limb recovery, and this is what is provided mostly by applying robotic devices.}, }
@article {pmid37533587, year = {2023}, author = {Pais-Vieira, M and Aksenova, T and Tsytsarev, V and Faber, J}, title = {Editorial: Sensorimotor decoding: characterization and modeling for rehabilitation and assistive technologies.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1243226}, pmid = {37533587}, issn = {1662-5161}, }
@article {pmid37531857, year = {2023}, author = {Mirzabagherian, H and Menhaj, MB and Suratgar, AA and Talebi, N and Abbasi Sardari, MR and Sajedin, A}, title = {Temporal-spatial convolutional residual network for decoding attempted movement related EEG signals of subjects with spinal cord injury.}, journal = {Computers in biology and medicine}, volume = {164}, number = {}, pages = {107159}, doi = {10.1016/j.compbiomed.2023.107159}, pmid = {37531857}, issn = {1879-0534}, abstract = {Brain Computer Interface (BCI) offers a promising approach to restoring hand functionality for people with cervical spinal cord injury (SCI). A reliable classification of brain activities based on appropriate flexibility in feature extraction could enhance BCI systems performance. In the present study, based on convolutional layers with temporal-spatial, Separable and Depthwise structures, we develop Temporal-Spatial Convolutional Residual Network)TSCR-Net(and Temporal-Spatial Convolutional Iterative Residual Network)TSCIR-Net(structures to classify electroencephalogram (EEG) signals. Using EEG signals in five different hand movement classes of SCI people, we compare the effectiveness of TSCIR-Net and TSCR-Net models with some competitive methods. We use the bayesian hyperparameter optimization algorithm to tune the hyperparameters of compact convolutional neural networks. In order to show the high generalizability of the proposed models, we compare the results of the models in different frequency ranges. Our proposed models decoded distinctive characteristics of different movement efforts and obtained higher classification accuracy than previous deep neural networks. Our findings indicate that TSCIR-Net and TSCR-Net models fulfills a better classification accuracy of 71.11%, and 64.55% for EEG_All and 57.74%, and 67.87% for EEG_Low frequency data sets than the compared methods in the literature.}, }
@article {pmid37529233, year = {2023}, author = {Chandrasekaran, S and Bhagat, NA and Ramdeo, R and Ebrahimi, S and Sharma, PD and Griffin, DG and Stein, A and Harkema, SJ and Bouton, CE}, title = {Case study: persistent recovery of hand movement and tactile sensation in peripheral nerve injury using targeted transcutaneous spinal cord stimulation.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1210544}, pmid = {37529233}, issn = {1662-4548}, abstract = {Peripheral nerve injury can lead to chronic pain, paralysis, and loss of sensation, severely affecting quality of life. Spinal cord stimulation has been used in the clinic to provide pain relief arising from peripheral nerve injuries, however, its ability to restore function after peripheral nerve injury have not been explored. Neuromodulation of the spinal cord through transcutaneous spinal cord stimulation (tSCS), when paired with activity-based training, has shown promising results towards restoring volitional limb control in people with spinal cord injury. We show, for the first time, the effectiveness of targeted tSCS in restoring strength (407% increase from 1.79 ± 1.24 N to up to 7.3 ± 0.93 N) and significantly increasing hand dexterity in an individual with paralysis due to a peripheral nerve injury (PNI). Furthermore, this is the first study to document a persisting 3-point improvement during clinical assessment of tactile sensation in peripheral injury after receiving 6 weeks of tSCS. Lastly, the motor and sensory gains persisted for several months after stimulation was received, suggesting tSCS may lead to long-lasting benefits, even in PNI. Non-invasive spinal cord stimulation shows tremendous promise as a safe and effective therapeutic approach with broad applications in functional recovery after debilitating injuries.}, }
@article {pmid37528087, year = {2023}, author = {Fang, A and Wang, Y and Guan, N and Zuo, Y and Lin, L and Guo, B and Mo, A and Wu, Y and Lin, X and Cai, W and Chen, X and Ye, J and Abdelrahman, Z and Li, X and Zheng, H and Wu, Z and Jin, S and Xu, K and Huang, Y and Gu, X and Yu, B and Wang, X}, title = {Author Correction: Porous microneedle patch with sustained delivery of extracellular vesicles mitigates severe spinal cord injury.}, journal = {Nature communications}, volume = {14}, number = {1}, pages = {4603}, doi = {10.1038/s41467-023-40368-w}, pmid = {37528087}, issn = {2041-1723}, }
@article {pmid37527325, year = {2023}, author = {Razzak, I and Bouadjenek, MR and Saris, RA and Ding, W}, title = {Support Matrix Machine via Joint l2,1 and Nuclear Norm Minimization Under Matrix Completion Framework for Classification of Corrupted Data.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2023.3293888}, pmid = {37527325}, issn = {2162-2388}, abstract = {Traditional support vector machines (SVMs) are fragile in the presence of outliers; even a single corrupt data point can arbitrarily alter the quality of the approximation. If even a small fraction of columns is corrupted, then classification performance will inevitably deteriorate. This article considers the problem of high-dimensional data classification, where a number of the columns are arbitrarily corrupted. An efficient Support Matrix Machine that simultaneously performs matrix Recovery (SSMRe) is proposed, i.e. feature selection and classification through joint minimization of l2,1 (the nuclear norm of L). The data are assumed to consist of a low-rank clean matrix plus a sparse noisy matrix. SSMRe works under incoherence and ambiguity conditions and is able to recover an intrinsic matrix of higher rank in the presence of data densely corrupted. The objective function is a spectral extension of the conventional elastic net; it combines the property of matrix recovery along with low rank and joint sparsity to deal with complex high-dimensional noisy data. Furthermore, SSMRe leverages structural information, as well as the intrinsic structure of data, avoiding the inevitable upper bound. Experimental results on different real-time applications, supported by the theoretical analysis and statistical testing, show significant gain for BCI, face recognition, and person identification datasets, especially in the presence of outliers, while preserving a reasonable number of support vectors.}, }
@article {pmid37527288, year = {2023}, author = {Liang, S and Xuan, C and Hang, W and Lei, B and Wang, J and Qin, J and Choi, KS}, title = {Domain-generalized EEG Classification with Category-oriented Feature Decorrelation and Cross-view Consistency Learning.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3300961}, pmid = {37527288}, issn = {1558-0210}, abstract = {Generalizing the electroencephalogram (EEG) decoding methods to unseen subjects is an important research direction for realizing practical application of brain-computer interfaces (BCIs). Since distribution shifts across subjects, the performance of most current deep neural networks for decoding EEG signals degrades when dealing with unseen subjects. Domain generalization (DG) aims to tackle this issue by learning invariant representations across subjects. To this end, we propose a novel domain-generalized EEG classification framework, named FDCL, to generalize EEG decoding through category-relevant and -irrelevant Feature Decorrelation and Cross-view invariant feature Learning. Specifically, we first devise data augmented regularization through mixing the segments of same-category features from multiple subjects, which increases the diversity of EEG data by spanning the space of subjects. Furthermore, we introduce feature decorrelation regularization to learn the weights of the augmented EEG trials to remove the dependencies between their features, so that the true mapping relationship between relevant features and corresponding labels can be better established. To further distill subject-invariant EEG feature representations, cross-view consistency learning regularization is introduced to encourage consistent predictions of category-relevant features induced from different augmented EEG views. We seamlessly integrate three complementary regularizations into a unified DG framework to jointly improve the generalizability and robustness of the model on unseen subjects. Experimental results on motor imagery (MI) based EEG datasets validate that the proposed FDCL outperforms the available state-of-the-art methods.}, }
@article {pmid37524520, year = {2023}, author = {Sawyer, A and Cooke, L and Ramsey, NF and Putrino, D}, title = {The digital motor output: a conceptual framework for a meaningful clinical performance metric for a motor neuroprosthesis.}, journal = {Journal of neurointerventional surgery}, volume = {}, number = {}, pages = {}, doi = {10.1136/jnis-2023-020316}, pmid = {37524520}, issn = {1759-8486}, abstract = {In recent years, the majority of the population has become increasingly reliant on continuous and independent control of smart devices to conduct activities of daily living. Upper extremity movement is typically required to generate the motor outputs that control these interfaces, such as rapidly and accurately navigating and clicking a mouse, or activating a touch screen. For people living with tetraplegia, these abilities are lost, significantly compromising their ability to interact with their environment. Implantable brain computer interfaces (BCIs) hold promise for restoring lost neurologic function, including motor neuroprostheses (MNPs). An implantable MNP can directly infer motor intent by detecting brain signals and transmitting the motor signal out of the brain to generate a motor output and subsequently control computer actions. This physiological function is typically performed by the motor neurons in the human body. To evaluate the use of these implanted technologies, there is a need for an objective measurement of the effectiveness of MNPs in restoring motor outputs. Here, we propose the concept of digital motor outputs (DMOs) to address this: a motor output decoded directly from a neural recording during an attempted limb or orofacial movement is transformed into a command that controls an electronic device. Digital motor outputs are diverse and can be categorized as discrete or continuous representations of motor control, and the clinical utility of the control of a single, discrete DMO has been reported in multiple studies. This sets the stage for the DMO to emerge as a quantitative measure of MNP performance.}, }
@article {pmid37524073, year = {2023}, author = {Sadras, N and Sani, OG and Ahmadipour, P and Shanechi, MM}, title = {Post-stimulus encoding of decision confidence in EEG: toward a brain-computer interface for decision making.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/acec14}, pmid = {37524073}, issn = {1741-2552}, abstract = {OBJECTIVE: When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain-computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI.<br><br>APPROACH: We investigate the neural correlates of confidence by collecting high-density EEG during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap.<br><br>MAIN RESULTS: We perform event-related potential (ERP) and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-related activity to confound stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high.<br><br>SIGNIFICANCE: Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.}, }
@article {pmid37522623, year = {2023}, author = {Moon, J and Chau, T}, title = {Online Ternary Classification of Covert Speech by Leveraging the Passive Perception of Speech.}, journal = {International journal of neural systems}, volume = {}, number = {}, pages = {2350048}, doi = {10.1142/S012906572350048X}, pmid = {37522623}, issn = {1793-6462}, abstract = {Brain-computer interfaces (BCIs) provide communicative alternatives to those without functional speech. Covert speech (CS)-based BCIs enable communication simply by thinking of words and thus have intuitive appeal. However, an elusive barrier to their clinical translation is the collection of voluminous examples of high-quality CS signals, as iteratively rehearsing words for long durations is mentally fatiguing. Research on CS and speech perception (SP) identifies common spatiotemporal patterns in their respective electroencephalographic (EEG) signals, pointing towards shared encoding mechanisms. The goal of this study was to investigate whether a model that leverages the signal similarities between SP and CS can differentiate speech-related EEG signals online. Ten participants completed a dyadic protocol where in each trial, they listened to a randomly selected word and then subsequently mentally rehearsed the word. In the offline sessions, eight words were presented to participants. For the subsequent online sessions, the two most distinct words (most separable in terms of their EEG signals) were chosen to form a ternary classification problem (two words and rest). The model comprised a functional mapping derived from SP and CS signals of the same speech token (features are extracted via a Riemannian approach). An average ternary online accuracy of 75.3% (60% chance level) was achieved across participants, with individual accuracies as high as 93%. Moreover, we observed that the signal-to-noise ratio (SNR) of CS signals was enhanced by perception-covert modeling according to the level of high-frequency ([Formula: see text]-band) correspondence between CS and SP. These findings may lead to less burdensome data collection for training speech BCIs, which could eventually enhance the rate at which the vocabulary can grow.}, }
@article {pmid37522052, year = {2023}, author = {Li, M and Wu, L and Lin, F and Guo, M and Xu, G}, title = {Dual stimuli interface with logical division using local move stimuli.}, journal = {Cognitive neurodynamics}, volume = {17}, number = {4}, pages = {965-973}, pmid = {37522052}, issn = {1871-4080}, abstract = {Improving information transfer rate is a key to prompt the speed of outputting instructions of the event-related potential-based brain-computer interface. Our previous study designed a dual-stimuli interface that simultaneously presents two types of different stimuli to improve the speed. While, adding more stimuli into this interface makes subject easily affected by "flanker effect" that decreases the accuracy of recognizing intention. To achieve high recognition accuracy with many stimuli, this study proposes a dual stimuli interface based on whole flash and local move (DS-WL) and two rules of stimulus arrangement to induce the brain signals. Twenty subjects participated in the experiment, and their signals are recognized by a back propagation neural network classifier. The local move induces larger and later signals of targets to help discriminate the two kinds of stimuli; the rules reduce the N200 and P300 amplitudes of non-target, which improves accuracy. This study demonstrates that the DS-WL is a useful way to shorten the instruction output cycle and speed up the instructions outputting by local move and rules.}, }
@article {pmid37522042, year = {2023}, author = {Hualiang, L and Xupeng, Y and Yuzhong, L and Tingjun, X and Wei, T and Yali, S and Qiru, W and Chaolin, X and Yu, W and Weilin, L and Long, J}, title = {A novel noninvasive brain-computer interface by imagining isometric force levels.}, journal = {Cognitive neurodynamics}, volume = {17}, number = {4}, pages = {975-983}, pmid = {37522042}, issn = {1871-4080}, abstract = {Physiological circuits differ across increasing isometric force levels during unilateral contraction. Therefore, we first explored the possibility of predicting the force level based on electroencephalogram (EEG) activity recorded during a single trial of unilateral 5% or 40% of maximal isometric voluntary contraction (MVC) in right-hand grip imagination. Nine healthy subjects were involved in this study. The subjects were required to randomly perform 20 trials for each force level while imagining a right-hand grip. We proposed the use of common spatial patterns (CSPs) and coherence between EEG signals as features in a support vector machine for force level prediction. The results showed that the force levels could be predicted through single-trial EEGs while imagining the grip (mean accuracy = 81.4 ± 13.29%). Additionally, we tested the possibility of online control of a ball game using the above paradigm through unilateral grip imagination at different force levels (i.e., 5% of MVC imagination and 40% of MVC imagination for right-hand movement control). Subjects played the ball games effectively by controlling direction with our novel BCI system (n = 9, mean accuracy = 76.67 ± 9.35%). Data analysis validated the use of our BCI system in the online control of a ball game. This information may provide additional commands for the control of robots by users through combinations with other traditional brain-computer interfaces, e.g., different limb imaginations.}, }
@article {pmid37521708, year = {2023}, author = {Sun, C and Mou, C}, title = {Survey on the research direction of EEG-based signal processing.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1203059}, pmid = {37521708}, issn = {1662-4548}, abstract = {Electroencephalography (EEG) is increasingly important in Brain-Computer Interface (BCI) systems due to its portability and simplicity. In this paper, we provide a comprehensive review of research on EEG signal processing techniques since 2021, with a focus on preprocessing, feature extraction, and classification methods. We analyzed 61 research articles retrieved from academic search engines, including CNKI, PubMed, Nature, IEEE Xplore, and Science Direct. For preprocessing, we focus on innovatively proposed preprocessing methods, channel selection, and data augmentation. Data augmentation is classified into conventional methods (sliding windows, segmentation and recombination, and noise injection) and deep learning methods [Generative Adversarial Networks (GAN) and Variation AutoEncoder (VAE)]. We also pay attention to the application of deep learning, and multi-method fusion approaches, including both conventional algorithm fusion and fusion between conventional algorithms and deep learning. Our analysis identifies 35 (57.4%), 18 (29.5%), and 37 (60.7%) studies in the directions of preprocessing, feature extraction, and classification, respectively. We find that preprocessing methods have become widely used in EEG classification (96.7% of reviewed papers) and comparative experiments have been conducted in some studies to validate preprocessing. We also discussed the adoption of channel selection and data augmentation and concluded several mentionable matters about data augmentation. Furthermore, deep learning methods have shown great promise in EEG classification, with Convolutional Neural Networks (CNNs) being the main structure of deep neural networks (92.3% of deep learning papers). We summarize and analyze several innovative neural networks, including CNNs and multi-structure fusion. However, we also identified several problems and limitations of current deep learning techniques in EEG classification, including inappropriate input, low cross-subject accuracy, unbalanced between parameters and time costs, and a lack of interpretability. Finally, we highlight the emerging trend of multi-method fusion approaches (49.2% of reviewed papers) and analyze the data and some examples. We also provide insights into some challenges of multi-method fusion. Our review lays a foundation for future studies to improve EEG classification performance.}, }
@article {pmid37520987, year = {2023}, author = {Lei, Y and Wang, D and Wang, W and Qu, H and Wang, J and Shi, B}, title = {Improving single-hand open/close motor imagery classification by error-related potentials correction.}, journal = {Heliyon}, volume = {9}, number = {8}, pages = {e18452}, pmid = {37520987}, issn = {2405-8440}, abstract = {OBJECTIVE: The ability of a brain-computer interface (BCI) to classify brain activity in electroencephalograms (EEG) during motor imagery (MI) tasks is an important performance indicator. Because the cortical regions that drive the single-handed open and closed tasks overlap, it is difficult to classify the EEG signals during executing both tasks.<br><br>APPROACH: The addition of special EEG features can improve the accuracy of classifying single-hand open and closed tasks. In this work, we designed a hybrid BCI paradigm based on error-related potentials (ErrP) and motor imagery (MI) and proposed a strategy to correct the classification results of MI by using ErrP information. The ErrP and MI features of EEG data from 11 subjects were superimposed.<br><br>MAIN RESULTS: The corrected strategy improved the classification accuracy of single-hand open/close MI tasks from 52.3% to 73.7%, an increase of approximately 21%.<br><br>SIGNIFICANCE: Our hybrid BCI paradigm improves the classification accuracy of single-hand MI by adding ErrP information, which provides a new approach for improving the classification performance of BCI.}, }
