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Bibliography on: Brain-Computer Interface

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ESP: PubMed Auto Bibliography 28 Mar 2024 at 01:40 Created: 

Brain-Computer Interface

Wikipedia: A brain–computer interface (BCI), sometimes called a neural control interface (NCI), mind–machine interface (MMI), direct neural interface (DNI), or brain–machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. Research on BCIs began in the 1970s at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. The papers published after this research also mark the first appearance of the expression brain–computer interface in scientific literature. BCI-effected sensory input: Due to the cortical plasticity of the brain, signals from implanted prostheses can, after adaptation, be handled by the brain like natural sensor or effector channels. Following years of animal experimentation, the first neuroprosthetic devices implanted in humans appeared in the mid-1990s. BCI-effected motor output: When artificial intelligence is used to decode neural activity, then send that decoded information to some kind of effector device, BCIs have the potential to restore communication to people who have lost the ability to move or speak. To date, the focus has largely been on motor skills such as reaching or grasping. However, in May of 2021 a study showed that an AI/BCI system could be use to translate thoughts about handwriting into the output of legible characters at a usable rate (90 characters per minute with 94% accuracy).

Created with PubMed® Query: (bci OR (brain-computer OR brain-machine OR mind-machine OR neural-control interface) NOT 26799652[PMID] ) NOT pmcbook NOT ispreviousversion

Citations The Papers (from PubMed®)

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RevDate: 2024-03-25

Deng L, Wei W, Qiao C, et al (2024)

Dynamic aberrances of substantia nigra-relevant coactivation patterns in first-episode treatment-naïve patients with schizophrenia.

Psychological medicine pii:S0033291724000655 [Epub ahead of print].

BACKGROUND: Although dopaminergic disturbances are well-known in schizophrenia, the understanding of dopamine-related brain dynamics remains limited. This study investigates the dynamic coactivation patterns (CAPs) associated with the substantia nigra (SN), a key dopaminergic nucleus, in first-episode treatment-naïve patients with schizophrenia (FES).

METHODS: Resting-state fMRI data were collected from 84 FES and 94 healthy controls (HCs). Frame-wise clustering was implemented to generate CAPs related to SN activation or deactivation. Connectome features of each CAP were derived using an edge-centric method. The occurrence for each CAP and the balance ratio for antagonistic CAPs were calculated and compared between two groups, and correlations between temporal dynamic metrics and symptom burdens were explored.

RESULTS: Functional reconfigurations in CAPs exhibited significant differences between the activation and deactivation states of SN. During SN activation, FES more frequently recruited a CAP characterized by activated default network, language network, control network, and the caudate, compared to HCs (F = 8.54, FDR-p = 0.030). Moreover, FES displayed a tilted balance towards a CAP featuring SN-coactivation with the control network, caudate, and thalamus, as opposed to its antagonistic CAP (F = 7.48, FDR-p = 0.030). During SN deactivation, FES exhibited increased recruitment of a CAP with activated visual and dorsal attention networks but decreased recruitment of its opposing CAP (F = 6.58, FDR-p = 0.034).

CONCLUSION: Our results suggest that neuroregulatory dysfunction in dopaminergic pathways involving SN potentially mediates aberrant time-varying functional reorganizations in schizophrenia. This finding enriches the dopamine hypothesis of schizophrenia from the perspective of brain dynamics.

RevDate: 2024-03-23

Jeong HJ, Lee H, Choo MS, et al (2024)

Effect of detrusor underactivity on surgical outcomes of holmium laser enucleation of the prostate.

BJU international [Epub ahead of print].

OBJECTIVE: To evaluate the effect of detrusor underactivity (DUA) on the postoperative outcomes of holmium laser enucleation of the prostate (HoLEP) in patients with benign prostatic hyperplasia (BPH).

PATIENTS AND METHODS: Patients with BPH who underwent HoLEP between January 2018 and December 2022 were enrolled in this prospective database study. Patients were divided into DUA (bladder contractility index [BCI] <100) and non-DUA (BCI ≥100) groups. Objective (maximum urinary flow rate [Qmax], post-void residual urine volume [PVR]) and subjective outcomes (International Prostate Symptom Score [IPSS], Overactive Bladder Symptom Score [OABSS], satisfaction with treatment question [STQ], overall response assessment [ORA], and willingness to undergo surgery question [WUSQ]) were compared between the two groups before surgery, and at 3 and 6 months after HoLEP.

RESULTS: A total of 689 patients, with a mean (standard deviation [SD]) age of 69.8 (7.1) years, were enrolled. The mean (SD) BCI in the non-DUA (325 [47.2%]) and DUA (364 [52.8%]) groups was 123.4 (21.4) and 78.6 (14.2), respectively. Both objective (Qmax and PVR) and subjective (IPSS, IPSS-quality of life, and OABSS) outcomes after surgery significantly improved in both groups. The Qmax was lower in the DUA than in the non-DUA group postoperatively. At 6 months postoperatively, the total IPSS was higher in the DUA than in the non-DUA group. There were no significant differences in surgical complications between the two groups. Responses to the STQ, ORA, and WUSQ at 6 months postoperatively demonstrated that the patients were satisfied with the surgery (90.5% in the DUA group; 95.2% in the non-DUA group), their symptoms improved with surgery (95.9% in the DUA group; 100.0% in the non-DUA group), and they were willing to undergo surgery again (95.9% in the DUA group; 97.9% in the non-DUA group). There were no significant differences in the responses to the STQ and WUSQ between the two groups.

CONCLUSION: Our midterm results demonstrated that patients with BPH and DUA showed minimal differences in clinical outcomes after HoLEP compared to those without DUA. The overall satisfaction was high in the DUA group.

RevDate: 2024-03-25
CmpDate: 2024-03-25

Zou P, Liu C, Zhang Y, et al (2024)

Transurethral surgical treatment for benign prostatic hyperplasia with detrusor underactivity: a systematic review and meta-analysis.

Systematic reviews, 13(1):93.

BACKGROUND: The efficacy of surgical treatment for benign prostatic hyperplasia (BPH) patients with detrusor underactivity (DU) remains controversial.

METHODS: To summarize relevant evidence, three databases (PubMed, Embase, and Web of Science) were searched from database inception to May 1, 2023. Transurethral surgical treatment modalities include transurethral prostatectomy (TURP), photoselective vaporization of the prostate (PVP), and transurethral incision of the prostate (TUIP). The efficacy of the transurethral surgical treatment was assessed according to maximal flow rate on uroflowmetry (Qmax), International Prostate Symptom Score (IPSS), postvoid residual (PVR), quality of life (QoL), voided volume, bladder contractility index (BCI) and maximal detrusor pressure at maximal flow rate (PdetQmax). Pooled mean differences (MDs) were used as summary statistics for comparison. The quality of enrolled studies was evaluated by using the Newcastle-Ottawa Scale. Sensitivity analysis and funnel plots were applied to assess possible biases.

RESULTS: In this study, 10 studies with a total of 1142 patients enrolled. In BPH patients with DU, within half a year, significant improvements in Qmax (pooled MD, 4.79; 95% CI, 2.43-7.16; P < 0.05), IPSS(pooled MD, - 14.29; 95%CI, - 16.67-11.90; P < 0.05), QoL (pooled MD, - 1.57; 95% CI, - 2.37-0.78; P < 0.05), voided volume (pooled MD, 62.19; 95% CI, 17.91-106.48; P < 0.05), BCI (pooled MD, 23.59; 95% CI, 8.15-39.04; P < 0.05), and PdetQmax (pooled MD, 28.62; 95% CI, 6.72-50.52; P < 0.05) were observed after surgery. In addition, after more than 1 year, significant improvements were observed in Qmax (pooled MD, 6.75; 95%CI, 4.35-9.15; P < 0.05), IPSS(pooled MD, - 13.76; 95%CI, - 15.17-12.35; P < 0.05), PVR (pooled MD, - 179.78; 95%CI, - 185.12-174.44; P < 0.05), QoL (pooled MD, - 2.61; 95%CI, - 3.12-2.09; P < 0.05), and PdetQmax (pooled MD, 27.94; 95%CI, 11.70-44.19; P < 0.05). Compared with DU patients who did not receive surgery, DU patients who received surgery showed better improvement in PVR (pooled MD, 137.00; 95%CI, 6.90-267.10; P < 0.05) and PdetQmax (pooled MD, - 8.00; 95%CI, - 14.68-1.32; P < 0.05).

CONCLUSIONS: Our meta-analysis results showed that transurethral surgery can improve the symptoms of BPH patients with DU. Surgery also showed advantages over pharmacological treatment for BPH patients with DU.

PROSPERO CRD42023415188.

RevDate: 2024-03-22

Li XY, Zhang SY, Hong YZ, et al (2024)

TGR5-mediated lateral hypothalamus-dCA3-dorsolateral septum circuit regulates depressive-like behavior in male mice.

Neuron pii:S0896-6273(24)00152-1 [Epub ahead of print].