@article {pmid37521509, year = {2022}, author = {Martini, M and Kemper, C}, title = {[Cybersecurity of brain-computer interfaces].}, journal = {International cybersecurity law review}, volume = {3}, number = {1}, pages = {191-243}, pmid = {37521509}, issn = {2662-9739}, abstract = {Brain-computer interfaces inspire visions of superhuman powers, enabling users to control protheses and other devices solely with their thoughts. But the rapid development and commercialization of this technology also brings security risks. Attacks on brain-computer interfaces may cause harrowing consequences for users, from eavesdropping on neurological data to manipulating brain activity. At present, data protection law, the regulation of medical devices, and the new rules on the sale of goods with digital elements all govern aspects of cybersecurity. There are, nevertheless, significant gaps. The article analyzes how the legal system currently addresses the risks of cyberattacks on brain-computer interfaces-and how policymakers could address such risks in the future.}, }
@article {pmid37519930, year = {2023}, author = {Dong, Y and Wang, S and Huang, Q and Berg, RW and Li, G and He, J}, title = {Neural Decoding for Intracortical Brain-Computer Interfaces.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {4}, number = {}, pages = {0044}, pmid = {37519930}, issn = {2692-7632}, abstract = {Brain-computer interfaces have revolutionized the field of neuroscience by providing a solution for paralyzed patients to control external devices and improve the quality of daily life. To accurately and stably control effectors, it is important for decoders to recognize an individual's motor intention from neural activity either by noninvasive or intracortical neural recording. Intracortical recording is an invasive way of measuring neural electrical activity with high temporal and spatial resolution. Herein, we review recent developments in neural signal decoding methods for intracortical brain-computer interfaces. These methods have achieved good performance in analyzing neural activity and controlling robots and prostheses in nonhuman primates and humans. For more complex paradigms in motor rehabilitation or other clinical applications, there remains more space for further improvements of decoders.}, }
@article {pmid37519929, year = {2023}, author = {Si, X and He, H and Yu, J and Ming, D}, title = {Cross-Subject Emotion Recognition Brain-Computer Interface Based on fNIRS and DBJNet.}, journal = {Cyborg and bionic systems (Washington, D.C.)}, volume = {4}, number = {}, pages = {0045}, pmid = {37519929}, issn = {2692-7632}, abstract = {Functional near-infrared spectroscopy (fNIRS) is a noninvasive brain imaging technique that has gradually been applied in emotion recognition research due to its advantages of high spatial resolution, real time, and convenience. However, the current research on emotion recognition based on fNIRS is mainly limited to within-subject, and there is a lack of related work on emotion recognition across subjects. Therefore, in this paper, we designed an emotion evoking experiment with videos as stimuli and constructed the fNIRS emotion recognition database. On this basis, deep learning technology was introduced for the first time, and a dual-branch joint network (DBJNet) was constructed, creating the ability to generalize the model to new participants. The decoding performance obtained by the proposed model shows that fNIRS can effectively distinguish positive versus neutral versus negative emotions (accuracy is 74.8%, F1 score is 72.9%), and the decoding performance on the 2-category emotion recognition task of distinguishing positive versus neutral (accuracy is 89.5%, F1 score is 88.3%), negative versus neutral (accuracy is 91.7%, F1 score is 91.1%) proved fNIRS has a powerful ability to decode emotions. Furthermore, the results of the ablation study of the model structure demonstrate that the joint convolutional neural network branch and the statistical branch achieve the highest decoding performance. The work in this paper is expected to facilitate the development of fNIRS affective brain-computer interface.}, }
@article {pmid37519868, year = {2023}, author = {Rouzitalab, A and Boulay, CB and Park, J and Sachs, AJ}, title = {Intracortical brain-computer interfaces in primates: a review and outlook.}, journal = {Biomedical engineering letters}, volume = {13}, number = {3}, pages = {375-390}, pmid = {37519868}, issn = {2093-985X}, abstract = {Brain-computer interfaces (BCI) translate brain signals into artificial output to restore or replace natural central nervous system (CNS) functions. Multiple processes, including sensorimotor integration, decision-making, motor planning, execution, and updating, are involved in any movement. For example, a BCI may be better able to restore naturalistic motor behaviors if it uses signals from multiple brain areas and decodes natural behaviors' cognitive and motor aspects. This review provides an overview of the preliminary information necessary to plan a BCI project focusing on intracortical implants in primates. Since the brain structure and areas of non-human primates (NHP) are similar to humans, exploring the result of NHP studies will eventually benefit human BCI studies. The different types of BCI systems based on the target cortical area, types of signals, and decoding methods will be discussed. In addition, various successful state-of-the-art cases will be reviewed in more detail, focusing on the general algorithm followed in the real-time system. Finally, an outlook for improving the current BCI research studies will be debated.}, }
@article {pmid37518828, year = {2023}, author = {Trotier, A and Bagnoli, E and Walski, T and Evers, J and Pugliese, E and Lowery, M and Kilcoyne, M and Fitzgerald, U and Biggs, M}, title = {Micromotion Derived Fluid Shear Stress Mediates Peri-Electrode Gliosis through Mechanosensitive Ion Channels.}, journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)}, volume = {}, number = {}, pages = {e2301352}, doi = {10.1002/advs.202301352}, pmid = {37518828}, issn = {2198-3844}, support = {/SFI_/Science Foundation Ireland/Ireland ; }, abstract = {The development of bioelectronic neural implant technologies has advanced significantly over the past 5 years, particularly in brain-machine interfaces and electronic medicine. However, neuroelectrode-based therapies require invasive neurosurgery and can subject neural tissues to micromotion-induced mechanical shear, leading to chronic inflammation, the formation of a peri-electrode void and the deposition of reactive glial scar tissue. These structures act as physical barriers, hindering electrical signal propagation and reducing neural implant functionality. Although well documented, the mechanisms behind the initiation and progression of these processes are poorly understood. Herein, in silico analysis of micromotion-induced peri-electrode void progression and gliosis is described. Subsequently, ventral mesencephalic cells exposed to milliscale fluid shear stress in vitro exhibited increased expression of gliosis-associated proteins and overexpression of mechanosensitive ion channels PIEZO1 (piezo-type mechanosensitive ion channel component 1) and TRPA1 (transient receptor potential ankyrin 1), effects further confirmed in vivo in a rat model of peri-electrode gliosis. Furthermore, in vitro analysis indicates that chemical inhibition/activation of PIEZO1 affects fluid shear stress mediated astrocyte reactivity in a mitochondrial-dependent manner. Together, the results suggest that mechanosensitive ion channels play a major role in the development of a peri-electrode void and micromotion-induced glial scarring at the peri-electrode region.}, }
@article {pmid37517788, year = {2023}, author = {Shi, X and Li, B and Wang, W and Qin, Y and Wang, H and Wang, X}, title = {Classification algorithm for EEG-based motor imagery using hybrid neural network with spatio-temporal convolution and multi-head attention mechanism.}, journal = {Neuroscience}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuroscience.2023.07.020}, pmid = {37517788}, issn = {1873-7544}, abstract = {Motor imagery (MI) is a brain-computer interface (BCI) technique in which specific brain regions are activated when people imagine their limbs (or muscles) moving, even without actual movement. The technology converts electroencephalogram (EEG) signals generated by the brain into computer-readable commands by measuring neural activity. Classification of motor imagery is one of the tasks in BCI. Researchers have done a lot of work on motor imagery classification, and the existing literature has relatively mature decoding methods for two-class motor tasks. However, as the categories of EEG-based motor imagery tasks increase, further exploration is needed for decoding research on four-class motor imagery tasks. In this study, we designed a hybrid neural network that combines spatiotemporal convolution and attention mechanisms. Specifically, the data is first processed by spatiotemporal convolution to extract features and then processed by a Multi-branch Convolution block. Finally, the processed data is input into the encoder layer of the Transformer for a self-attention calculation to obtain the classification results. Our approach was tested on the well-known MI datasets BCI Competition IV 2a and 2b, and the results show that the 2a dataset has a global average classification accuracy of 83.3% and a kappa value of 0.78. Experimental results show that the proposed method outperforms most of the existing methods.}, }
@article {pmid37515875, year = {2023}, author = {Chen, F and Wang, J and Liu, H and Kong, W and Zhao, Z and Ma, L and Liao, H and Zhang, D}, title = {Frequency constraint-based adversarial attack on deep neural networks for medical image classification.}, journal = {Computers in biology and medicine}, volume = {164}, number = {}, pages = {107248}, doi = {10.1016/j.compbiomed.2023.107248}, pmid = {37515875}, issn = {1879-0534}, abstract = {The security of AI systems has gained significant attention in recent years, particularly in the medical diagnosis field. To develop a secure medical image classification system based on deep neural networks, it is crucial to design effective adversarial attacks that can embed hidden, malicious behaviors into the system. However, designing a unified attack method that can generate imperceptible attack samples with high content similarity and be applied to diverse medical image classification systems is challenging due to the diversity of medical imaging modalities and dimensionalities. Most existing attack methods are designed to attack natural image classification models, which inevitably corrupt the semantics of pixels by applying spatial perturbations. To address this issue, we propose a novel frequency constraint-based adversarial attack method capable of delivering attacks in various medical image classification tasks. Specially, our method introduces a frequency constraint to inject perturbation into high-frequency information while preserving low-frequency information to ensure content similarity. Our experiments include four public medical image datasets, including a 3D CT dataset, a 2D chest X-Ray image dataset, a 2D breast ultrasound dataset, and a 2D thyroid ultrasound dataset, which contain different imaging modalities and dimensionalities. The results demonstrate the superior performance of our model over other state-of-the-art adversarial attack methods for attacking medical image classification tasks on different imaging modalities and dimensionalities.}, }
@article {pmid37515595, year = {2023}, author = {Cai, R and Liu, Y and Wang, X and Wei, H and Wang, J and Cao, Y and Lei, J and Li, D}, title = {Influences of standardized clinical probing on peri-implant soft tissue seal in a situation of peri-implant mucositis: A histomorphometric study in dogs.}, journal = {Journal of periodontology}, volume = {}, number = {}, pages = {}, doi = {10.1002/JPER.23-0167}, pmid = {37515595}, issn = {1943-3670}, abstract = {BACKGROUND: Clinical probing is commonly recommended to evaluate peri-implant conditions. In a situation of peri-implant mucositis or peri-implantitis, the peri-implant seal healing from the disruption of soft tissue caused by probing has not yet been studied. This study aimed to investigate soft tissue healing after standardized clinical probing around osseointegrated implants with peri-implant mucositis in a dog model.<br><br>METHODS: Three transmucosal implants in each hemi-mandible of 6 dogs randomly assigned to the peri-implant healthy group or peri-implant mucositis group were probed randomly in the mesial or distal site as probing groups (PH or PM), the cross-sectional opposite sites as unprobed control groups. Histomorphometric measurements of implant shoulder (IS)-alveolar bone contact to the implant surface (BCI), apical termination of the junctional epithelium (aJE)-BCI, mucosal margin (MM)-BCI, and MM-aJE were performed at 1 day, 1 week and 2 weeks after probing. Apoptosis, proliferation, proinflammatory cytokines, and matrix metalloproteinases (MMPs) of peri-implant soft tissue were estimated by immunofluorescent analysis.<br><br>RESULTS: In PM group, apical migration of junctional epithelium was revealed by significantly decreased aJE-BCI from 1 day to 2 weeks in comparison to unprobed sites (P<0.05), while no significant differences were found in PH group. Immunofluorescent analysis showed higher levels of interleukin 1&#x3b2; (IL-1&#x3b2;), IL-6, tumor necrosis factor &#x3b1; (TNF-&#x3b1;), MMP-1, and MMP-8, together with exaggerated apoptosis and proliferation of peri-implant soft tissue in PM group.<br><br>CONCLUSION: Within the limitations, standardized clinical probing might lead to apical migration of the junctional epithelium in a situation of peri-implant mucositis. This article is protected by copyright. All rights reserved.}, }
@article {pmid37514728, year = {2023}, author = {Chaddad, A and Wu, Y and Kateb, R and Bouridane, A}, title = {Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {14}, pages = {}, pmid = {37514728}, issn = {1424-8220}, mesh = {*Signal Processing, Computer-Assisted ; Sleep ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Databases, Factual ; Algorithms ; }, abstract = {The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain-computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisition and pretreatment (denoising) to feature extraction, classification, and application. We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing. Additionally, we identify the current limitations of these techniques and analyze their future development trends. We conclude by offering some suggestions for future research in the field of EEG signal processing.}, }
@article {pmid37514603, year = {2023}, author = {Liang, L and Zhang, Q and Zhou, J and Li, W and Gao, X}, title = {Dataset Evaluation Method and Application for Performance Testing of SSVEP-BCI Decoding Algorithm.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {14}, pages = {}, pmid = {37514603}, issn = {1424-8220}, support = {No. 2021YFF0601801//China Academy of Information and Communications Technology/ ; }, mesh = {Humans ; *Evoked Potentials, Visual ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Photic Stimulation ; Algorithms ; }, abstract = {Steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems have been extensively researched over the past two decades, and multiple sets of standard datasets have been published and widely used. However, there are differences in sample distribution and collection equipment across different datasets, and there is a lack of a unified evaluation method. Most new SSVEP decoding algorithms are tested based on self-collected data or offline performance verification using one or two previous datasets, which can lead to performance differences when used in actual application scenarios. To address these issues, this paper proposed a SSVEP dataset evaluation method and analyzed six datasets with frequency and phase modulation paradigms to form an SSVEP algorithm evaluation dataset system. Finally, based on the above datasets, performance tests were carried out on the four existing SSVEP decoding algorithms. The findings reveal that the performance of the same algorithm varies significantly when tested on diverse datasets. Substantial performance variations were observed among subjects, ranging from the best-performing to the worst-performing. The above results demonstrate that the SSVEP dataset evaluation method can integrate six datasets to form a SSVEP algorithm performance testing dataset system. This system can test and verify the SSVEP decoding algorithm from different perspectives such as different subjects, different environments, and different equipment, which is helpful for the research of new SSVEP decoding algorithms and has significant reference value for other BCI application fields.}, }
@article {pmid37514082, year = {2023}, author = {Bourdin, A and Ortoli, M and Karadayi, R and Przegralek, L and Sennlaub, F and Bodaghi, B and Guillonneau, X and Carpentier, A and Touhami, S}, title = {Efficacy and Safety of Low-Intensity Pulsed Ultrasound-Induced Blood-Retinal Barrier Opening in Mice.}, journal = {Pharmaceutics}, volume = {15}, number = {7}, pages = {}, pmid = {37514082}, issn = {1999-4923}, support = {ANR-18-IAHU-01//IHU FOReSIGHT/ ; none//ASTRL/ ; }, abstract = {Systemic drugs can treat various retinal pathologies such as retinal cancers; however, their ocular diffusion may be limited by the blood-retina barrier (BRB). Sonication corresponds to the use of ultrasound (US) to increase the permeability of cell barriers including in the BRB. The objective was to study the efficacy and safety of sonication using microbubble-assisted low-intensity pulsed US in inducing a transient opening of the BRB. The eyes of C57/BL6J mice were sonicated at different acoustic pressures (0.10 to 0.50 MPa). Efficacy analyses consisted of fluorescein angiography (FA) performed at different timepoints and the size of the leaked molecules was assessed using FITC-marked dextrans. Tolerance was assessed by fundus photographs, optical coherence tomography, immunohistochemistry, RT-qPCR, and electroretinograms. Sonication at 0.15 MPa was the most suitable pressure for transient BRB permeabilization without altering the morphology or function of the retina. It did not increase the expression of inflammation or apoptosis markers in the retina, retinal pigment epithelium, or choroid. The dextran assay suggested that drugs up to 150 kDa in size can cross the BRB. Microbubble-assisted sonication at an optimized acoustic pressure of 0.15 MPa provides a non-invasive method to transiently open the BRB, increasing the retinal diffusion of systemic drugs without inducing any noticeable side-effect.}, }
@article {pmid37509941, year = {2023}, author = {Zhu, JY and Li, MM and Zhang, ZH and Liu, G and Wan, H}, title = {Performance Baseline of Phase Transfer Entropy Methods for Detecting Animal Brain Area Interactions.}, journal = {Entropy (Basel, Switzerland)}, volume = {25}, number = {7}, pages = {}, pmid = {37509941}, issn = {1099-4300}, support = {61673353//National Natural Science Foundation of China/ ; }, abstract = {Objective: Phase transfer entropy (TEθ) methods perform well in animal sensory-spatial associative learning. However, their advantages and disadvantages remain unclear, constraining their usage. Method: This paper proposes the performance baseline of the TEθ methods. Specifically, four TEθ methods are applied to the simulated signals generated by a neural mass model and the actual neural data from ferrets with known interaction properties to investigate the accuracy, stability, and computational complexity of the TEθ methods in identifying the directional coupling. Then, the most suitable method is selected based on the performance baseline and used on the local field potential recorded from pigeons to detect the interaction between the hippocampus (Hp) and nidopallium caudolaterale (NCL) in visual-spatial associative learning. Results: (1) This paper obtains a performance baseline table that contains the most suitable method for different scenarios. (2) The TEθ method identifies an information flow preferentially from Hp to NCL of pigeons at the θ band (4-12 Hz) in visual-spatial associative learning. Significance: These outcomes provide a reference for the TEθ methods in detecting the interactions between brain areas.}, }