Although bile acids play a notable role in depression, the pathological significance of the bile acid TGR5 membrane-type receptor in this disorder remains elusive. Using depression models of chronic social defeat stress and chronic restraint stress in male mice, we found that TGR5 in the lateral hypothalamic area (LHA) predominantly decreased in GABAergic neurons, the excitability of which increased in depressive-like mice. Upregulation of TGR5 or inhibition of GABAergic excitability in LHA markedly alleviated depressive-like behavior, whereas down-regulation of TGR5 or enhancement of GABAergic excitability facilitated stress-induced depressive-like behavior. TGR5 also bidirectionally regulated excitability of LHA GABAergic neurons via extracellular regulated protein kinases-dependent Kv4.2 channels. Notably, LHA GABAergic neurons specifically innervated dorsal CA3 (dCA3) CaMKIIα neurons for mediation of depressive-like behavior. LHA GABAergic TGR5 exerted antidepressant-like effects by disinhibiting dCA3 CaMKIIα neurons projecting to the dorsolateral septum (DLS). These findings advance our understanding of TGR5 and the LHA[GABA]→dCA3[CaMKIIα]→DLS[GABA] circuit for the development of potential therapeutic strategies in depression.

RevDate: 2024-03-22

Caillet AH, Phillips ATM, Modenese L, et al (2024)

NeuroMechanics: Electrophysiological and computational methods to accurately estimate the neural drive to muscles in humans in vivo.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology, 76:102873 pii:S1050-6411(24)00017-8 [Epub ahead of print].

The ultimate neural signal for muscle control is the neural drive sent from the spinal cord to muscles. This neural signal comprises the ensemble of action potentials discharged by the active spinal motoneurons, which is transmitted to the innervated muscle fibres to generate forces. Accurately estimating the neural drive to muscles in humans in vivo is challenging since it requires the identification of the activity of a sample of motor units (MUs) that is representative of the active MU population. Current electrophysiological recordings usually fail in this task by identifying small MU samples with over-representation of higher-threshold with respect to lower-threshold MUs. Here, we describe recent advances in electrophysiological methods that allow the identification of more representative samples of greater numbers of MUs than previously possible. This is obtained with large and very dense arrays of electromyographic electrodes. Moreover, recently developed computational methods of data augmentation further extend experimental MU samples to infer the activity of the full MU pool. In conclusion, the combination of new electrode technologies and computational modelling allows for an accurate estimate of the neural drive to muscles and opens new perspectives in the study of the neural control of movement and in neural interfacing.

RevDate: 2024-03-22

McNamara IN, Wellman SM, Li L, et al (2024)

Electrode sharpness and insertion speed reduce tissue damage near high-density penetrating arrays.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Over the past decade, neural electrodes have played a crucial role in bridging biological tissues with electronic and robotic devices. This study focuses on evaluating the optimal tip profile and insertion speed for effectively implanting Paradromics' high-density Fine Microwire Arrays (FμA) prototypes into the primary visual cortex (V1) of mice and rats, addressing the challenges associated with the "bed-of-nails" effect and tissue dimpling.

APPROACH: Tissue response was assessed by investigating the impact of electrodes on the blood-brain barrier (BBB) and cellular damage, with a specific emphasis on tailored insertion strategies to minimize tissue disruption during electrode implantation.

MAIN RESULTS: Electro-sharpened arrays demonstrated a marked reduction in cellular damage within 50 μm of the electrode tip compared to blunt and angled arrays. Histological analysis revealed that slow insertion speeds led to greater BBB compromise than fast and pneumatic methods. Successful single-unit recordings validated the efficacy of the optimized electro-sharpened arrays in capturing neural activity.

SIGNIFICANCE: These findings underscore the critical role of tailored insertion strategies in minimizing tissue damage during electrode implantation, highlighting the suitability of electro-sharpened arrays for long-term implant applications. This research contributes to a deeper understanding of the complexities associated with high-channel-count microelectrode array implantation, emphasizing the importance of meticulous assessment and optimization of key parameters for effective integration and minimal tissue disruption. By elucidating the interplay between insertion parameters and tissue response, our study lays a strong foundation for the development of advanced implantable devices with a reduction in reactive gliosis and improved performance in neural recording applications.