@article {pmid37509039, year = {2023}, author = {Dong, Y and Wen, X and Gao, F and Gao, C and Cao, R and Xiang, J and Cao, R}, title = {Subject-Independent EEG Classification of Motor Imagery Based on Dual-Branch Feature Fusion.}, journal = {Brain sciences}, volume = {13}, number = {7}, pages = {}, pmid = {37509039}, issn = {2076-3425}, support = {62206196//the National Natural Science Foundation of China/ ; 202103021223035//the Natural Science Foundation of Shanxi/ ; }, abstract = {A brain computer interface (BCI) system helps people with motor dysfunction interact with the external environment. With the advancement of technology, BCI systems have been applied in practice, but their practicability and usability are still greatly challenged. A large amount of calibration time is often required before BCI systems are used, which can consume the patient's energy and easily lead to anxiety. This paper proposes a novel motion-assisted method based on a novel dual-branch multiscale auto encoder network (MSAENet) to decode human brain motion imagery intentions, while introducing a central loss function to compensate for the shortcomings of traditional classifiers that only consider inter-class differences and ignore intra-class coupling. The effectiveness of the method is validated on three datasets, namely BCIIV2a, SMR-BCI and OpenBMI, to achieve zero calibration of the MI-BCI system. The results show that our proposed network displays good results on all three datasets. In the case of subject-independence, the MSAENet outperformed the other four comparison methods on the BCIIV2a and SMR-BCI datasets, while achieving F1_score values as high as 69.34% on the OpenBMI dataset. Our method maintains better classification accuracy with a small number of parameters and short prediction times, and the method achieves zero calibration of the MI-BCI system.}, }
@article {pmid37508946, year = {2023}, author = {Seifpour, S and Šatka, A}, title = {Tensor Decomposition Analysis of Longitudinal EEG Signals Reveals Differential Oscillatory Dynamics in Eyes-Closed and Eyes-Open Motor Imagery BCI: A Case Report.}, journal = {Brain sciences}, volume = {13}, number = {7}, pages = {}, pmid = {37508946}, issn = {2076-3425}, support = {2/0023/22//Ministry of Education, Science, Research, and Sport of the Slovak Republic (VEGA)/ ; }, abstract = {Functional dissociation of brain neural activity induced by opening or closing the eyes has been well established. However, how the temporal dynamics of the underlying neuronal modulations differ between these eye conditions during movement-related behaviours is less known. Using a robotic-assisted motor imagery brain-computer interface (MI BCI), we measured neural activity over the motor regions with electroencephalography (EEG) in a stroke survivor during his longitudinal rehabilitation training. We investigated lateralized oscillatory sensorimotor rhythm modulations while the patient imagined moving his hemiplegic hand with closed and open eyes to control an external robotic splint. In order to precisely identify the main profiles of neural activation affected by MI with eyes-open (MIEO) and eyes-closed (MIEC), a data-driven approach based on parallel factor analysis (PARAFAC) tensor decomposition was employed. Using the proposed framework, a set of narrow-band, subject-specific sensorimotor rhythms was identified; each of them had its own spatial and time signature. When MIEC trials were compared with MIEO trials, three key narrow-band rhythms whose peak frequencies centred at ∼8.0 Hz, ∼11.5 Hz, and ∼15.5 Hz, were identified with differently modulated oscillatory dynamics during movement preparation, initiation, and completion time frames. Furthermore, we observed that lower and higher sensorimotor oscillations represent different functional mechanisms within the MI paradigm, reinforcing the hypothesis that rhythmic activity in the human sensorimotor system is dissociated. Leveraging PARAFAC, this study achieves remarkable precision in estimating latent sensorimotor neural substrates, aiding the investigation of the specific functional mechanisms involved in the MI process.}, }
@article {pmid37508828, year = {2023}, author = {Li, M and Qi, Y and Pan, G}, title = {Optimal Feature Analysis for Identification Based on Intracranial Brain Signals with Machine Learning Algorithms.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {10}, number = {7}, pages = {}, pmid = {37508828}, issn = {2306-5354}, support = {2021ZD0200400//China Brain Project/ ; U1909202 and 61925603//National Natural Science Foundation of China/ ; 2020C03004//Key Research and Development Program of Zhejiang Province/ ; }, abstract = {Biometrics, e.g., fingerprints, the iris, and the face, have been widely used to authenticate individuals. However, most biometrics are not cancellable, i.e., once these traditional biometrics are cloned or stolen, they cannot be replaced easily. Unlike traditional biometrics, brain biometrics are extremely difficult to clone or forge due to the natural randomness across different individuals, which makes them an ideal option for identity authentication. Most existing brain biometrics are based on an electroencephalogram (EEG), which typically demonstrates unstable performance due to the low signal-to-noise ratio (SNR). Thus, in this paper, we propose the use of intracortical brain signals, which have higher resolution and SNR, to realize the construction of a high-performance brain biometric. Significantly, this is the first study to investigate the features of intracortical brain signals for identification. Specifically, several features based on local field potential are computed for identification, and their performance is compared with different machine learning algorithms. The results show that frequency domain features and time-frequency domain features are excellent for intra-day and inter-day identification. Furthermore, the energy features perform best among all features with 98% intra-day and 93% inter-day identification accuracy, which demonstrates the great potential of intracraial brain signals to be biometrics. This paper may serve as a guidance for future intracranial brain researches and the development of more reliable and high-performance brain biometrics.}, }
@article {pmid37508789, year = {2023}, author = {Mathon, B and Duarte Rocha, V and Py, JB and Falcan, A and Bergeret, T}, title = {An Air-Filled Bicycle Helmet for Mitigating Traumatic Brain Injury.}, journal = {Bioengineering (Basel, Switzerland)}, volume = {10}, number = {7}, pages = {}, pmid = {37508789}, issn = {2306-5354}, support = {X//ALLIANZ FRANCE/ ; }, abstract = {We created a novel air-filled bicycle helmet. The aims of this study were (i) to assess the head injury mitigation performance of the proposed helmet and (ii) to compare those performance results against the performance results of an expanded polystyrene (EPS) traditional bicycle helmet. Two bicycle helmet types were subjected to impacts in guided vertical drop tests onto a flat anvil: EPS helmets and air-filled helmets (Bumpair). The maximum acceleration value recorded during the test on the Bumpair helmet was 86.76 ± 3.06 g, while the acceleration during the first shock on the traditional helmets reached 207.85 ± 5.55 g (p < 0.001). For the traditional helmets, the acceleration increased steadily over the number of shocks. There was a strong correlation between the number of impacts and the response of the traditional helmet (cor = 0.94; p < 0.001), while the Bumpair helmets showed a less significant dependence over time (cor = 0.36; p = 0.048), meaning previous impacts had a lower consequence. The air-filled helmet significantly reduced the maximal linear acceleration when compared to an EPS traditional helmet, showing improvements in impact energy mitigation, as well as in resistance to repeated impacts. This novel helmet concept could improve head injury mitigation in cyclists.}, }
@article {pmid37507031, year = {2023}, author = {Song, S and Druschel, LN and Chan, ER and Capadona, JR}, title = {Differential expression of genes involved in the chronic response to intracortical microelectrodes.}, journal = {Acta biomaterialia}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.actbio.2023.07.038}, pmid = {37507031}, issn = {1878-7568}, abstract = {Brain-Machine Interface systems (BMIs) are clinically valuable devices that can provide functional restoration for patients with spinal cord injury or improved integration for patients requiring prostheses. Intracortical microelectrodes can record neuronal action potentials at a resolution necessary for precisely controlling BMIs. However, intracortical microelectrodes have a demonstrated history of progressive decline in the recording performance with time, inhibiting their usefulness. One major contributor to decreased performance is the neuroinflammatory response to the implanted microelectrodes. The neuroinflammatory response can lead to neurodegeneration and the formation of a glial scar at the implant site. Historically, histological imaging of relatively few known cellular and protein markers has characterized the neuroinflammatory response to implanted microelectrode arrays. However, neuroinflammation requires many molecular players to coordinate the response - meaning traditional methods could result in an incomplete understanding. Taking advantage of recent advancements in tools to characterize the relative or absolute DNA/RNA expression levels, a few groups have begun to explore gene expression at the microelectrode-tissue interface using. We have utilized a custom panel of ∼813 neuroinflammatory-specific genes developed with NanoString for bulk tissue analysis at the microelectrode-tissue interface. Our previous studies characterized the acute innate immune response to intracortical microelectrodes. Here we investigated the gene expression at the microelectrode-tissue interface in wild-type (WT) mice chronically implanted with nonfunctioning probes (4WK, 8WK, and 16WK). We found 28 differentially expressed genes at chronic time points, many in the complement and extracellular matrix system. Further, the expression levels were relatively stable over time. Genes identified here represent chronic molecular players at the microelectrode implant sites and potential therapeutic targets for the long-term integration of microelectrodes. STATEMENT OF SIGNIFICANCE: Intracortical microelectrodes can record neuronal action potentials at a resolution necessary for the precise control of Brain-Machine Interface systems (BMIs). However, intracortical microelectrodes have a demonstrated history of progressive declines in the recording performance with time, inhibiting the usefulness. One major contributor to decline in these devices is the neuroinflammatory response against the implanted microelectrodes. Historically, neuroinflammation to implanted microelectrode arrays has been characterized with histological imaging of relatively few known cellular and protein markers. Few studies have begun to develop a more in depth understanding of the molecular pathways facilitating device-mediated neuroinflammation. Here, we are among the first to identify genetic pathways that could represent targets to improve the host-response to intracortical microelectrodes, and ultimately device performance.}, }
@article {pmid37506011, year = {2023}, author = {Altindis, F and Banerjee, A and Phlypo, R and Yilmaz, B and Congedo, M}, title = {Transfer Learning for P300 Brain-Computer Interfaces by Joint Alignment of Feature Vectors.}, journal = {IEEE journal of biomedical and health informatics}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/JBHI.2023.3299837}, pmid = {37506011}, issn = {2168-2208}, abstract = {This paper presents a new transfer learning method named group learning, that jointly aligns multiple domains (many-to-many) and an extension named fast alignment that aligns any further domain to previously aligned group of domains (many-to-one). The proposed group alignment algorithm (GALIA) is evaluated on brain-computer interface (BCI) data and optimal hyper-parameter values of the algorithm are studied for classification performance and computational cost. Six publicly available P300 databases comprising 333 sessions from 177 subjects are used. As compared to the conventional subject-specific train/test pipeline, both group learning and fast alignment significantly improve the classification accuracy except for the database with clinical subjects (average improvement: 2.12±1.88%). GALIA utilizes cyclic approximate joint diagonalization (AJD) to find a set of linear transformations, one for each domain, jointly aligning the feature vectors of all domains. Group learning achieves a many-to-many transfer learning without compromising the classification performance on non-clinical BCI data. Fast alignment further extends the group learning for any unseen domains, allowing a many-to-one transfer learning with the same properties. The former method creates a single machine learning model using data from previous subjects and/or sessions, whereas the latter exploits the trained model for an unseen domain requiring no further training of the classifier.}, }
@article {pmid37506000, year = {2023}, author = {Wang, W and Qi, F and Wipf, D and Cai, C and Yu, T and Li, Y and Yu, Z and Wu, W}, title = {Sparse Bayesian Learning for End-to-End EEG Decoding.}, journal = {IEEE transactions on pattern analysis and machine intelligence}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TPAMI.2023.3299568}, pmid = {37506000}, issn = {1939-3539}, abstract = {Decoding brain activity from non-invasive electroencephalography (EEG) is crucial for brain-computer interfaces (BCIs) and the study of brain disorders. Notably, end-to-end EEG decoding has gained widespread popularity in recent years owing to the remarkable advances in deep learning research. However, many EEG studies suffer from limited sample sizes, making it difficult for existing deep learning models to effectively generalize to highly noisy EEG data. To address this fundamental limitation, this paper proposes a novel end-to-end EEG decoding algorithm that utilizes a low-rank weight matrix to encode both spatio-temporal filters and the classifier, all optimized under a principled sparse Bayesian learning (SBL) framework. Importantly, this SBL framework also enables us to learn hyperparameters that optimally penalize the model in a Bayesian fashion. The proposed decoding algorithm is systematically benchmarked on five motor imagery BCI EEG datasets (N=192) and an emotion recognition EEG dataset (N=45), in comparison with several contemporary algorithms, including end-to-end deep-learning-based EEG decoding algorithms. The classification results demonstrate that our algorithm significantly outperforms the competing algorithms while yielding neurophysiologically meaningful spatio-temporal patterns. Our algorithm therefore advances the state-of-the-art by providing a novel EEG-tailored machine learning tool for decoding brain activity.Code is available at https://github.com/EEGdecoding/Code-SBLEST.}, }
@article {pmid37502681, year = {2023}, author = {Chen, D and Huang, H and Bao, X and Pan, J and Li, Y}, title = {An EEG-based attention recognition method: fusion of time domain, frequency domain, and non-linear dynamics features.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1194554}, pmid = {37502681}, issn = {1662-4548}, abstract = {INTRODUCTION: Attention is a complex cognitive function of human brain that plays a vital role in our daily lives. Electroencephalogram (EEG) is used to measure and analyze attention due to its high temporal resolution. Although several attention recognition brain-computer interfaces (BCIs) have been proposed, there is a scarcity of studies with a sufficient number of subjects, valid paradigms, and reliable recognition analysis across subjects.<br><br>METHODS: In this study, we proposed a novel attention paradigm and feature fusion method to extract features, which fused time domain features, frequency domain features and nonlinear dynamics features. We then constructed an attention recognition framework for 85 subjects.<br><br>RESULTS AND DISCUSSION: We achieved an intra-subject average classification accuracy of 85.05% &#xb1; 6.87% and an inter-subject average classification accuracy of 81.60% &#xb1; 9.93%, respectively. We further explored the neural patterns in attention recognition, where attention states showed less activation than non-attention states in the prefrontal and occipital areas in &#x3b1;, &#x3b2; and &#x3b8; bands. The research explores, for the first time, the fusion of time domain features, frequency domain features and nonlinear dynamics features for attention recognition, providing a new understanding of attention recognition.}, }
@article {pmid37501450, year = {2023}, author = {Ding, X and Yang, L and Li, C}, title = {Study of MI-BCI classification method based on the Riemannian transform of personalized EEG spatiotemporal features.}, journal = {Mathematical biosciences and engineering : MBE}, volume = {20}, number = {7}, pages = {12454-12471}, doi = {10.3934/mbe.2023554}, pmid = {37501450}, issn = {1551-0018}, abstract = {Motor imagery (MI) is a traditional paradigm of brain-computer interface (BCI) and can assist users in creating direct connections between their brains and external equipment. The common spatial patterns algorithm is the most popular spatial filtering technique for collecting EEG signal features in MI-based BCI systems. Due to the defect that it only considers the spatial information of EEG signals and is susceptible to noise interference and other issues, its performance is diminished. In this study, we developed a Riemannian transform feature extraction method based on filter bank fusion with a combination of multiple time windows. First, we proposed the multi-time window data segmentation and recombination method by combining it with a filter group to create new data samples. This approach could capture individual differences due to the variation in time-frequency patterns across different participants, thereby improving the model's generalization performance. Second, Riemannian geometry was used for feature extraction from non-Euclidean structured EEG data. Then, considering the non-Gaussian distribution of EEG signals, the neighborhood component analysis (NCA) algorithm was chosen for feature selection. Finally, to meet real-time requirements and a low complexity, we employed a Support Vector Machine (SVM) as the classification algorithm. The proposed model achieved improved accuracy and robustness. In this study, we proposed an algorithm with superior performance on the BCI Competition IV dataset 2a, achieving an accuracy of 89%, a kappa value of 0.73 and an AUC of 0.9, demonstrating advanced capabilities. Furthermore, we analyzed data collected in our laboratory, and the proposed method achieved an accuracy of 77.4%, surpassing other comparative models. This method not only significantly improved the classification accuracy of motor imagery EEG signals but also bore significant implications for applications in the fields of brain-computer interfaces and neural engineering.}, }
@article {pmid37499661, year = {2023}, author = {Du, Y and Zhou, S and Ma, C and Chen, H and Du, A and Deng, G and Liu, Y and Tose, AJ and Sun, L and Liu, Y and Wu, H and Lou, H and Yu, YQ and Zhao, T and Lammel, S and Duan, S and Yang, H}, title = {Dopamine release and negative valence gated by inhibitory neurons in the laterodorsal tegmental nucleus.}, journal = {Neuron}, volume = {}, number = {}, pages = {}, doi = {10.1016/j.neuron.2023.06.021}, pmid = {37499661}, issn = {1097-4199}, abstract = {GABAergic neurons in the laterodorsal tegmental nucleus (LDT[GABA]) encode aversion by directly inhibiting mesolimbic dopamine (DA). Yet, the detailed cellular and circuit mechanisms by which these cells relay unpleasant stimuli to DA neurons and regulate behavioral output remain largely unclear. Here, we show that LDT[GABA] neurons bidirectionally respond to rewarding and aversive stimuli in mice. Activation of LDT[GABA] neurons promotes aversion and reduces DA release in the lateral nucleus accumbens. Furthermore, we identified two molecularly distinct LDT[GABA] cell populations. Somatostatin-expressing (Sst[+]) LDT[GABA] neurons indirectly regulate the mesolimbic DA system by disinhibiting excitatory hypothalamic neurons. In contrast, Reelin-expressing LDT[GABA] neurons directly inhibit downstream DA neurons. The identification of separate GABAergic subpopulations in a single brainstem nucleus that relay unpleasant stimuli to the mesolimbic DA system through direct and indirect projections is critical for establishing a circuit-level understanding of how negative valence is encoded in the mammalian brain.}, }
@article {pmid37499295, year = {2023}, author = {Luo, J and Wang, Y and Xia, S and Lu, N and Ren, X and Shi, Z and Hei, X}, title = {A shallow mirror transformer for subject-independent motor imagery BCI.}, journal = {Computers in biology and medicine}, volume = {164}, number = {}, pages = {107254}, doi = {10.1016/j.compbiomed.2023.107254}, pmid = {37499295}, issn = {1879-0534}, abstract = {OBJECTIVE: Motor imagery BCI plays an increasingly important role in motor disorders rehabilitation. However, the position and duration of the discriminative segment in an EEG trial vary from subject to subject and even trial to trial, and this leads to poor performance of subject-independent motor imagery classification. Thus, determining how to detect and utilize the discriminative signal segments is crucial for improving the performance of subject-independent motor imagery BCI.<br><br>APPROACH: In this paper, a shallow mirror transformer is proposed for subject-independent motor imagery EEG classification. Specifically, a multihead self-attention layer with a global receptive field is employed to detect and utilize the discriminative segment from the entire input EEG trial. Furthermore, the mirror EEG signal and the mirror network structure are constructed to improve the classification precision based on ensemble learning. Finally, the subject-independent setup was used to evaluate the shallow mirror transformer on motor imagery EEG signals from subjects existing in the training set and new subjects.<br><br>MAIN RESULTS: The experiments results on BCI Competition IV datasets 2a and 2b and the OpenBMI dataset demonstrated the promising effectiveness of the proposed shallow mirror transformer. The shallow mirror transformer obtained average accuracies of 74.48% and 76.1% for new subjects and existing subjects, respectively, which were highest among the compared state-of-the-art methods. In addition, visualization of the attention score showed the ability of discriminative EEG segment detection. This paper demonstrated that multihead self-attention is effective in capturing global EEG signal information in motor imagery classification.<br><br>SIGNIFICANCE: This study provides an effective model based on a multihead self-attention layer for subject-independent motor imagery-based BCIs. To the best of our knowledge, this is the shallowest transformer model available, in which a small number of parameters promotes the performance in motor imagery EEG classification for such a small sample problem.}, }
@article {pmid37499267, year = {2023}, author = {Abdou, H and Treffalls, RN and Stonko, DP and Kundi, R and Morrison, JJ}, title = {Endovascular stenting techniques for blunt carotid injury.}, journal = {Vascular}, volume = {}, number = {}, pages = {17085381231193062}, doi = {10.1177/17085381231193062}, pmid = {37499267}, issn = {1708-539X}, abstract = {OBJECTIVES: While methods of endovascular carotid artery stenting have improved over time, concerns surrounding the safety and efficacy of stenting for blunt carotid injury (BCI) remain. This study aims to present our approach to carotid artery stenting (CAS) by incorporating new technologies such as flow-diverting stents and circuits.<br><br>METHODS: There is no robust evidence to support routine carotid artery stenting; however, there are several therapeutic options and approaches for treating BCI that currently require an individualized approach. Endovascular stenting and specific stent selection are largely dictated by the disease process the surgeon intends to treat. We will discuss patient selection, medical management, and the most common revascularization techniques, including transfemoral stenting, trans-carotid arterial revascularization using flow reversal, and stent-assisting coiling.<br><br>RESULTS: It must be stressed that endovascular intervention is not an alternative to or preclusive of antithrombotic or anticoagulant therapy. In the setting of BCI, transfemoral CAS is most appropriate in patients who are symptomatic, have a rapidly progressing or large lesion, and do not have a soft thrombus present due to risk of embolism. Unlike transfemoral CAS, TCAR offers an elegant solution for embolic protection when patients have a soft thrombus present. In the case of a large pseudoaneurysm, we perform stent-assisted coiling.<br><br>CONCLUSIONS: We practice selective endovascular intervention, stenting lesions that are flow-limiting or have large or rapidly expanding pseudoaneurysms, and only in patients for whom anticoagulation and antiplatelet agents are not contraindicated. As technology and investigation progress, the concerns regarding the safety and the role of endovascular intervention in the treatment of BCI will be more clearly defined.}, }
@article {pmid37498754, year = {2023}, author = {Ma, X and Chen, W and Pei, Z and Liu, J and Huang, B and Chen, J}, title = {A Temporal Dependency Learning CNN with Attention Mechanism for MI-EEG Decoding.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3299355}, pmid = {37498754}, issn = {1558-0210}, abstract = {Deep learning methods have been widely explored in motor imagery (MI)-based brain computer interface (BCI) systems to decode electroencephalography (EEG) signals. However, most studies fail to fully explore temporal dependencies among MI-related patterns generated in different stages during MI tasks, resulting in limited MI-EEG decoding performance. Apart from feature extraction, learning temporal dependencies is equally important to develop a subject-specific MI-based BCI because every subject has their own way of performing MI tasks. In this paper, a novel temporal dependency learning convolutional neural network (CNN) with attention mechanism is proposed to address MI-EEG decoding. The network first learns spatial and spectral information from multi-view EEG data via the spatial convolution block. Then, a series of non-overlapped time windows is employed to segment the output data, and the discriminative feature is further extracted from each time window to capture MI-related patterns generated in different stages. Furthermore, to explore temporal dependencies among discriminative features in different time windows, we design a temporal attention module that assigns different weights to features in various time windows and fuses them into more discriminative features. The experimental results on the BCI Competition IV-2a (BCIC-IV-2a) and OpenBMI datasets show that our proposed network outperforms the state-of-the-art algorithms and achieves the average accuracy of 79.48%, improved by 2.30% on the BCIC-IV-2a dataset. We demonstrate that learning temporal dependencies effectively improves MI-EEG decoding performance. The code is available at https://github.com/Ma-Xinzhi/LightConvNet.}, }
@article {pmid37498753, year = {2023}, author = {Zhou, Y and Yu, T and Gao, W and Huang, W and Lu, Z and Huang, Q and Li, Y}, title = {Shared Three-dimensional Robotic Arm Control Based on Asynchronous BCI and Computer Vision.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3299350}, pmid = {37498753}, issn = {1558-0210}, abstract = {OBJECTIVE: A brain-computer interface (BCI) can be used to translate neuronal activity into commands to control external devices. However, using noninvasive BCI to control a robotic arm for movements in three-dimensional (3D) environments and accomplish complicated daily tasks, such as grasping and drinking, remains a challenge.<br><br>APPROACH: In this study, a shared robotic arm control system based on hybrid asynchronous BCI and computer vision was presented. The BCI model, which combines steady-state visual evoked potentials (SSVEPs) and blink-related electrooculography (EOG) signals, allows users to freely choose from fifteen commands in an asynchronous mode corresponding to robot actions in a 3D workspace and reach targets with a wide movement range, while computer vision can identify objects and assist a robotic arm in completing more precise tasks, such as grasping a target automatically.<br><br>RESULTS: Ten subjects participated in the experiments and achieved an average accuracy of more than 92% and a high trajectory efficiency for robot movement. All subjects were able to perform the reach-grasp-drink tasks successfully using the proposed shared control method, with fewer error commands and shorter completion time than with direct BCI control.<br><br>SIGNIFICANCE: Our results demonstrated the feasibility and efficiency of generating practical multidimensional control of an intuitive robotic arm by merging hybrid asynchronous BCI and computer vision-based recognition.}, }
@article {pmid37498009, year = {2023}, author = {van Merode, NAM and Nijholt, IM and Heesakkers, JP and van Koeveringe, GA and Steffens, MG and Witte, LPW}, title = {Effect of bladder outlet procedures on urodynamic assessments in men with an acontractile or underactive detrusor: A systematic review and meta-analysis.}, journal = {Neurourology and urodynamics}, volume = {}, number = {}, pages = {}, doi = {10.1002/nau.25253}, pmid = {37498009}, issn = {1520-6777}, support = {//Isala Science and Innovation Fund Grant/ ; }, abstract = {OBJECTIVE: To review the effect of bladder outlet procedures on urodynamic outcomes and symptom scores in males with detrusor underactivity (DU) or acontractile detrusors (AD).<br><br>MATERIALS AND METHODS: We performed a systematic review and meta-analysis of research publications derived from PubMed, Embase, Web of Science, and Ovid Medline to identify clinical studies of adult men with non-neurogenic DU or AD who underwent any bladder outlet procedure. Outcomes comprised the detrusor pressure at maximum flow (Pdet Qmax), maximum flow rate (Qmax), international prostate symptom score (IPSS), and quality of life (QoL). This study is registered under PROSPERO CRD42020215832.<br><br>RESULTS: We included 13 studies of bladder outlet procedures, of which 6 reported decreased and 7 reported improved Pdet Qmax after the procedure. Meta-analysis revealed an increase in the pooled mean Pdet Qmax of 5.99&#x2009;cmH2 0 after surgery (95%&#xa0;CI:&#xa0;0.59-11.40; p&#x2009;=&#x2009;0.03; I[2] 95%). Notably, the Pdet Qmax improved in all subgroups with a preoperative bladder contractility index (BCI)&#xa0;<50 and decreased in all subgroups with a BCI&#xa0;&#x2265;50. All studies reported an improved Qmax after surgery, with a pooled mean difference of 5.87&#x2009;mL/s (95%&#xa0;CI:&#xa0;4.25-7.49; I[2] 93%). Only three studies reported QoL, but pooling suggested significant improvements after surgery (mean, -2.41 points; 95%&#xa0;CI:&#xa0;-2.81 to -2.01; p&#x2009;=&#x2009;0.007). All seven studies reporting IPSS demonstrated improvement (mean, -12.82; 95%&#xa0;CI:&#xa0;-14.76 to -10.88; p&#x2009;<&#x2009;0.001).<br><br>CONCLUSIONS: This review shows that Pdet Qmax and Qmax increases after surgical bladder outlet procedures in men with DU&#xa0;and AD. Bladder outlet procedures should be discussed as part of the shared decision-making process for this group. The evidence was of low to very low certainty.}, }
@article {pmid37497042, year = {2023}, author = {Degirmenci, M and Yuce, YK and Perc, M and Isler, Y}, title = {Statistically significant features improve binary and multiple Motor Imagery task predictions from EEGs.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1223307}, pmid = {37497042}, issn = {1662-5161}, abstract = {In recent studies, in the field of Brain-Computer Interface (BCI), researchers have focused on Motor Imagery tasks. Motor Imagery-based electroencephalogram (EEG) signals provide the interaction and communication between the paralyzed patients and the outside world for moving and controlling external devices such as wheelchair and moving cursors. However, current approaches in the Motor Imagery-BCI system design require effective feature extraction methods and classification algorithms to acquire discriminative features from EEG signals due to the non-linear and non-stationary structure of EEG signals. This study investigates the effect of statistical significance-based feature selection on binary and multi-class Motor Imagery EEG signal classifications. In the feature extraction process performed 24 different time-domain features, 15 different frequency-domain features which are energy, variance, and entropy of Fourier transform within five EEG frequency subbands, 15 different time-frequency domain features which are energy, variance, and entropy of Wavelet transform based on five EEG frequency subbands, and 4 different Poincare plot-based non-linear parameters are extracted from each EEG channel. A total of 1,364 Motor Imagery EEG features are supplied from 22 channel EEG signals for each input EEG data. In the statistical significance-based feature selection process, the best one among all possible combinations of these features is tried to be determined using the independent t-test and one-way analysis of variance (ANOVA) test on binary and multi-class Motor Imagery EEG signal classifications, respectively. The whole extracted feature set and the feature set that contain statistically significant features only are classified in this study. We implemented 6 and 7 different classifiers in multi-class and binary (two-class) classification tasks, respectively. The classification process is evaluated using the five-fold cross-validation method, and each classification algorithm is tested 10 times. These repeated tests provide to check the repeatability of the results. The maximum of 61.86 and 47.36% for the two-class and four-class scenarios, respectively, are obtained with Ensemble Subspace Discriminant among all these classifiers using selected features including only statistically significant features. The results reveal that the introduced statistical significance-based feature selection approach improves the classifier performances by achieving higher classifier performances with fewer relevant components in Motor Imagery task classification. In conclusion, the main contribution of the presented study is two-fold evaluation of non-linear parameters as an alternative to the commonly used features and the prediction of multiple Motor Imagery tasks using statistically significant features.}, }
@article {pmid37497040, year = {2023}, author = {Kleih-Dahms, SC and Botrel, L}, title = {Neurofeedback therapy to improve cognitive function in patients with chronic post-stroke attention deficits: a within-subjects comparison.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1155584}, pmid = {37497040}, issn = {1662-5161}, abstract = {INTRODUCTION: We investigated a slow-cortical potential (SCP) neurofeedback therapy approach for rehabilitating chronic attention deficits after stroke. This study is the first attempt to train patients who survived stroke with SCP neurofeedback therapy.<br><br>METHODS: We included N = 5 participants in a within-subjects follow-up design. We assessed neuropsychological and psychological performance at baseline (4 weeks before study onset), before study onset, after neurofeedback training, and at 3 months follow-up. Participants underwent 20 sessions of SCP neurofeedback training.<br><br>RESULTS: Participants learned to regulate SCPs toward negativity, and we found indications for improved attention after the SCP neurofeedback therapy in some participants. Quality of life improved throughout the study according to engagement in activities of daily living. The self-reported motivation was related to mean SCP activation in two participants.<br><br>DISCUSSION: We would like to bring attention to the potential of SCP neurofeedback therapy as a new rehabilitation method for treating post-stroke cognitive deficits. Studies with larger samples are warranted to corroborate the results.}, }
@article {pmid37496741, year = {2023}, author = {Xu, H and Cao, K and Chen, H and Abudusalamu, A and Wu, W and Xue, Y}, title = {Emotional brain network decoded by biological spiking neural network.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1200701}, pmid = {37496741}, issn = {1662-4548}, abstract = {INTRODUCTION: Emotional disorders are essential manifestations of many neurological and psychiatric diseases. Nowadays, researchers try to explore bi-directional brain-computer interface techniques to help the patients. However, the related functional brain areas and biological markers are still unclear, and the dynamic connection mechanism is also unknown.<br><br>METHODS: To find effective regions related to different emotion recognition and intervention, our research focuses on finding emotional EEG brain networks using spiking neural network algorithm with binary coding. We collected EEG data while human participants watched emotional videos (fear, sadness, happiness, and neutrality), and analyzed the dynamic connections between the electrodes and the biological rhythms of different emotions.<br><br>RESULTS: The analysis has shown that the local high-activation brain network of fear and sadness is mainly in the parietal lobe area. The local high-level brain network of happiness is in the prefrontal-temporal lobe-central area. Furthermore, the &#x3b1; frequency band could effectively represent negative emotions, while the &#x3b1; frequency band could be used as a biological marker of happiness. The decoding accuracy of the three emotions reached 86.36%, 95.18%, and 89.09%, respectively, fully reflecting the excellent emotional decoding performance of the spiking neural network with self- backpropagation.<br><br>DISCUSSION: The introduction of the self-backpropagation mechanism effectively improves the performance of the spiking neural network model. Different emotions exhibit distinct EEG networks and neuro-oscillatory-based biological markers. These emotional brain networks and biological markers may provide important hints for brain-computer interface technique exploration to help related brain disease recovery.}, }
@article {pmid37496515, year = {2023}, author = {Lu, Z and Wang, T and Zhang, R}, title = {Editorial: Affective brain-computer interface in emotion artificial intelligence and medical engineering.}, journal = {Frontiers in computational neuroscience}, volume = {17}, number = {}, pages = {1237252}, pmid = {37496515}, issn = {1662-5188}, }
@article {pmid37496395, year = {2023}, author = {Estaño, LA and Jumawan, JC}, title = {The prevailing infection of Schistosoma japonicum and other zoonotic parasites in bubaline reservoir hosts in the ricefield of lake ecosystem: the case of Lake Mainit, Philippines.}, journal = {Parasitology}, volume = {}, number = {}, pages = {1-6}, doi = {10.1017/S0031182023000537}, pmid = {37496395}, issn = {1469-8161}, support = {//Philippine Council for Health Research and Development/ ; }, abstract = {Bovines are important reservoir hosts of schistosomiasis, placing humans and animals in rice fields areas at risk of infection. This study reported the prevailing infection of zoonotic parasites from bovine feces in the rice fields adjacent to Lake Mainit, Philippines. Formalin Ethyl Acetate Sedimentation was performed on 124 bovine fecal samples from rice fields and documented eggs and cysts from seven parasites: Schistosoma japonicum, Fasciola gigantica, Ascaris sp., Strongyloides sp., Balantidium coli, coccidian oocyst and a hookworm species. Among these parasites, F. gigantica harboured the highest infection with a 100% prevalence rate, followed by hookworms (51.61%), B. coli (30.64%) and S. japonicum (12.09%), respectively. The intensity of infection of S. japonicum eggs per gram (MPEG = 4.19) among bovines is categorized as ‘light.’ Bovine contamination index (BCI) calculations revealed that, on average, infected bovines in rice fields excrete 104 750 S. japonicum eggs daily. However, across all ricefield stations, bovines were heavily infected with fascioliasis with BCI at 162 700 F. gigantica eggs per day. The study reports that apart from the persistent cases of schistosomiasis in the area, bovines in these rice fields are also heavily infected with fascioliasis. The study confirms the critical role of bovines as a reservoir host for continued infection of schistosomiasis, fascioliasis and other diseases in the rice fields of Lake Mainit. Immediate intervention to manage the spread of these diseases in bovines is recommended.}, }