RevDate: 2024-03-26

Han Y, Ke Y, Wang R, et al (2024)

Enhancing SSVEP-BCI Performance under Fatigue State Using Dynamic Stopping Strategy.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have gained significant attention in recent years due to their advantages, including high communication rates, short calibration times, and high signal-to-noise ratios. However, one of the major challenges in practical application is performance degradation due to fatigue during continuous and prolonged use. This study aims to mitigate the negative effects of fatigue on SSVEP-BCI performance by adjusting data lengths and updating detection models using dynamic stopping strategy. Two 16-target SSVEP-BCIs were used for data collection, one using low-frequency stimulation and the other using high-frequency stimulation. A self-recorded fatigue dataset from twenty-four subjects was used for extensive evaluation studies. The simulated online experiment demonstrated that the proposed methods outperformed the conventional fixed stopping strategy in terms of classification accuracy, information transfer rate, and selection time, regardless of stimulus frequency. In conclusion, the proposed methods can improve the performance of the SSVEP-BCI in fatigue state and provide superior performance over extended periods of sustained use.

RevDate: 2024-03-25
CmpDate: 2024-03-25

Myszor IT, Lapka K, Hermannsson K, et al (2024)

Bile acid metabolites enhance expression of cathelicidin antimicrobial peptide in airway epithelium through activation of the TGR5-ERK1/2 pathway.

Scientific reports, 14(1):6750.

Signals for the maintenance of epithelial homeostasis are provided in part by commensal bacteria metabolites, that promote tissue homeostasis in the gut and remote organs as microbiota metabolites enter the bloodstream. In our study, we investigated the effects of bile acid metabolites, 3-oxolithocholic acid (3-oxoLCA), alloisolithocholic acid (AILCA) and isolithocholic acid (ILCA) produced from lithocholic acid (LCA) by microbiota, on the regulation of innate immune responses connected to the expression of host defense peptide cathelicidin in lung epithelial cells. The bile acid metabolites enhanced expression of cathelicidin at low concentrations in human bronchial epithelial cell line BCi-NS1.1 and primary bronchial/tracheal cells (HBEpC), indicating physiological relevance for modulation of innate immunity in airway epithelium by bile acid metabolites. Our study concentrated on deciphering signaling pathways regulating expression of human cathelicidin, revealing that LCA and 3-oxoLCA activate the surface G protein-coupled bile acid receptor 1 (TGR5, Takeda-G-protein-receptor-5)-extracellular signal-regulated kinase (ERK1/2) cascade, rather than the nuclear receptors, aryl hydrocarbon receptor, farnesoid X receptor and vitamin D3 receptor in bronchial epithelium. Overall, our study provides new insights into the modulation of innate immune responses by microbiota bile acid metabolites in the gut-lung axis, highlighting the differences in epithelial responses between different tissues.

RevDate: 2024-03-22

Chen S, Xi X, Wang T, et al (2024)

Optimizing motion imagery classification with limited channels using the common spatial pattern-based integrated algorithm.

Medical & biological engineering & computing [Epub ahead of print].

The extraction of effective classification features from electroencephalogram (EEG) signals in motor imagery is a popular research topic. The Common Spatial Pattern (CSP) algorithm is widely employed in this field. However, the performance of the traditional CSP method depends significantly on the choice of a specific frequency band and channel number of EEG data. Furthermore, inter-class variance among these frequency bands and the limited number of available EEG channels can adversely affect the CSP algorithm's ability to extract meaningful features from the relevant signal frequency bands. We hypothesize that multiple Intrinsic Mode Functions (IMFS), into which the raw EEG signal is decomposed, can better capture the non-Gaussian characteristics of the signal, thus compensating for the limitations of the CSP algorithm when dealing with nonlinear and non-Gaussian distributed data with few channels. Therefore, this paper proposes a novel method that integrates Variational Mode Decomposition (VMD), Phase Space Reconstruction (PSR), and the CSP algorithm to address these issues. VMD is used to filter and enhance the quality of the collected data, PSR is employed to increase the effective data channels (data augmentation), and the subsequent CSP filtering can obtain signals with spatial features, which are decoded by Convolutional Neural Networks (CNN) for action decoding. This study utilizes self-collected EEG data to demonstrate that the new method can achieve a good classification accuracy of 82.30% on average, confirming the improved algorithm's effectiveness and feasibility. Furthermore, this study conducted validation on the publicly available BCI Competition IV dataset 2b, demonstrating an average classification accuracy of 87.49%.

RevDate: 2024-03-21

Zheng L, Dong Y, Tian S, et al (2024)

A calibration-free c-VEP based BCI employing narrow-band random sequences.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Code-modulated visual evoked potential (c-VEP) based brain-computer interfaces (BCIs) exhibit high encodingefficiency. Nevertheless, the majority of c-VEP based BCIs necessitate an initial training or calibration session, particularlywhen the number of targets expands, which impedes the practicality. To address this predicament, this study introduces a calibration-free c-VEP based BCI employing narrow-band random sequences.