@article {pmid37494734, year = {2023}, author = {Zhang, X and Wang, X and Zhu, H and Zhang, D and Chen, J and Wen, Y and Li, Y and Jin, L and Xie, C and Guo, D and Luo, T and Tong, J and Zhou, Y and Shen, Y}, title = {Short-wavelength artificial light affects visual neural pathway development in mice.}, journal = {Ecotoxicology and environmental safety}, volume = {263}, number = {}, pages = {115282}, doi = {10.1016/j.ecoenv.2023.115282}, pmid = {37494734}, issn = {1090-2414}, abstract = {Nearly all modern life depends on artificial light; however, it does cause health problems. With certain restrictions of artificial light emitting technology, the influence of the light spectrum is inevitable. The most remarkable problem is its overload in the short wavelength component. Short wavelength artificial light has a wide range of influences from ocular development to mental problems. The visual neuronal pathway, as the primary light-sensing structure, may contain the fundamental mechanism of all light-induced abnormalities. However, how the artificial light spectrum shapes the visual neuronal pathway during development in mammals is poorly understood. We placed C57BL/6 mice in three different spectrum environments (full-spectrum white light: 400-750 nm; violet light: 400 ± 20 nm; green light: 510 ± 20 nm) beginning at eye opening, with a fixed light time of 7:00-19:00. During development, we assessed the ocular axial dimension, visual function and retinal neurons. After two weeks under short wavelength conditions, the ocular axial length (AL), anterior chamber depth (ACD) and length of lens thickness, real vitreous chamber depth and retinal thickness (LLVR) were shorter, visual acuity (VA) decreased, and retinal electrical activity was impaired. The density of S-cones in the dorsal and ventral retinas both decreased after one week under short wavelength conditions. In the ventral retina, it increased after three weeks. Retinal ganglion cell (RGC) density and axon thickness were not influenced; however, the axonal terminals in the lateral geniculate nucleus (LGN) were less clustered and sparse. Amacrine cells (ACs) were significantly more activated. Green light has few effects. The KEGG and GO enrichment analyses showed that many genes related to neural circuitry, synaptic formation and neurotransmitter function were differentially expressed in the short wavelength light group. In conclusion, exposure to short wavelength artificial light in the early stage of vision-dependent development in mice delayed the development of the visual pathway. The axon terminus structure and neurotransmitter function may be the major suffering.}, }
@article {pmid37494151, year = {2023}, author = {Forenzo, D and Liu, Y and Kim, J and Ding, Y and Yoon, T and He, B}, title = {Integrating Simultaneous Motor Imagery and Spatial Attention for EEG-BCI Control.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2023.3298957}, pmid = {37494151}, issn = {1558-2531}, abstract = {OBJECTIVE: EEG-based brain-computer interfaces (BCI) are non-invasive approaches for replacing or restoring motor functions in impaired patients, and direct brain-to-device communication in the general population. Motor imagery (MI) is one of the most used BCI paradigms, but its performance varies across individuals and certain users require substantial training to develop control. In this study, we propose to integrate a MI paradigm simultaneously with a recently proposed Overt Spatial Attention (OSA) paradigm, to accomplish BCI control.<br><br>METHODS: We evaluated a cohort of 25 human subjects' ability to control a virtual cursor in one- and two-dimensions over 5 BCI sessions. The subjects used 5 different BCI paradigms: MI alone, OSA alone, MI and OSA simultaneously towards the same target (MI+OSA), and MI for one axis while OSA controls the other (MI/OSA and OSA/MI).<br><br>RESULTS: Our results show that MI+OSA reached the highest average online performance in 2D tasks at 49% Percent Valid Correct (PVC), and statistically outperforms both MI alone (42%) and OSA alone (45%). MI+OSA had a similar performance to each subject's best individual method between MI alone and OSA alone (50%) and 9 subjects reached their highest average BCI performance using MI+OSA.<br><br>CONCLUSION: Integrating MI and OSA leads to improved performance over both individual methods at the group level and is the best BCI paradigm option for some subjects.<br><br>SIGNIFICANCE: This work proposes a new BCI control paradigm that integrates two existing paradigms and demonstrates its value by showing that it can improve users' BCI performance.}, }
@article {pmid37492903, year = {2023}, author = {Zhang, Y and Valsecchi, M and Gegenfurtner, KR and Chen, J}, title = {Laplacian reference is optimal for steady-state visual evoked potentials.}, journal = {Journal of neurophysiology}, volume = {}, number = {}, pages = {}, doi = {10.1152/jn.00469.2022}, pmid = {37492903}, issn = {1522-1598}, support = {31900758//MOST | National Natural Science Foundation of China (NSFC)/ ; 222641018//Deutsche Forschungsgemeinschaft (DFG)/ ; }, abstract = {Steady-state visual evoked potentials (SSVEPs) are widely used in human neuroscience studies and applications such as brain-computer interfaces. Surprisingly, no previous study has systematically evaluated different reference methods for SSVEP analysis, despite that signal reference is crucial for the proper assessment of neural activities. In the present study, using four datasets from our previous SSVEP studies (1-3) and three public datasets from other studies (4-6), we compared four reference methods: monopolar reference, common average reference, averaged-mastoids reference, and Laplacian reference. The quality of the resulting SSVEP signals was compared in terms of both signal-to-noise ratios (SNRs) and reliability. The results showed that Laplacian reference, which uses signals at the maximally activated electrode after subtracting the average of the nearby electrodes to reduce common noise, gave rise to the highest SNRs. Furthermore, the Laplacian reference resulted in SSVEP signals that were highly reliable across recording sessions or trials. These results suggest that Laplacian reference is optimal for SSVEP studies and applications. Laplacian reference is especially advantageous for SSVEP experiments where short preparation time is preferred, since it requires only data from the maximally activated electrode and a few surrounding electrodes.}, }
@article {pmid37491998, year = {2023}, author = {Wang, J and Wang, X and Zou, J and Duan, J and Shen, Z and Xu, N and Chen, Y and Zhang, J and He, H and Bi, Y and Ding, N}, title = {Neural substrate underlying the learning of a passage with unfamiliar vocabulary and syntax.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {}, number = {}, pages = {}, doi = {10.1093/cercor/bhad263}, pmid = {37491998}, issn = {1460-2199}, support = {2021ZD0204105//STI2030-Major Project/ ; 2022C03011//Key R&amp;D Program of Zhejiang/ ; }, abstract = {Speech comprehension is a complex process involving multiple stages, such as decoding of phonetic units, recognizing words, and understanding sentences and passages. In this study, we identify cortical networks beyond basic phonetic processing using a novel passage learning paradigm. Participants learn to comprehend a story composed of syllables of their native language, but containing unfamiliar vocabulary and syntax. Three learning methods are employed, each resulting in some degree of learning within a 12-min learning session. Functional magnetic resonance imaging results reveal that, when listening to the same story, the classic temporal-frontal language network is significantly enhanced by learning. Critically, activation of the left anterior and posterior temporal lobe correlates with the learning outcome that is assessed behaviorally through, e.g. word recognition and passage comprehension tests. This study demonstrates that a brief learning session is sufficient to induce neural plasticity in the left temporal lobe, which underlies the transformation from phonetic units to the units of meaning, such as words and sentences.}, }
@article {pmid37491837, year = {2023}, author = {Li, S and Lv, D and Qian, C and Jiang, J and Zhang, P and Xi, C and Wu, L and Gao, X and Fu, Y and Zhang, D and Chen, Y and Huang, H and Zhu, Y and Wang, X and Lai, J and Hu, S}, title = {Circulating T-cell subsets discrepancy between bipolar disorder and major depressive disorder during mood episodes: A naturalistic, retrospective study of 1015 cases.}, journal = {CNS neuroscience &amp; therapeutics}, volume = {}, number = {}, pages = {}, doi = {10.1111/cns.14361}, pmid = {37491837}, issn = {1755-5949}, support = {2020R01001//Innovation Group Program of Zhejiang Province/ ; 2021R52016//Leading Talent of Scientific and Technological Innovation/ ; 81971271//National Natural Science Foundation of China/ ; 82201676//National Natural Science Foundation of China/ ; LQ20H090013//Natural Science Foundation of Zhejiang Province/ ; 2021C03107//Zhejiang Provincial Key Research and Development Program/ ; }, abstract = {AIMS: We aimed to investigate whether peripheral T-cell subsets could be a biomarker to distinguish major depressive disorder (MDD) and bipolar disorder (BD).<br><br>METHODS: Medical records of hospitalized patients in the Department of Psychiatry, the First Affiliated Hospital, Zhejiang University School of Medicine, from January 2015 to September 2020 with a discharge diagnosis of MDD or BD were reviewed. Patients who underwent peripheral blood examination of T-cell subtype proportions, including CD3+, CD4+, CD8+ T-cell, and natural killer (NK) cells, were enrolled. The Chi-square test, t-test, or one-way analysis of variance were used to analyze group differences. Demographic profiles and T-cell data were used to construct a random forest classifier-based diagnostic model.<br><br>RESULTS: Totally, 98 cases of BD mania, 459 cases of BD depression (BD-D), and 458 cases of MDD were included. There were significant differences in the proportions of CD3+, CD4+, CD8+ T-cell, and NK cells among the three groups. Compared with MDD, the BD-D group showed higher CD8+ but lower CD4+ T-cell and a significantly lower ratio of CD4+ and CD8+ proportions. The random forest model achieved an area under the curve of 0.77 (95% confidence interval: 0.71-0.83) to distinguish BD-D from MDD patients.<br><br>CONCLUSION: These findings imply that BD and MDD patients may harbor different T-cell inflammatory patterns, which could be a potential diagnostic biomarker for mood disorders.}, }
@article {pmid37491671, year = {2023}, author = {Jiang, H and Chen, P and Sun, Z and Liang, C and Xue, R and Zhao, L and Wang, Q and Li, X and Deng, W and Gao, Z and Huang, F and Huang, S and Zhang, Y and Li, T}, title = {Assisting schizophrenia diagnosis using clinical electroencephalography and interpretable graph neural networks: a real-world and cross-site study.}, journal = {Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology}, volume = {}, number = {}, pages = {}, pmid = {37491671}, issn = {1740-634X}, abstract = {Schizophrenia (SCZ) is a chronic and serious mental disorder with a high mortality rate. At present, there is a lack of objective, cost-effective and widely disseminated diagnosis tools to address this mental health crisis globally. Clinical electroencephalogram (EEG) is a noninvasive technique to measure brain activity with high temporal resolution, and accumulating evidence demonstrates that clinical EEG is capable of capturing abnormal SCZ neuropathology. Although EEG-based automated diagnostic tools have obtained impressive performance on individual datasets, the transportability of potential EEG biomarkers in cross-site real-world application is still an open question. To address the challenges of small sample sizes and population heterogeneity, we develop an advanced interpretable deep learning model using multimodal clinical EEG features and demographic information as inputs to graph neural networks, and further propose different transfer learning strategies to adapt to different clinical scenarios. Taking the disease discrimination of health control (HC) and SCZ with 1030 participants as a use case, our model is trained on a small clinical dataset (N = 188, Chinese) and enhanced using a large-scale public dataset (N = 508, American) of adult participants. Cross-site validation from an independent dataset of adult participants (N = 157, Chinese) produced stable performance, with AUCs of 0.793-0.852 and accuracies of 0.786-0.858 for different SCZ prevalence, respectively. In addition, cross-site validation from another dataset of adolescent boys (N = 84, Russian) yielded an AUC of 0.702 and an accuracy of 0.690. Moreover, feature visualization further revealed that the ranking of feature importance varied significantly among different datasets, and that EEG theta and alpha band power appeared to be the most significant and translational biomarkers of SCZ pathology. Overall, our promising results demonstrate the feasibility of SCZ discrimination using EEG biomarkers in multiple clinical settings.}, }
@article {pmid37491649, year = {2023}, author = {Shao, YR and Yu, JY and Ma, Y and Dong, Y and Wu, ZY}, title = {CAT Interruption as a Protective Factor in Chinese Patients with Spinocerebellar Ataxia Type 1.}, journal = {Cerebellum (London, England)}, volume = {}, number = {}, pages = {}, pmid = {37491649}, issn = {1473-4230}, abstract = {Spinocerebellar ataxia type 1 (SCA1) is the third most common type of spinocerebellar ataxias in China. CAT interruptions in the pathogenic alleles of SCA1 patients had only been reported by limited documents and there was a lack of data based on the Chinese population. In this study, we detected CAT interrupted pathogenic alleles in SCA1 patients from 4 out of 79 (5.1%) Chinese families. Their total CAG repeats were larger (median 58 vs. 47, p < 0.001) but ages at onset were later (median 46 vs. 38, p = 0.020). The longest uninterrupted CAG repeats could explain 65.4% of the AAO variance, making an increase of 28.0% compared to the total CAG repeats. The interruption pattern was greatly different between Chinese cohort and Caucasian cohort, indicating the effect of race.}, }
@article {pmid37488871, year = {2024}, author = {Tao, Q and Chao, H and Fang, D and Dou, D}, title = {Progress in neurorehabilitation research and the support by the National Natural Science Foundation of China from 2010 to 2022.}, journal = {Neural regeneration research}, volume = {19}, number = {1}, pages = {226-232}, doi = {10.4103/1673-5374.375342}, pmid = {37488871}, issn = {1673-5374}, abstract = {The National Natural Science Foundation of China is one of the major funding agencies for neurorehabilitation research in China. This study reviews the frontier directions and achievements in the field of neurorehabilitation in China and worldwide. We used data from the Web of Science Core Collection (WoSCC) database to analyze the publications and data provided by the National Natural Science Foundation of China to analyze funding information. In addition, the prospects for neurorehabilitation research in China are discussed. From 2010 to 2022, a total of 74,220 publications in neurorehabilitation were identified, with there being an overall upward tendency. During this period, the National Natural Science Foundation of China has funded 476 research projects with a total funding of 192.38 million RMB to support neurorehabilitation research in China. With the support of the National Natural Science Foundation of China, China has made some achievements in neurorehabilitation research. Research related to neurorehabilitation is believed to be making steady and significant progress in China.}, }
@article {pmid37487829, year = {2023}, author = {Zhao, Y and Chen, Y and Cheng, K and Huang, W}, title = {Artificial intelligence based multimodal language decoding from brain activity: A review.}, journal = {Brain research bulletin}, volume = {201}, number = {}, pages = {110713}, doi = {10.1016/j.brainresbull.2023.110713}, pmid = {37487829}, issn = {1873-2747}, abstract = {Decoding brain activity is conducive to the breakthrough of brain-computer interface (BCI) technology. The development of artificial intelligence (AI) continually promotes the progress of brain language decoding technology. Existent research has mainly focused on a single modality and paid insufficient attention to AI methods. Therefore, our objective is to provide an overview of relevant decoding research from the perspective of different modalities and methodologies. The modalities involve text, speech, image, and video, whereas the core method is using AI-built decoders to translate brain signals induced by multimodal stimuli into text or vocal language. The semantic information of brain activity can be successfully decoded into a language at various levels, ranging from words through sentences to discourses. However, the decoding effect is affected by various factors, such as the decoding model, vector representation model, and brain regions. Challenges and future directions are also discussed. The advances in brain language decoding and BCI technology will potentially assist patients with clinical aphasia in regaining the ability to communicate.}, }
@article {pmid37487487, year = {2023}, author = {Thomas, TM and Singh, A and Bullock, LP and Liang, D and Morse, CW and Scherschligt, X and Seymour, JP and Tandon, N}, title = {Decoding articulatory and phonetic components of naturalistic continuous speech from the distributed language network.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ace9fb}, pmid = {37487487}, issn = {1741-2552}, abstract = {Objective The speech production network relies on a widely distributed brain network. However, research and development of speech brain-computer interfaces (speech-BCIs) has typically focused on decoding speech only from superficial subregions readily accessible by subdural grid arrays - typically placed over the sensorimotor cortex. Alternatively, the technique of stereo-electroencephalography (sEEG) enables access to distributed brain regions using multiple depth electrodes with lower surgical risks, especially in patients with brain injuries resulting in aphasia and other speech disorders. Approach To investigate the decoding potential of widespread electrode coverage in multiple cortical sites, we used a naturalistic continuous speech production task. We obtained neural recordings using sEEG from eight participants while they read aloud sentences. We trained linear classifiers to decode distinct speech components (articulatory components and phonemes) solely based on broadband gamma activity and evaluated the decoding performance using nested 5-fold cross-validation. Main Results We achieved an average classification accuracy of 18.7% across 9 places of articulation (e.g. bilabials, palatals), 26.5% across 5 manner of articulation labels (e.g. affricates, fricatives), and 4.81% across 38 phonemes. The highest classification accuracies achieved with a single large dataset were 26.3% for place of articulation, 35.7% for manner of articulation, and 9.88% for phonemes. Electrodes that contributed high decoding power were distributed across multiple sulcal and gyral sites in both dominant and non-dominant hemispheres, including ventral sensorimotor, inferior frontal, superior temporal, and fusiform cortices. Rather than finding a distinct cortical locus for each speech component, we observed neural correlates of both articulatory and phonetic components in multiple hubs of a widespread language production network. Significance These results reveal the distributed cortical representations whose activity can enable decoding speech components during continuous speech through the use of this minimally invasive recording method, elucidating language neurobiology and neural targets for future speech-BCIs.}, }
@article {pmid37486136, year = {2023}, author = {Arpaia, P and Esposito, A and Moccaldi, N and Parvis, M}, title = {A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare.}, journal = {Journal of visualized experiments : JoVE}, volume = {}, number = {197}, pages = {}, doi = {10.3791/65007}, pmid = {37486136}, issn = {1940-087X}, abstract = {The present work focuses on how to build a wearable brain-computer interface (BCI). BCIs are a novel means of human-computer interaction that relies on direct measurements of brain signals to assist both people with disabilities and those who are able-bodied. Application examples include robotic control, industrial inspection, and neurorehabilitation. Notably, recent studies have shown that steady-state visually evoked potentials (SSVEPs) are particularly suited for communication and control applications, and efforts are currently being made to bring BCI technology into daily life. To achieve this aim, the final system must rely on wearable, portable, and low-cost instrumentation. In exploiting SSVEPs, a flickering visual stimulus with fixed frequencies is required. Thus, in considering daily-life constraints, the possibility to provide visual stimuli by means of smart glasses was explored in this study. Moreover, to detect the elicited potentials, a commercial device for electroencephalography (EEG) was considered. This consists of a single differential channel with dry electrodes (no conductive gel), thus achieving the utmost wearability and portability. In such a BCI, the user can interact with the smart glasses by merely staring at icons appearing on the display. Upon this simple principle, a user-friendly, low-cost BCI was built by integrating extended reality (XR) glasses with a commercially available EEG device. The functionality of this wearable XR-BCI was examined with an experimental campaign involving 20 subjects. The classification accuracy was between 80%-95% on average depending on the stimulation time. Given these results, the system can be used as a human-machine interface for industrial inspection but also for rehabilitation in ADHD and autism.}, }