APPROACH: For the encoding method, a series of random sequences were generated within a specific frequency band. The c-VEP signals were subsequently elicited through the application of on-type grid flashes that were modulated by these sequences. For the calibration-free decoding algorithm, filter-bank canonical correlation analysis (FBCCA) was utilized with the reference templates generated from the original sequences. Thirty-five subjects participated into an online BCI experiment. The performances of c-VEP based BCIs utilizing narrow-band random sequences with frequency bands of 15~25 Hz (NBRS-15) and 8~16 Hz (NBRS-8) were compared with that of a steady-state visual evoked potential (SSVEP) based BCI within a frequency range of 8~15.8 Hz. Main results. The offline analysis results demonstrated a substantial correlation between the c-VEPs and the original narrow-band random sequences. After parameter optimization, the calibration-free system employing the NBRS 15 frequency band achieved an-average information transfer rate (ITR) of 78.56 ± 37.03 bits/min, which exhibited no significant difference compared to the performance of the SSVEP based system when utilizing FBCCA. The proposed system achieved an average ITR of 104.2 ± 67.18 bits/min in a simulation of a 1000-target BCI system.

SIGNIFICANCE: This study introduces a novel calibration-free c-VEP based BCI system employing narrow-band random sequences and shows great potential of the proposed system in achieving a large number of targets and high ITR. .

RevDate: 2024-03-21

Sadras N, Pesaran B, MM Shanechi (2024)

Event detection and classification from multimodal time series with application to neural data.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: The detection of events in time-series data is a common signal-processing problem. When the data can be modeled as a known template signal with an unknown delay in Gaussian noise, detection of the template signal can be done with a traditional matched filter. However, in many applications, the event of interest is represented in multimodal data consisting of both Gaussian and point-process time series. Neuroscience experiments for example can simultaneously record multimodal neural signals such as local field potentials (LFPs), which can be modeled as Gaussian, and neuronal spikes, which can be modeled as point processes. Currently, no method exists for event detection from such multimodal data, and as such our objective in this work is to develop a method to meet this need.

APPROACH: Here we address this challenge by developing the multimodal event detector (MED) algorithm which simultaneously estimates event times and classes. To do this, we write a multimodal likelihood function for Gaussian and point-process observations and derive the associated maximum likelihood estimator of simultaneous event times and classes. We additionally introduce a cross-modal scaling parameter to account for model mismatch in real datasets.

MAIN RESULTS: We validate this method in extensive simulations as well as in a neural spike-LFP dataset recorded from a macaque monkey performing an eye-movement task, where the events of interest are eye movements with unknown times and directions. We show that the MED can successfully detect eye movement onset and classify eye movement direction. Further, the MED successfully combines information across data modalities, with multimodal performance exceeding unimodal performance.

SIGNIFICANCE: This method can facilitate applications such as the discovery of latent events in multimodal neural population activity and the development of brain-computer interfaces for naturalistic setups without constrained tasks or prior knowledge of event times.

RevDate: 2024-03-21

V S, M Ramasubba Reddy (2024)

Classification of Motor Imagery EEG signals using high resolution Time-Frequency Representations and Convolutional Neural network.

Biomedical physics & engineering express [Epub ahead of print].

A Motor Imagery (MI) based Brain Computer Interface (BCI) system aims to provide neuro-rehabilitation for the motor disabled people and patients with brain injuries (e.g., stroke patients) etc. The aim of this work is to classify the left and right hand MI tasks by utilizing the occurrence of event related desynchronization and synchronization (ERD\ERS) in the Electroencephalogram (EEG) during these tasks. This study proposes to use a set of Complex Morlet Wavelets (CMW) having frequency dependent widths to generate high-resolution time-frequency representations (TFR) of the MI EEG signals present in the channels C3 and C4. A novel method for the selection of the value of number of cycles relative to the center frequency of the CMW is studied here for extracting the MI task features. The generated TFRs are given as input to a Convolutional neural network (CNN) for classifying them into left or right hand MI tasks. The proposed framework attains a classification accuracy of 82.2% on the BCI Competition IV dataset 2a, showing that the TFRs generated in this work give a higher classification accuracy than the baseline methods and other existing algorithms.