@article {pmid37484920, year = {2023}, author = {Bates, M and Sunderam, S}, title = {Hand-worn devices for assessment and rehabilitation of motor function and their potential use in BCI protocols: a review.}, journal = {Frontiers in human neuroscience}, volume = {17}, number = {}, pages = {1121481}, pmid = {37484920}, issn = {1662-5161}, abstract = {INTRODUCTION: Various neurological conditions can impair hand function. Affected individuals cannot fully participate in activities of daily living due to the lack of fine motor control. Neurorehabilitation emphasizes repetitive movement and subjective clinical assessments that require clinical experience to administer.<br><br>METHODS: Here, we perform a review of literature focused on the use of hand-worn devices for rehabilitation and assessment of hand function. We paid particular attention to protocols that involve brain-computer interfaces (BCIs) since BCIs are gaining ground as a means for detecting volitional signals as the basis for interactive motor training protocols to augment recovery. All devices reviewed either monitor, assist, stimulate, or support hand and finger movement.<br><br>RESULTS: A majority of studies reviewed here test or validate devices through clinical trials, especially for stroke. Even though sensor gloves are the most commonly employed type of device in this domain, they have certain limitations. Many such gloves use bend or inertial sensors to monitor the movement of individual digits, but few monitor both movement and applied pressure. The use of such devices in BCI protocols is also uncommon.<br><br>DISCUSSION: We conclude that hand-worn devices that monitor both flexion and grip will benefit both clinical diagnostic assessment of function during treatment and closed-loop BCI protocols aimed at rehabilitation.}, }
@article {pmid37484690, year = {2023}, author = {Xu, M and Qian, L and Wang, S and Cai, H and Sun, Y and Thakor, N and Qi, X and Sun, Y}, title = {Brain network analysis reveals convergent and divergent aberrations between mild stroke patients with cortical and subcortical infarcts during cognitive task performing.}, journal = {Frontiers in aging neuroscience}, volume = {15}, number = {}, pages = {1193292}, pmid = {37484690}, issn = {1663-4365}, abstract = {Although consistent evidence has revealed that cognitive impairment is a common sequela in patients with mild stroke, few studies have focused on it, nor the impact of lesion location on cognitive function. Evidence on the neural mechanisms underlying the effects of mild stroke and lesion location on cognitive function is limited. This prompted us to conduct a comprehensive and quantitative study of functional brain network properties in mild stroke patients with different lesion locations. Specifically, an empirical approach was introduced in the present work to explore the impact of mild stroke-induced cognitive alterations on functional brain network reorganization during cognitive tasks (i.e., visual and auditory oddball). Electroencephalogram functional connectivity was estimated from three groups (i.e., 40 patients with cortical infarctions, 48 patients with subcortical infarctions, and 50 healthy controls). Using graph theoretical analysis, we quantitatively investigated the topological reorganization of functional brain networks at both global and nodal levels. Results showed that both patient groups had significantly worse behavioral performance on both tasks, with significantly longer reaction times and reduced response accuracy. Furthermore, decreased global and local efficiency were found in both patient groups, indicating a mild stroke-related disruption in information processing efficiency that is independent of lesion location. Regarding the nodal level, both divergent and convergent node strength distribution patterns were revealed between both patient groups, implying that mild stroke with different lesion locations would lead to complex regional alterations during visual and auditory information processing, while certain robust cognitive processes were independent of lesion location. These findings provide some of the first quantitative insights into the complex neural mechanisms of mild stroke-induced cognitive impairment and extend our understanding of underlying alterations in cognition-related brain networks induced by different lesion locations, which may help to promote post-stroke management and rehabilitation.}, }
@article {pmid37483349, year = {2023}, author = {Chandrasekaran, S and Bhagat, NA and Ramdeo, R and Ebrahimi, S and Sharma, PD and Griffin, DG and Stein, A and Harkema, SJ and Bouton, CE}, title = {Targeted transcutaneous spinal cord stimulation promotes persistent recovery of upper limb strength and tactile sensation in spinal cord injury: a pilot study.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1210328}, pmid = {37483349}, issn = {1662-4548}, abstract = {Long-term recovery of limb function is a significant unmet need in people with paralysis. Neuromodulation of the spinal cord through epidural stimulation, when paired with intense activity-based training, has shown promising results toward restoring volitional limb control in people with spinal cord injury. Non-invasive neuromodulation of the cervical spinal cord using transcutaneous spinal cord stimulation (tSCS) has shown similar improvements in upper-limb motor control rehabilitation. However, the motor and sensory rehabilitative effects of activating specific cervical spinal segments using tSCS have largely remained unexplored. We show in two individuals with motor-complete SCI that targeted stimulation of the cervical spinal cord resulted in up to a 1,136% increase in exerted force, with weekly activity-based training. Furthermore, this is the first study to document up to a 2-point improvement in clinical assessment of tactile sensation in SCI after receiving tSCS. Lastly, participant gains persisted after a one-month period void of stimulation, suggesting that targeted tSCS may lead to persistent recovery of motor and sensory function.}, }
@article {pmid37480644, year = {2023}, author = {Chen, L and Liao, H and Kong, W and Zhang, D and Chen, F}, title = {Anatomy preserving GAN for realistic simulation of intraoperative liver ultrasound images.}, journal = {Computer methods and programs in biomedicine}, volume = {240}, number = {}, pages = {107642}, doi = {10.1016/j.cmpb.2023.107642}, pmid = {37480644}, issn = {1872-7565}, abstract = {In ultrasound-guided liver surgery, the lack of large-scale intraoperative ultrasound images with important anatomical structures remains an obstacle hindering the successful application of AI to ultrasound guidance. In this case, intraoperative ultrasound (iUS) simulation should be conducted from preoperative magnetic resonance (pMR), which not only helps doctors understand the characteristics of iUS in advance, but also expands the iUS dataset from various imaging positions, thereby promoting the automatic iUS analysis in ultrasound guidance. Herein, a novel anatomy preserving generative adversarial network (ApGAN) framework was proposed to generate simulated intraoperative ultrasound (Sim-iUS) of liver with precise structure information from pMR. Specifically, the low-rank factors based bimodal fusion was first established focusing on the effective information of hepatic parenchyma. Then, a deformation field based correction module was introduced to learn and correct the slight structural distortion from surgical operations. Meanwhile, the multiple loss functions were designed to constrain the simulation of the content, structures, and style. Empirical results of clinical data showed that the proposed ApGAN obtained higher Structural Similarity (SSIM) of 0.74 and Fr´echet Inception Distance (FID) of 35.54 compared to existing methods. Furthermore, the average Hausdorff Distance (HD) error of the liver capsule structure was less than 0.25 mm, and the average relative (Euclidean Distance) ED error for polyps was 0.12 mm, indicating the high-level precision of this ApGAN in simulating the anatomical structures and focal areas.}, }
@article {pmid37480186, year = {2023}, author = {Werner, CM and Willing, LB and Goudsward, HJ and McBride, AR and Stella, SL and Holmes, GM}, title = {Plasticity of colonic enteric nervous system following spinal cord injury in male and female rats.}, journal = {Neurogastroenterology and motility : the official journal of the European Gastrointestinal Motility Society}, volume = {}, number = {}, pages = {e14646}, doi = {10.1111/nmo.14646}, pmid = {37480186}, issn = {1365-2982}, support = {R01-NS-105987/NS/NINDS NIH HHS/United States ; }, abstract = {BACKGROUND: Neurogenic bowel is a dysmotility disorder following spinal cord injury (SCI) that negatively impacts quality of life, social integration, and physical health. Colonic transit is directly modulated by the enteric nervous system. Interstitial Cells of Cajal (ICC) distributed throughout the small intestine and colon serve as specialized pacemaker cells, generating rhythmic electrical slow waves within intestinal smooth muscle, or serve as an interface between smooth muscle cells and enteric motor neurons of the myenteric plexus. Interstitial Cells of Cajal loss has been reported for other preclinical models of dysmotility, and our previous experimental SCI study provided evidence of reduced excitatory and inhibitory enteric neuronal count and smooth muscle neural control.<br><br>METHODS: Immunohistochemistry for the ICC-specific marker c-Kit was utilized to examine neuromuscular remodeling of the distal colon in male and female rats with experimental SCI.<br><br>KEY RESULTS: Myenteric plexus ICC (ICC-MP) exhibited increased cell counts 3&#x2009;days following SCI in male rats, but did not significantly increase in females until 3&#x2009;weeks after SCI. On average, ICC-MP total primary arborization length increased significantly in male rats at 3-day, 3-week, and 6-week time points, whereas in females, this increase occurred most frequently at 6&#x2009;weeks post-SCI. Conversely, circular muscle ICC (ICC-CM) did not demonstrate post-SCI changes.<br><br>CONCLUSIONS AND INFERENCES: These data demonstrate resiliency of the ICC-MP in neurogenic bowel following SCI, unlike seen in other related disease states. This plasticity underscores the need to further understand neuromuscular changes driving colonic dysmotility after SCI in order to advance therapeutic targets for neurogenic bowel treatment.}, }
@article {pmid37478039, year = {2023}, author = {Wang, H and Cao, L and Huang, C and Jia, J and Dong, Y and Fan, C and De Albuquerque, VHC}, title = {A novel algorithmic structure of EEG Channel Attention combined with Swin Transformer for motor patterns classification.}, journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNSRE.2023.3297654}, pmid = {37478039}, issn = {1558-0210}, abstract = {With the development of brain-computer interfaces (BCI) technologies, EEG-based BCI applications have been deployed for medical purposes. Motor imagery (MI), applied to promote neural rehabilitation for stroke patients, is among the most common BCI paradigms that. The Electroencephalogram (EEG) signals, encompassing an extensive range of channels, render the training dataset a high-dimensional construct. This high dimensionality, inherent in such a dataset, tends to challenge traditional deep learning approaches, causing them to potentially disregard the intrinsic correlations amongst these channels. Such an oversight often culminates in erroneous data classification, presenting a significant drawback of these conventional methodologies. In our study, we propose a novel algorithmic structure of EEG channel-attention combined with Swin Transformer for motor pattern recognition in BCI rehabilitation. Effectively, the self-attention module from transformer architecture could captures temporal-spectral-spatial features hidden in EEG data. The experimental results verify that our proposed methods outperformed other state-of-art approaches with the average accuracy of 87.67%. It is implied that our method can extract high-level and latent connections among temporal-spectral features in contrast to traditional deep learning methods. This paper demonstrates that channel-attention combined with Swin Transformer methods has great potential for implementing high-performance motor pattern-based BCI systems.}, }
@article {pmid37473638, year = {2023}, author = {Zhang, S and Shi, E and Wu, L and Wang, R and Yu, S and Liu, Z and Xu, S and Liu, T and Zhao, S}, title = {Differentiating brain states via multi-clip random fragment strategy-based interactive bidirectional recurrent neural network.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {165}, number = {}, pages = {1035-1049}, doi = {10.1016/j.neunet.2023.06.040}, pmid = {37473638}, issn = {1879-2782}, abstract = {EEG is widely adopted to study the brain and brain computer interface (BCI) for its non-invasiveness and low costs. Specifically EEG can be applied to differentiate brain states, which is important for better understanding the working mechanisms of the brain. Recurrent neural network (RNN)-based learning strategy has been widely utilized to differentiate brain states, because its optimization architectures improve the classification performance for differentiating brain states at the group level. However, present classification performance is still far from satisfactory. We have identified two major focal points for improvements: one is about organizing the input EEG signals, and the other is related to the design of the RNN architecture. To optimize the above-mentioned issues and achieve better brain state classification performance, we propose a novel multi-clip random fragment strategy-based interactive bidirectional recurrent neural network (McRFS-IBiRNN) model in this work. This model has two advantages over previous methods. First, the McRFS component is designed to re-organize the input EEG signals to make them more suitable for the RNN architecture. Second, the IBiRNN component is an innovative design to model the RNN layers with interaction connections to enhance the fusion of bidirectional features. By adopting the proposed model, promising brain states classification performances are obtained. For example, 96.97% and 99.34% of individual and group level four-category classification accuracies are successfully obtained on the EEG motor/imagery dataset, respectively. A 99.01% accuracy can be observed for four-category classification tasks with new subjects not seen before, which demonstrates the generalization of our proposed method. Compared with existing methods, our model outperforms them with superior results. Overall, the proposed McRFS-IBiRNN model demonstrates great superiority in differentiating brain states on EEG signals.}, }
@article {pmid37467742, year = {2023}, author = {Hu, R and Ming, G and Wang, Y and Gao, X}, title = {A sub-region combination scheme for spatial coding in a high-frequency SSVEP-based BCI.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ace8bd}, pmid = {37467742}, issn = {1741-2552}, abstract = {In studying the spatial coding mechanism of visual evoked potentials, it is significant to construct a model that shows the relationship between steady-state visual evoked potential (SSVEP) responses to the local and global visual field stimulation. In order to investigate whether SSVEPs produced by sub-region stimulation can predict that produced by joint region stimulation, a sub-region combination scheme for spatial coding in a high-frequency SSVEP-based brain-computer interface (BCI) is developed innovatively. Approach. An annular visual field is divided equally into eight sub-regions. The 60 Hz visual stimuli in different sub-regions and joint regions are presented separately to participants. The SSVEP produced by the sub-region stimulation is superimposed to simulate the SSVEP produced by the joint region stimulation with different spatial combinations. A four-class spatially-coded BCI paradigm is used to evaluate the simulated classification performance, and the performance ranking of all simulated SSVEPs is obtained. Six representative stimulus patterns from two performance levels and three stimulus areas are applied to the online BCI system for each participant. Main results. The experimental result shows that the proposed scheme can implement a spatially-coded visual BCI system and realize satisfactory performance with imperceptible flicker. Offline analysis indicates that the classification accuracy and information transfer rate (ITR) are 89.69±8.75% and 24.35±7.09 bits min-1 with 3s data length under the 3/8 stimulus area. The online BCI system reaches an average classification accuracy of 87.50±9.13% with 3s data length, resulting in an ITR of 22.48±6.71 bits min-1 under the 3/8 stimulus area. Significance. This study proves the feasibility of using the sub-region's response to predict the joint region's response. It has the potential to extend to other frequency bands and lays a foundation for future research on more complex spatial coding methods.}, }
@article {pmid37467739, year = {2023}, author = {Berezutskaya, J and Freudenburg, ZV and Vansteensel, MJ and Aarnoutse, EJ and Ramsey, NF and van Gerven, MAJ}, title = {Direct speech reconstruction from sensorimotor brain activity with optimized deep learning models.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ace8be}, pmid = {37467739}, issn = {1741-2552}, abstract = {Development of brain-computer interface (BCI) technology is key for enabling communication in individuals who have lost the faculty of speech due to severe motor paralysis. A BCI control strategy that is gaining attention employs speech decoding from neural data. Recent studies have shown that a combination of direct neural recordings and advanced computational models can provide promising results. Understanding which decoding strategies deliver best and directly applicable results is crucial for advancing the field. In this paper, we optimized and validated a decoding approach based on speech reconstruction directly from high-density electrocorticography recordings from sensorimotor cortex during a speech production task. We show that 1) dedicated machine learning optimization of reconstruction models is key for achieving the best reconstruction performance; 2) individual word decoding in reconstructed speech achieves 92-100% accuracy (chance level is 8%); 3) direct reconstruction from sensorimotor brain activity produces intelligible speech. These results underline the need for model optimization in achieving best speech decoding results and highlight the potential that reconstruction-based speech decoding from sensorimotor cortex can offer for development of next-generation BCI technology for communication.}, }
@article {pmid37467714, year = {2023}, author = {Giraud, AL and Su, Y}, title = {Reconstructing language from brain signals and deconstructing adversarial thought-reading.}, journal = {Cell reports. Medicine}, volume = {4}, number = {7}, pages = {101115}, doi = {10.1016/j.xcrm.2023.101115}, pmid = {37467714}, issn = {2666-3791}, mesh = {*Reading ; Brain ; Language ; *Brain-Computer Interfaces ; }, abstract = {Tang et al.[1] report a noninvasive brain-computer interface (BCI) that reconstructs perceived and intended continuous language from semantic brain responses. The study offers new possibilities to radically facilitate neural speech decoder applications and addresses concerns about misuse in non-medical scenarios.}, }
@article {pmid37467641, year = {2023}, author = {Wang, M and Shao, W and Huang, S and Zhang, D}, title = {Hypergraph-regularized multimodal learning by graph diffusion for imaging genetics based Alzheimer's Disease diagnosis.}, journal = {Medical image analysis}, volume = {89}, number = {}, pages = {102883}, doi = {10.1016/j.media.2023.102883}, pmid = {37467641}, issn = {1361-8423}, abstract = {Recent studies show that multi-modal data fusion techniques combining information from diverse sources are helpful to diagnose and predict complex brain disorders. However, most existing diagnosis methods have only simply employed a feature combination strategy for multiple imaging and genetic data, ignoring the imaging phenotypes associated with the risk gene information. To this end, we present a hypergraph-regularized multimodal learning by graph diffusion (HMGD) for joint association learning and outcome prediction. Specifically, we first present a graph diffusion method for enhancing similarity measures among subjects given from multi-modality phenotypes, which fully uses multiple input similarity graphs and integrates them into a unified graph with valuable geometric structures among different imaging phenotypes. Then, we employ the unified graph to represent the high-order similarity relationships among subjects, and enforce a hypergraph-regularized term to incorporate both inter- and cross-modality information for selecting the imaging phenotypes associated with the risk single nucleotide polymorphism (SNP). Finally, a multi-kernel support vector machine (MK-SVM) is adopted to fuse such phenotypic features selected from different modalities for the final diagnosis and prediction. The proposed approach is experimentally explored on brain imaging genetic data of the Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets. Relevant results present that the proposed approach is superior to several competing algorithms, and realizes strong associations and discovers significant consistent and robust ROIs across different imaging phenotypes associated with the genetic risk biomarkers to guide disease interpretation and prediction.}, }
@article {pmid37466825, year = {2023}, author = {Poppe, C and Elger, BS}, title = {Brain-Computer Interfaces, Completely Locked-In State in Neurodegenerative Diseases, and End-of-Life Decisions.}, journal = {Journal of bioethical inquiry}, volume = {}, number = {}, pages = {}, pmid = {37466825}, issn = {1176-7529}, abstract = {In the future, policies surrounding end-of-life decisions will be faced with the question of whether competent people in a completely locked-in state should be enabled to make end-of-life decisions via brain-computer interfaces (BCI). This article raises ethical issues with acting through BCIs in the context of these decisions, specifically self-administration requirements within assisted suicide policies. We argue that enabling patients to end their life even once they have entered completely locked-in state might, paradoxically, prolong and uphold their quality of life.}, }
@article {pmid37465143, year = {2023}, author = {Shah, NP and Willsey, MS and Hahn, N and Kamdar, F and Avansino, DT and Hochberg, LR and Shenoy, KV and Henderson, JM}, title = {A brain-computer typing interface using finger movements.}, journal = {International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering}, volume = {2023}, number = {}, pages = {}, pmid = {37465143}, issn = {1948-3546}, support = {R01 DC014034/DC/NIDCD NIH HHS/United States ; U01 NS123101/NS/NINDS NIH HHS/United States ; }, abstract = {Intracortical brain computer interfaces (iBCIs) decode neural activity from the cortex and enable motor and communication prostheses, such as cursor control, handwriting and speech, for people with paralysis. This paper introduces a new iBCI communication prosthesis using a 3D keyboard interface for typing using continuous, closed loop movement of multiple fingers. A participant-specific BCI keyboard prototype was developed for a BrainGate2 clinical trial participant (T5) using neural recordings from the hand-knob area of the left premotor cortex. We assessed the relative decoding accuracy of flexion/extension movements of individual single fingers (5 degrees of freedom (DOF)) vs. three groups of fingers (thumb, index-middle, and ring-small fingers, 3 DOF). Neural decoding using 3 independent DOF was more accurate (95%) than that using 5 DOF (76%). A virtual keyboard was then developed where each finger group moved along a flexion-extension arc to acquire targets that corresponded to English letters and symbols. The locations of these letter/symbols were optimized using natural language statistics, resulting in an approximately a 2× reduction in distance traveled by fingers on average compared to a random keyboard layout. This keyboard was tested using a simple real-time closed loop decoder enabling T5 to type with 31 symbols at 90% accuracy and approximately 2.3 sec/symbol (excluding a 2 second hold time) on average.}, }
@article {pmid37464883, year = {2023}, author = {Li, G and Jiang, S and Meng, J and Wu, Z and Jiang, H and Fan, Z and Hu, J and Sheng, X and Zhang, D and Schalk, G and Chen, L and Zhu, X}, title = {Spatio-temporal evolution of human neural activity during visually cued hand movements.}, journal = {Cerebral cortex (New York, N.Y. : 1991)}, volume = {}, number = {}, pages = {}, doi = {10.1093/cercor/bhad242}, pmid = {37464883}, issn = {1460-2199}, support = {52105030//National Natural Science Foundation of China/ ; 2018SHZDZX01//Shanghai Municipal Science and Technology Major Project/ ; //ZJLab/ ; AH0200003//Medical and Engineering Cross Foundation of Shanghai Jiao Tong University/ ; }, abstract = {Making hand movements in response to visual cues is common in daily life. It has been well known that this process activates multiple areas in the brain, but how these neural activations progress across space and time remains largely unknown. Taking advantage of intracranial electroencephalographic (iEEG) recordings using depth and subdural electrodes from 36 human subjects using the same task, we applied single-trial and cross-trial analyses to high-frequency iEEG activity. The results show that the neural activation was widely distributed across the human brain both within and on the surface of the brain, and focused specifically on certain areas in the parietal, frontal, and occipital lobes, where parietal lobes present significant left lateralization on the activation. We also demonstrate temporal differences across these brain regions. Finally, we evaluated the degree to which the timing of activity within these regions was related to sensory or motor function. The findings of this study promote the understanding of task-related neural processing of the human brain, and may provide important insights for translational applications.}, }
@article {pmid37463076, year = {2023}, author = {Li, P and Gao, X and Li, C and Yi, C and Huang, W and Si, Y and Li, F and Cao, Z and Tian, Y and Xu, P}, title = {Granger Causal Inference Based on Dual Laplacian Distribution and Its Application to MI-BCI Classification.}, journal = {IEEE transactions on neural networks and learning systems}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TNNLS.2023.3292179}, pmid = {37463076}, issn = {2162-2388}, abstract = {Granger causality-based effective brain connectivity provides a powerful tool to probe the neural mechanism for information processing and the potential features for brain computer interfaces. However, in real applications, traditional Granger causality is prone to the influence of outliers, such as inevitable ocular artifacts, resulting in unreasonable brain linkages and the failure to decipher inherent cognition states. In this work, motivated by constructing the sparse causality brain networks under the strong physiological outlier noise conditions, we proposed a dual Laplacian Granger causality analysis (DLap-GCA) by imposing Laplacian distributions on both model parameters and residuals. In essence, the first Laplacian assumption on residuals will resist the influence of outliers in electroencephalogram (EEG) on causality inference, and the second Laplacian assumption on model parameters will sparsely characterize the intrinsic interactions among multiple brain regions. Through simulation study, we quantitatively verified its effectiveness in suppressing the influence of complex outliers, the stable capacity for model estimation, and sparse network inference. The application to motor-imagery (MI) EEG further reveals that our method can effectively capture the inherent hemispheric lateralization of MI tasks with sparse patterns even under strong noise conditions. The MI classification based on the network features derived from the proposed approach shows higher accuracy than other existing traditional approaches, which is attributed to the discriminative network structures being captured in a timely manner by DLap-GCA even under the single-trial online condition. Basically, these results consistently show its robustness to the influence of complex outliers and the capability of characterizing representative brain networks for cognition information processing, which has the potential to offer reliable network structures for both cognitive studies and future brain-computer interface (BCI) realization.}, }
@article {pmid37461446, year = {2023}, author = {Bashford, L and Rosenthal, I and Kellis, S and Bjånes, D and Pejsa, K and Brunton, BW and Andersen, RA}, title = {Neural subspaces of imagined movements in parietal cortex remain stable over several years in humans.}, journal = {bioRxiv : the preprint server for biology}, volume = {}, number = {}, pages = {}, doi = {10.1101/2023.07.05.547767}, pmid = {37461446}, abstract = {A crucial goal in brain-machine interfacing is long-term stability of neural decoding performance, ideally without regular retraining. Here we demonstrate stable neural decoding over several years in two human participants, achieved by latent subspace alignment of multi-unit intracortical recordings in posterior parietal cortex. These results can be practically applied to significantly expand the longevity and generalizability of future movement decoding devices.}, }
@article {pmid37461360, year = {2023}, author = {Comita, LS and Aguilar, S and Hubbell, SP and Pérez, R}, title = {Long-term seedling and small sapling census data from the Barro Colorado Island 50 ha Forest Dynamics Plot, Panama.}, journal = {Ecology}, volume = {}, number = {}, pages = {e4140}, doi = {10.1002/ecy.4140}, pmid = {37461360}, issn = {1939-9170}, abstract = {Tropical forests are well known for their high woody plant diversity. Processes occurring at early life stages are thought to play a critical role in maintaining this high diversity and shaping the composition of tropical tree communities. To evaluate hypothesized mechanisms promoting tropical tree species coexistence and influencing composition, we initiated a census of woody seedlings and small saplings in the permanent 50 ha Forest Dynamics Plot (FDP) on Barro Colorado Island (BCI), Panama. Situated in old-growth, lowland tropical moist forest, the BCI FDP was originally established in 1980 to monitor trees and shrubs ≥1 cm diameter at 1.3 m above ground (dbh) at ca. 5-yr intervals. However, critical data on the dynamics occurring at earlier life stages were initially lacking. Therefore, in 2001 we established a 1-m[2] seedling plot in the center of every 5 x 5 m section of the BCI FDP. All freestanding woody individuals ≥20 cm tall and <1 cm dbh (hereafter referred to as seedlings) were tagged, mapped, measured, and identified to species in 19,313 1-m[2] seedling plots. Because seedling dynamics are rapid, we censused these seedling plots every 1-2 years. Here we present data from the 14 censuses of these seedling plots conducted between the initial census in 2001 to the most recent census, in 2018. This data set includes nearly 1M observations of ~185,000 individuals of >400 tree, shrub, and liana species. These data will permit spatially-explicit analyses of seedling distributions, recruitment, growth, and survival for hundreds of woody plant species. In addition, the data presented here can be linked to openly-available, long-term data on the dynamics of trees and shrubs ≥1cm dbh in the BCI FDP, as well as existing data sets from the site on climate, canopy structure, phylogenetic relatedness, functional traits, soil nutrients, and topography. This data set can be freely used for non-commercial purposes; we request that users of these data cite this data paper in all publications resulting from the use of this data set. This article is protected by copyright. All rights reserved.}, }
@article {pmid37460730, year = {2023}, author = {Si, X and Zhou, Y and Li, S and Zhang, X and Han, S and Xiang, S and Ming, D}, title = {Brain-Computer Interfaces in Visualized Medicine.}, journal = {Advances in experimental medicine and biology}, volume = {1199}, number = {}, pages = {127-153}, pmid = {37460730}, issn = {0065-2598}, mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; }, abstract = {The brain-computer interface (BCI), also known as a brain-machine interface (BMI), has attracted extensive attention in biomedical applications. More importantly, BCI technologies have substantially revolutionized early predictions, diagnostic techniques, and rehabilitation strategies addressing acute diseases because of BCI's innovations and clinical translations. Therefore, in this chapter, a comprehensive description of the basic concepts of BCI will be exhibited, and various visualization techniques employed in BCI's medical applications will be discussed.}, }
@article {pmid37459853, year = {2023}, author = {Meng, K and Goodarzy, F and Kim, E and Park, YJ and Kim, JS and Cook, MJ and Chung, CK and Grayden, DB}, title = {Continuous synthesis of artificial speech sounds from human cortical surface recordings during silent speech production.}, journal = {Journal of neural engineering}, volume = {}, number = {}, pages = {}, doi = {10.1088/1741-2552/ace7f6}, pmid = {37459853}, issn = {1741-2552}, abstract = {OBJECTIVE: Brain-computer interfaces can restore various forms of communication in paralyzed patients who have lost their ability to articulate intelligible speech. This study aimed to demonstrate the feasibility of closed-loop synthesis of artificial speech sounds from human cortical surface recordings during silent speech production.<br><br>APPROACH: Ten participants with intractable epilepsy were temporarily implanted with intracranial electrode arrays over cortical surfaces. A decoding model that predicted audible outputs directly from patient-specific neural feature inputs was trained during overt word reading and immediately tested with overt, mimed and imagined word reading. Predicted outputs were later assessed objectively against corresponding voice recordings and subjectively through human perceptual judgments.<br><br>MAIN RESULTS: Artificial speech sounds were successfully synthesized during overt and mimed utterances by two participants with some coverage of the precentral gyrus. About a third of these sounds were correctly identified by na&#xef;ve listeners in two-alternative forced-choice tasks. A similar outcome could not be achieved during imagined utterances by any of the participants. However, neural feature contribution analyses suggested the presence of exploitable activation patterns during imagined speech in the postcentral gyrus and the superior temporal gyrus. In future work, a more comprehensive coverage of cortical surfaces, including posterior parts of the middle frontal gyrus and the inferior frontal gyrus, could improve synthesis performance during imagined speech.<br><br>SIGNIFICANCE: As the field of speech neuroprostheses is rapidly moving toward clinical trials, this study addressed important considerations about task instructions and brain coverage when conducting research on silent speech with non-target participants.}, }
@article {pmid37457015, year = {2023}, author = {Li, Y and Chen, B and Wang, G and Yoshimura, N and Koike, Y}, title = {Partial maximum correntropy regression for robust electrocorticography decoding.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1213035}, pmid = {37457015}, issn = {1662-4548}, abstract = {The Partial Least Square Regression (PLSR) method has shown admirable competence for predicting continuous variables from inter-correlated electrocorticography signals in the brain-computer interface. However, PLSR is essentially formulated with the least square criterion, thus, being considerably prone to the performance deterioration caused by the brain recording noises. To address this problem, this study aims to propose a new robust variant for PLSR. To this end, the maximum correntropy criterion (MCC) is utilized to propose a new robust implementation of PLSR, called Partial Maximum Correntropy Regression (PMCR). The half-quadratic optimization is utilized to calculate the robust projectors for the dimensionality reduction, and the regression coefficients are optimized by a fixed-point optimization method. The proposed PMCR is evaluated with a synthetic example and a public electrocorticography dataset under three performance indicators. For the synthetic example, PMCR realized better prediction results compared with the other existing methods. PMCR could also abstract valid information with a limited number of decomposition factors in a noisy regression scenario. For the electrocorticography dataset, PMCR achieved superior decoding performance in most cases, and also realized the minimal neurophysiological pattern deterioration with the interference of the noises. The experimental results demonstrate that, the proposed PMCR could outperform the existing methods in a noisy, inter-correlated, and high-dimensional decoding task. PMCR could alleviate the performance degradation caused by the adverse noises and ameliorate the electrocorticography decoding robustness for the brain-computer interface.}, }
@article {pmid37457014, year = {2023}, author = {Li, R and Zhang, Y and Fan, G and Li, Z and Li, J and Fan, S and Lou, C and Liu, X}, title = {Design and implementation of high sampling rate and multichannel wireless recorder for EEG monitoring and SSVEP response detection.}, journal = {Frontiers in neuroscience}, volume = {17}, number = {}, pages = {1193950}, pmid = {37457014}, issn = {1662-4548}, abstract = {INTRODUCTION: The collection and process of human brain activity signals play an essential role in developing brain-computer interface (BCI) systems. A portable electroencephalogram (EEG) device has become an important tool for monitoring brain activity and diagnosing mental diseases. However, the miniaturization, portability, and scalability of EEG recorder are the current bottleneck in the research and application of BCI.<br><br>METHODS: For scalp EEG and other applications, the current study designs a 32-channel EEG recorder with a sampling rate up to 30 kHz and 16-bit accuracy, which can meet both the demands of scalp and intracranial EEG signal recording. A fully integrated electrophysiology microchip RHS2116 controlled by FPGA is employed to build the EEG recorder, and the design meets the requirements of high sampling rate, high transmission rate and channel extensive.<br><br>RESULTS: The experimental results show that the developed EEG recorder provides a maximum 30 kHz sampling rate and 58 Mbps wireless transmission rate. The electrophysiological experiments were performed on scalp and intracranial EEG collection. An inflatable helmet with adjustable contact impedance was designed, and the pressurization can improve the SNR by approximately 4 times, the average accuracy of steady-state visual evoked potential (SSVEP) was 93.12%. Animal experiments were also performed on rats, and spike activity was captured successfully.<br><br>CONCLUSION: The designed multichannel wireless EEG collection system is simple and comfort, the helmet-EEG recorder can capture the bioelectric signals without noticeable interference, and it has high measurement performance and great potential for practical application in BCI systems.}, }
@article {pmid37456795, year = {2023}, author = {Wolpaw, JR and Thompson, AK}, title = {Enhancing neurorehabilitation by targeting beneficial plasticity.}, journal = {Frontiers in rehabilitation sciences}, volume = {4}, number = {}, pages = {1198679}, pmid = {37456795}, issn = {2673-6861}, abstract = {Neurorehabilitation is now one of the most exciting areas in neuroscience. Recognition that the central nervous system (CNS) remains plastic through life, new understanding of skilled behaviors (skills), and novel methods for engaging and guiding beneficial plasticity combine to provide unprecedented opportunities for restoring skills impaired by CNS injury or disease. The substrate of a skill is a distributed network of neurons and synapses that changes continually through life to ensure that skill performance remains satisfactory as new skills are acquired, and as growth, aging, and other life events occur. This substrate can extend from cortex to spinal cord. It has recently been given the name "heksor." In this new context, the primary goal of rehabilitation is to enable damaged heksors to repair themselves so that their skills are once again performed well. Skill-specific practice, the mainstay of standard therapy, often fails to optimally engage the many sites and kinds of plasticity available in the damaged CNS. New noninvasive technology-based interventions can target beneficial plasticity to critical sites in damaged heksors; these interventions may thereby enable much wider beneficial plasticity that enhances skill recovery. Targeted-plasticity interventions include operant conditioning of a spinal reflex or corticospinal motor evoked potential (MEP), paired-pulse facilitation of corticospinal connections, and brain-computer interface (BCI)-based training of electroencephalographic (EEG) sensorimotor rhythms. Initial studies in people with spinal cord injury, stroke, or multiple sclerosis show that these interventions can enhance skill recovery beyond that achieved by skill-specific practice alone. After treatment ends, the repaired heksors maintain the benefits.}, }