RevDate: 2024-03-22

Zhang X, He L, Gao Q, et al (2024)

Performance of the Action Observation-Based Brain-Computer Interface in Stroke Patients and Gaze Metrics Analysis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Brain-computer interfaces (BCIs) are anticipated to improve the efficacy of rehabilitation for people with motor disabilities. However, applying BCI in clinical practice is still a challenge due to the great diversity of patients. In the current study, a novel action observation (AO) based BCI was proposed and tested on stroke patients. Ten non-hemineglect patients and ten hemineglect patients were recruited. Four AO stimuli were designed, each presenting a decomposed action to complete the reach-and-grasp task. EEG data and eye movement data were collected. Eye movement data was utilized to analyze the reasons for individual differences in BCI performance. Task discriminative component analysis was utilized to perform online target detection. The results showed that the designed AO-based BCI could simultaneously induce steady state motion visual evoked potential (SSMVEP) from the occipital region and sensory motor rhythm from the sensorimotor region in stroke patients. The average online detection accuracy among the four AO stimuli reached 67% within 3 s in the non-hemineglect group, while the accuracy only reached 35% in the hemineglect group. Gaze metrics showed that the average total duration of fixations during the stimulus phase in the hemineglect group was only 1.31 s ± 0.532 s which was significantly lower than that in the non-hemineglect group. The results indicated that hemineglect patients have difficulty gazing at the AO stimulus, resulting in poor detection performance and weak desynchronization in the sensorimotor region. Furthermore, the degree of neglect is inversely proportional to the target detection accuracy in hemineglect stroke patients. In addition, the gaze metrics associated with cognitive load were significantly correlated with the accuracy in non-hemineglect patients. It indicated the cognitive load may affect the AO-based BCI. The current study will expedite the clinical application of AO-based BCI.

RevDate: 2024-03-22

Liu Y, Luo Y, Zhang N, et al (2024)

A scientometric review of the growing trends in transcranial alternating current stimulation (tACS).

Frontiers in human neuroscience, 18:1362593.

OBJECTIVE: The aim of the current study was to provide a comprehensive picture of tACS-related research in the last decade through a bibliometric approach in order to systematically analyze the current status and cutting-edge trends in this field.

METHODS: Articles and review articles related to tACS from 2013 to 2022 were searched on the Web of Science platform. A bibliometric analysis of authors, journals, countries, institutions, references, and keywords was performed using CiteSpace (6.2.R2), VOSviewer (1.6.19), Scimago Graphica (1.0.30), and Bibliometrix (4.2.2).

RESULTS: A total of 602 papers were included. There was an overall increase in annual relevant publications in the last decade. The most contributing author was Christoph S. Herrmann. Brain Stimulation was the most prolific journal. The most prolific countries and institutions were Germany and Harvard University, respectively.

CONCLUSION: The findings reveal the development prospects and future directions of tACS and provide valuable references for researchers in the field. In recent years, the keywords "gamma," "transcranial direct current simulation," and "Alzheimer's disease" that have erupted, as well as many references cited in the outbreak, have provided certain clues for the mining of research prefaces. This will act as a guide for future researchers in determining the path of tACS research.

RevDate: 2024-03-22

Tian Z, Wu Z, S Ying (2024)

Editorial: Brain functional analysis and brain-like intelligence.

Frontiers in neuroscience, 18:1383481.

RevDate: 2024-03-22

Huang C, Shi N, Miao Y, et al (2024)

Visual tracking brain-computer interface.

iScience, 27(4):109376.

Brain-computer interfaces (BCIs) offer a way to interact with computers without relying on physical movements. Non-invasive electroencephalography-based visual BCIs, known for efficient speed and calibration ease, face limitations in continuous tasks due to discrete stimulus design and decoding methods. To achieve continuous control, we implemented a novel spatial encoding stimulus paradigm and devised a corresponding projection method to enable continuous modulation of decoded velocity. Subsequently, we conducted experiments involving 17 participants and achieved Fitt's information transfer rate (ITR) of 0.55 bps for the fixed tracking task and 0.37 bps for the random tracking task. The proposed BCI with a high Fitt's ITR was then integrated into two applications, including painting and gaming. In conclusion, this study proposed a visual BCI based-control method to go beyond discrete commands, allowing natural continuous control based on neural activity.

RevDate: 2024-03-22

Mainul EA, MF Hossain (2024)

A metamaterial unit-cell based patch radiator for brain-machine interface technology.

Heliyon, 10(6):e27775.

This paper presents a novel approach to the design of a brain implantable antenna tailored for brain-machine interface (BMI) technology. The design is based on a U-shaped unit-cell metamaterial (MTM), introducing innovative features to enhance performance and address specific challenges associated with BMI applications. The motivation behind the use of the unit-cell structure is to elongate the electric path within the antenna patch, diverging from a reliance on the electrical properties of the MTM. Consequently, the unit cell is connected to an inset-fed transmission line and shorted to the ground. This configuration serves the dual purpose of reducing the size of the antenna and enabling resonance at the 2.442 GHz band within a seven-layer brain phantom. The antenna is designed using a FR-4 substrate (εr = 4.3 and tan δ = 0.025) of 1.5 mm thickness, and it is coated with a biocompatible polyamide material (εr = 4.3 and tan δ = 0.004) of 0.05 mm thickness. The proposed antenna achieves a compact dimension of 20 × 20 × 1.6 mm3 (0.338 × 0.338 × 0.027 λg3) and demonstrates a high bandwidth of 974 MHz with its gain of -14.6 dBi in the 2.442 GHz band. It also exhibits a matched impedance of 49.41-j1.32 Ω in the implantable condition, corresponding to a 50 Ω source impedance. In comparison to a selection of relevant research works, the proposed antenna has a low specific absorption rate (SAR) of 218 W/kg and 68 W/kg at 1g and 10g brain tissue standards, respectively. An antenna prototype has been fabricated and measured for return loss in both free space and in-vivo conditions using sheep's brain. The measurement results are found to be in close agreement with the simulation results for both conditions, showing the practical applicability of the proposed antenna for BMI applications.

RevDate: 2024-03-23
CmpDate: 2024-03-22

Liu XY, Mu JJ, Han JG, et al (2024)

Heart-brain axis: low blood pressure during off-pump CABG surgery is associated with postoperative heart failure.

Military Medical Research, 11(1):18.

RevDate: 2024-03-21

Liu X, Hu B, Si Y, et al (2024)

The role of eye movement signals in non-invasive brain-computer interface typing system.

Medical & biological engineering & computing [Epub ahead of print].

Brain-Computer Interfaces (BCIs) have shown great potential in providing communication and control for individuals with severe motor disabilities. However, traditional BCIs that rely on electroencephalography (EEG) signals suffer from low information transfer rates and high variability across users. Recently, eye movement signals have emerged as a promising alternative due to their high accuracy and robustness. Eye movement signals are the electrical or mechanical signals generated by the movements and behaviors of the eyes, serving to denote the diverse forms of eye movements, such as fixations, smooth pursuit, and other oculomotor activities like blinking. This article presents a review of recent studies on the development of BCI typing systems that incorporate eye movement signals. We first discuss the basic principles of BCI and the recent advancements in text entry. Then, we provide a comprehensive summary of the latest advancements in BCI typing systems that leverage eye movement signals. This includes an in-depth analysis of hybrid BCIs that are built upon the integration of electrooculography (EOG) and eye tracking technology, aiming to enhance the performance and functionality of the system. Moreover, we highlight the advantages and limitations of different approaches, as well as potential future directions. Overall, eye movement signals hold great potential for enhancing the usability and accessibility of BCI typing systems, and further research in this area could lead to more effective communication and control for individuals with motor disabilities.

RevDate: 2024-03-22

Yang H, Wu H, Kong L, et al (2024)

Precise detection of awareness in disorders of consciousness using deep learning framework.

NeuroImage, 290:120580 pii:S1053-8119(24)00075-2 [Epub ahead of print].

Diagnosis of disorders of consciousness (DOC) remains a formidable challenge. Deep learning methods have been widely applied in general neurological and psychiatry disorders, while limited in DOC domain. Considering the successful use of resting-state functional MRI (rs-fMRI) for evaluating patients with DOC, this study seeks to explore the conjunction of deep learning techniques and rs-fMRI in precisely detecting awareness in DOC. We initiated our research with a benchmark dataset comprising 140 participants, including 76 unresponsive wakefulness syndrome (UWS), 25 minimally conscious state (MCS), and 39 Controls, from three independent sites. We developed a cascade 3D EfficientNet-B3-based deep learning framework tailored for discriminating MCS from UWS patients, referred to as "DeepDOC", and compared its performance against five state-of-the-art machine learning models. We also included an independent dataset consists of 11 DOC patients to test whether our model could identify patients with cognitive motor dissociation (CMD), in which DOC patients were behaviorally diagnosed unconscious but could be detected conscious by brain computer interface (BCI) method. Our results demonstrate that DeepDOC outperforms the five machine learning models, achieving an area under curve (AUC) value of 0.927 and accuracy of 0.861 for distinguishing MCS from UWS patients. More importantly, DeepDOC excels in CMD identification, achieving an AUC of 1 and accuracy of 0.909. Using gradient-weighted class activation mapping algorithm, we found that the posterior cortex, encompassing the visual cortex, posterior middle temporal gyrus, posterior cingulate cortex, precuneus, and cerebellum, as making a more substantial contribution to classification compared to other brain regions. This research offers a convenient and accurate method for detecting covert awareness in patients with MCS and CMD using rs-fMRI data.

RevDate: 2024-03-21

Reichert C, Sweeney-Reed CM, Hinrichs H, et al (2024)

A toolbox for decoding BCI commands based on event-related potentials.