@article {pmid37452047, year = {2023}, author = {Wang, Z and Shi, N and Zhang, Y and Zheng, N and Li, H and Jiao, Y and Cheng, J and Wang, Y and Zhang, X and Chen, Y and Chen, Y and Wang, H and Xie, T and Wang, Y and Ma, Y and Gao, X and Feng, X}, title = {Conformal in-ear bioelectronics for visual and auditory brain-computer interfaces.}, journal = {Nature communications}, volume = {14}, number = {1}, pages = {4213}, pmid = {37452047}, issn = {2041-1723}, support = {11921002//National Natural Science Foundation of China (National Science Foundation of China)/ ; U20A6001//National Natural Science Foundation of China (National Science Foundation of China)/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Evoked Potentials, Visual ; Electroencephalography ; Calibration ; Language ; Photic Stimulation ; Algorithms ; }, abstract = {Brain-computer interfaces (BCIs) have attracted considerable attention in motor and language rehabilitation. Most devices use cap-based non-invasive, headband-based commercial products or microneedle-based invasive approaches, which are constrained for inconvenience, limited applications, inflammation risks and even irreversible damage to soft tissues. Here, we propose in-ear visual and auditory BCIs based on in-ear bioelectronics, named as SpiralE, which can adaptively expand and spiral along the auditory meatus under electrothermal actuation to ensure conformal contact. Participants achieve offline accuracies of 95% in 9-target steady state visual evoked potential (SSVEP) BCI classification and type target phrases successfully in a calibration-free 40-target online SSVEP speller experiment. Interestingly, in-ear SSVEPs exhibit significant 2[nd] harmonic tendencies, indicating that in-ear sensing may be complementary for studying harmonic spatial distributions in SSVEP studies. Moreover, natural speech auditory classification accuracy can reach 84% in cocktail party experiments. The SpiralE provides innovative concepts for designing 3D flexible bioelectronics and assists the development of biomedical engineering and neural monitoring.}, }
@article {pmid37450357, year = {2023}, author = {Yamamoto, MS and Sadatnejad, K and Tanaka, T and Islam, MR and Dehais, F and Tanaka, Y and Lotte, F}, title = {Modeling complex EEG data distribution on the Riemannian manifold toward outlier detection and multimodal classification.}, journal = {IEEE transactions on bio-medical engineering}, volume = {PP}, number = {}, pages = {}, doi = {10.1109/TBME.2023.3295769}, pmid = {37450357}, issn = {1558-2531}, abstract = {OBJECTIVE: The usage of Riemannian geometry for Brain-computer interfaces (BCIs) has gained momentum in re-cent years. Most of the machine learning techniques proposed for Riemannian BCIs consider the data distribution on a man-ifold to be unimodal. However, the distribution is likely to be multimodal rather than unimodal since high-data variability is a crucial limitation of electroencephalography (EEG). In this paper, we propose a novel data modeling method for considering complex data distributions on a Riemannian manifold of EEG covariance matrices, aiming to improve BCI reliability.<br><br>METHODS: Our method, Riemannian spectral clustering (RiSC), represents EEG covariance matrix distribution on a manifold using a graph with proposed sim-ilarity measurement based on geodesic distances, then clusters the graph nodes through spectral clustering. This allows flexibility to model both a unimodal and a multimodal distribution on a manifold. RiSC can be used as a basis to design an outlier detector named outlier detection Riemannian spectral clustering (oden-RiSC) and a multimodal classifier named multimodal classifier Riemannian spectral clustering (mcRiSC). All required parameters of odenRiSC/mcRiSC are selected in data-driven manner. More-over, there is no need to pre-set a threshold for outlier detection and the number of modes for multimodal classification.<br><br>RESULTS: The experimental evaluation revealed odenRiSC can detect EEG outliers more accurately than existing methods and mcRiSC out-performed the standard unimodal classifier, especially on high-variability datasets.<br><br>CONCLUSION: odenRiSC/mcRiSC are anticipated to contribute to making real-life BCIs outside labs and neuroer-gonomics applications more robust.<br><br>SIGNIFICANCE: RiSC can work as a robust EEG outlier detector and multimodal classifier.}, }
@article {pmid37450213, year = {2023}, author = {Adama, S and Bogdan, M}, title = {Assessing consciousness in patients with disorders of consciousness using soft-clustering.}, journal = {Brain informatics}, volume = {10}, number = {1}, pages = {16}, pmid = {37450213}, issn = {2198-4018}, abstract = {Consciousness is something we experience in our everyday life, more especially between the time we wake up in the morning and go to sleep at night, but also during the rapid eye movement (REM) sleep stage. Disorders of consciousness (DoC) are states in which a person's consciousness is damaged, possibly after a traumatic brain injury. Completely locked-in syndrome (CLIS) patients, on the other hand, display covert states of consciousness. Although they appear unconscious, their cognitive functions are mostly intact. Only, they cannot externally display it due to their quadriplegia and inability to speak. Determining these patients' states constitutes a challenging task. The ultimate goal of the approach presented in this paper is to assess these CLIS patients consciousness states. EEG data from DoC patients are used here first, under the assumption that if the proposed approach is able to accurately assess their consciousness states, it will assuredly do so on CLIS patients too. This method combines different sets of features consisting of spectral, complexity and connectivity measures in order to increase the probability of correctly estimating their consciousness levels. The obtained results showed that the proposed approach was able to correctly estimate several DoC patients' consciousness levels. This estimation is intended as a step prior attempting to communicate with them, in order to maximise the efficiency of brain-computer interfaces (BCI)-based communication systems.}, }
@article {pmid37448967, year = {2023}, author = {Zhou, Y and Yang, H and Wang, X and Yang, H and Sun, K and Zhou, Z and Sun, L and Zhao, J and Tao, TH and Wei, X}, title = {A mosquito mouthpart-like bionic neural probe.}, journal = {Microsystems &amp; nanoengineering}, volume = {9}, number = {}, pages = {88}, pmid = {37448967}, issn = {2055-7434}, abstract = {Advancements in microscale electrode technology have revolutionized the field of neuroscience and clinical applications by offering high temporal and spatial resolution of recording and stimulation. Flexible neural probes, with their mechanical compliance to brain tissue, have been shown to be superior to rigid devices in terms of stability and longevity in chronic recordings. Shuttle devices are commonly used to assist flexible probe implantation; however, the protective membrane of the brain still makes penetration difficult. Hidden damage to brain vessels during implantation is a significant risk. Inspired by the anatomy of the mosquito mouthparts, we present a biomimetic neuroprobe system that integrates high-sensitivity sensors with a high-fidelity multichannel flexible electrode array. This customizable system achieves distributed and minimally invasive implantation across brain regions. Most importantly, the system's nonvisual monitoring capability provides an early warning detection for intracranial soft tissues, such as vessels, reducing the potential for injury during implantation. The neural probe system demonstrates exceptional sensitivity and adaptability to environmental stimuli, as well as outstanding performance in postoperative and chronic recordings. These findings suggest that our biomimetic neural-probe device offers promising potential for future applications in neuroscience and brain-machine interfaces. A mosquito mouthpart-like bionic neural probe consisting of a highly sensitive tactile sensor module, a flexible microelectrode array, and implanted modules that mimic the structure of mosquito mouthparts. The system enables distributed implantation of electrode arrays across multiple brain regions while making the implantation minimally invasive and avoiding additional dural removal. The tactile sensor array can monitor the implantation process to achieve early warning of vascular damage. The excellent postoperative short-term recording performance and long-term neural activity tracking ability demonstrate that the system is a promising tool in the field of brain-computer interfaces.}, }
@article {pmid37447926, year = {2023}, author = {White, J and Power, SD}, title = {k-Fold Cross-Validation Can Significantly Over-Estimate True Classification Accuracy in Common EEG-Based Passive BCI Experimental Designs: An Empirical Investigation.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {13}, pages = {}, pmid = {37447926}, issn = {1424-8220}, support = {RGPIN-2016-04210//Natural Sciences and Engineering Research Council/ ; }, mesh = {*Brain-Computer Interfaces ; Electroencephalography/methods ; Research Design ; Algorithms ; }, abstract = {In passive BCI studies, a common approach is to collect data from mental states of interest during relatively long trials and divide these trials into shorter "epochs" to serve as individual samples in classification. While it is known that using k-fold cross-validation (CV) in this scenario can result in unreliable estimates of mental state separability (due to autocorrelation in the samples derived from the same trial), k-fold CV is still commonly used and reported in passive BCI studies. What is not known is the extent to which k-fold CV misrepresents true mental state separability. This makes it difficult to interpret the results of studies that use it. Furthermore, if the seriousness of the problem were clearly known, perhaps more researchers would be aware that they should avoid it. In this work, a novel experiment explored how the degree of correlation among samples within a class affects EEG-based mental state classification accuracy estimated by k-fold CV. Results were compared to a ground-truth (GT) accuracy and to "block-wise" CV, an alternative to k-fold which is purported to alleviate the autocorrelation issues. Factors such as the degree of true class separability and the feature set and classifier used were also explored. The results show that, under some conditions, k-fold CV inflated the GT classification accuracy by up to 25%, but block-wise CV underestimated the GT accuracy by as much as 11%. It is our recommendation that the number of samples derived from the same trial should be reduced whenever possible in single-subject analysis, and that both the k-fold and block-wise CV results are reported.}, }
@article {pmid37447852, year = {2023}, author = {Soangra, R and Smith, JA and Rajagopal, S and Yedavalli, SVR and Anirudh, ER}, title = {Classifying Unstable and Stable Walking Patterns Using Electroencephalography Signals and Machine Learning Algorithms.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {13}, pages = {}, pmid = {37447852}, issn = {1424-8220}, support = {R15 HD110941/HD/NICHD NIH HHS/United States ; }, mesh = {Adult ; Humans ; *Algorithms ; Electroencephalography/methods ; Neural Networks, Computer ; Walking ; *Brain-Computer Interfaces ; Machine Learning ; }, abstract = {Analyzing unstable gait patterns from Electroencephalography (EEG) signals is vital to develop real-time brain-computer interface (BCI) systems to prevent falls and associated injuries. This study investigates the feasibility of classification algorithms to detect walking instability utilizing EEG signals. A 64-channel Brain Vision EEG system was used to acquire EEG signals from 13 healthy adults. Participants performed walking trials for four different stable and unstable conditions: (i) normal walking, (ii) normal walking with medial-lateral perturbation (MLP), (iii) normal walking with dual-tasking (Stroop), (iv) normal walking with center of mass visual feedback. Digital biomarkers were extracted using wavelet energy and entropies from the EEG signals. Algorithms like the ChronoNet, SVM, Random Forest, gradient boosting and recurrent neural networks (LSTM) could classify with 67 to 82% accuracy. The classification results show that it is possible to accurately classify different gait patterns (from stable to unstable) using EEG-based digital biomarkers. This study develops various machine-learning-based classification models using EEG datasets with potential applications in detecting unsteady gait neural signals and intervening by preventing falls and injuries.}, }
@article {pmid37447849, year = {2023}, author = {Peksa, J and Mamchur, D}, title = {State-of-the-Art on Brain-Computer Interface Technology.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {13}, pages = {}, pmid = {37447849}, issn = {1424-8220}, mesh = {*Electroencephalography ; *Brain-Computer Interfaces ; Brain ; Algorithms ; Technology ; }, abstract = {This paper provides a comprehensive overview of the state-of-the-art in brain-computer interfaces (BCI). It begins by providing an introduction to BCIs, describing their main operation principles and most widely used platforms. The paper then examines the various components of a BCI system, such as hardware, software, and signal processing algorithms. Finally, it looks at current trends in research related to BCI use for medical, educational, and other purposes, as well as potential future applications of this technology. The paper concludes by highlighting some key challenges that still need to be addressed before widespread adoption can occur. By presenting an up-to-date assessment of the state-of-the-art in BCI technology, this paper will provide valuable insight into where this field is heading in terms of progress and innovation.}, }
@article {pmid37447780, year = {2023}, author = {Craik, A and González-España, JJ and Alamir, A and Edquilang, D and Wong, S and Sánchez Rodríguez, L and Feng, J and Francisco, GE and Contreras-Vidal, JL}, title = {Design and Validation of a Low-Cost Mobile EEG-Based Brain-Computer Interface.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {13}, pages = {}, pmid = {37447780}, issn = {1424-8220}, support = {1827769//National Science Foundation/ ; 1650536//National Science Foundation/ ; }, mesh = {Humans ; *Brain-Computer Interfaces ; Reproducibility of Results ; Electroencephalography ; Brain ; Eye Movements ; }, abstract = {Objective: We designed and validated a wireless, low-cost, easy-to-use, mobile, dry-electrode headset for scalp electroencephalography (EEG) recordings for closed-loop brain-computer (BCI) interface and internet-of-things (IoT) applications. Approach: The EEG-based BCI headset was designed from commercial off-the-shelf (COTS) components using a multi-pronged approach that balanced interoperability, cost, portability, usability, form factor, reliability, and closed-loop operation. Main Results: The adjustable headset was designed to accommodate 90% of the population. A patent-pending self-positioning dry electrode bracket allowed for vertical self-positioning while parting the user's hair to ensure contact of the electrode with the scalp. In the current prototype, five EEG electrodes were incorporated in the electrode bracket spanning the sensorimotor cortices bilaterally, and three skin sensors were included to measure eye movement and blinks. An inertial measurement unit (IMU) provides monitoring of head movements. The EEG amplifier operates with 24-bit resolution up to 500 Hz sampling frequency and can communicate with other devices using 802.11 b/g/n WiFi. It has high signal-to-noise ratio (SNR) and common-mode rejection ratio (CMRR) (121 dB and 110 dB, respectively) and low input noise. In closed-loop BCI mode, the system can operate at 40 Hz, including real-time adaptive noise cancellation and 512 MB of processor memory. It supports LabVIEW as a backend coding language and JavaScript (JS), Cascading Style Sheets (CSS), and HyperText Markup Language (HTML) as front-end coding languages and includes training and optimization of support vector machine (SVM) neural classifiers. Extensive bench testing supports the technical specifications and human-subject pilot testing of a closed-loop BCI application to support upper-limb rehabilitation and provides proof-of-concept validation for the device's use at both the clinic and at home. Significance: The usability, interoperability, portability, reliability, and programmability of the proposed wireless closed-loop BCI system provides a low-cost solution for BCI and neurorehabilitation research and IoT applications.}, }
@article {pmid37447728, year = {2023}, author = {Gracia, DI and Ortiz, M and Candela, T and Iáñez, E and Sánchez, RM and Díaz, C and Azorín, JM}, title = {Design and Evaluation of a Potential Non-Invasive Neurostimulation Strategy for Treating Persistent Anosmia in Post-COVID-19 Patients.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {13}, pages = {}, pmid = {37447728}, issn = {1424-8220}, support = {GVA COVID19/2021/062//Generalitat Valenciana/ ; }, mesh = {Humans ; *COVID-19/complications/therapy ; Anosmia/therapy/etiology ; SARS-CoV-2 ; *Olfaction Disorders/therapy/epidemiology/etiology ; *Transcranial Direct Current Stimulation ; Smell/physiology ; }, abstract = {A new pandemic was declared at the end of 2019 because of coronavirus disease 2019 (COVID-19). One of the effects of COVID-19 infection is anosmia (i.e., a loss of smell). Unfortunately, this olfactory dysfunction is persistent in around 5% of the world's population, and there is not an effective treatment for it yet. The aim of this paper is to describe a potential non-invasive neurostimulation strategy for treating persistent anosmia in post-COVID-19 patients. In order to design the neurostimulation strategy, 25 subjects who experienced anosmia due to COVID-19 infection underwent an olfactory assessment while their electroencephalographic (EEG) signals were recorded. These signals were used to investigate the activation of brain regions during the olfactory process and identify which regions would be suitable for neurostimulation. Afterwards, 15 subjects participated in the evaluation of the neurostimulation strategy, which was based on applying transcranial direct current stimulation (tDCS) in selected brain regions related to olfactory function. The results showed that subjects with lower scores in the olfactory assessment obtained greater improvement than the other subjects. Thus, tDCS could be a promising option for people who have not fully regained their sense of smell following COVID-19 infection.}, }
@article {pmid37447686, year = {2023}, author = {Arpaia, P and Coyle, D and Esposito, A and Natalizio, A and Parvis, M and Pesola, M and Vallefuoco, E}, title = {Paving the Way for Motor Imagery-Based Tele-Rehabilitation through a Fully Wearable BCI System.}, journal = {Sensors (Basel, Switzerland)}, volume = {23}, number = {13}, pages = {}, pmid = {37447686}, issn = {1424-8220}, mesh = {Humans ; *Brain-Computer Interfaces ; *Telerehabilitation ; Electroencephalography/methods ; Imagery, Psychotherapy/methods ; *Wearable Electronic Devices ; }, abstract = {The present study introduces a brain-computer interface designed and prototyped to be wearable and usable in daily life. Eight dry electroencephalographic sensors were adopted to acquire the brain activity associated with motor imagery. Multimodal feedback in extended reality was exploited to improve the online detection of neurological phenomena. Twenty-seven healthy subjects used the proposed system in five sessions to investigate the effects of feedback on motor i