Frontiers in human neuroscience, 18:1358809.

Commands in brain-computer interface (BCI) applications often rely on the decoding of event-related potentials (ERP). For instance, the P300 potential is frequently used as a marker of attention to an oddball event. Error-related potentials and the N2pc signal are further examples of ERPs used for BCI control. One challenge in decoding brain activity from the electroencephalogram (EEG) is the selection of the most suitable channels and appropriate features for a particular classification approach. Here we introduce a toolbox that enables ERP-based decoding using the full set of channels, while automatically extracting informative components from relevant channels. The strength of our approach is that it handles sequences of stimuli that encode multiple items using binary classification, such as target vs. nontarget events typically used in ERP-based spellers. We demonstrate examples of application scenarios and evaluate the performance of four openly available datasets: a P300-based matrix speller, a P300-based rapid serial visual presentation (RSVP) speller, a binary BCI based on the N2pc, and a dataset capturing error potentials. We show that our approach achieves performances comparable to those in the original papers, with the advantage that only conventional preprocessing is required by the user, while channel weighting and decoding algorithms are internally performed. Thus, we provide a tool to reliably decode ERPs for BCI use with minimal programming requirements.

RevDate: 2024-03-21

Degirmenci M, Yuce YK, Perc M, et al (2024)

EEG-based finger movement classification with intrinsic time-scale decomposition.

Frontiers in human neuroscience, 18:1362135.

INTRODUCTION: Brain-computer interfaces (BCIs) are systems that acquire the brain's electrical activity and provide control of external devices. Since electroencephalography (EEG) is the simplest non-invasive method to capture the brain's electrical activity, EEG-based BCIs are very popular designs. Aside from classifying the extremity movements, recent BCI studies have focused on the accurate coding of the finger movements on the same hand through their classification by employing machine learning techniques. State-of-the-art studies were interested in coding five finger movements by neglecting the brain's idle case (i.e., the state that brain is not performing any mental tasks). This may easily cause more false positives and degrade the classification performances dramatically, thus, the performance of BCIs. This study aims to propose a more realistic system to decode the movements of five fingers and the no mental task (NoMT) case from EEG signals.

METHODS: In this study, a novel praxis for feature extraction is utilized. Using Proper Rotational Components (PRCs) computed through Intrinsic Time Scale Decomposition (ITD), which has been successfully applied in different biomedical signals recently, features for classification are extracted. Subsequently, these features were applied to the inputs of well-known classifiers and their different implementations to discriminate between these six classes. The highest classifier performances obtained in both subject-independent and subject-dependent cases were reported. In addition, the ANOVA-based feature selection was examined to determine whether statistically significant features have an impact on the classifier performances or not.

RESULTS: As a result, the Ensemble Learning classifier achieved the highest accuracy of 55.0% among the tested classifiers, and ANOVA-based feature selection increases the performance of classifiers on five-finger movement determination in EEG-based BCI systems.

DISCUSSION: When compared with similar studies, proposed praxis achieved a modest yet significant improvement in classification performance although the number of classes was incremented by one (i.e., NoMT).

RevDate: 2024-03-21

Peng B, Bi L, Wang Z, et al (2024)

Robust Decoding of Upper-limb Movement Direction under Cognitive Distraction with Invariant Patterns in Embedding Manifold.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

Motor brain-computer interfaces (BCIs) have gained growing research interest in motor rehabilitation, restoration, and prostheses control. Decoding upper-limb movement direction with noninvasive BCIs has been extensively investigated. However, few of them address the intervention of cognitive distraction that impairs decoding performance in practice. In this study, we propose a novel decoding model with invariant patterns in embedding manifold on a mixed dataset pooled from electroencephalograph (EEG) signals under different attentional states. We reconstruct an embedding low-dimensional manifold that intrinsically characterizes movements of the upper limb and transfer patterns of neural activities decomposed from brain functional connectivity (FC) to the manifold subspace to further preserve movement-related information. Experimental results showed that the proposed decoding model had higher robustness on the mixed dataset of attentive and distracted states compared to the baseline method. Our research provides insights into modeling a uniform underlying mechanism of movement-related EEG signals and can help enhance the practicability of BCI systems under real-world situations.

RevDate: 2024-03-21

Raj V, Jagadish C, V Gautam (2021)

Understanding, engineering, and modulating the growth of neural networks: An interdisciplinary approach.

Biophysics reviews, 2(2):021303.

A deeper understanding of the brain and its function remains one of the most significant scientific challenges. It not only is required to find cures for a plethora of brain-related diseases and injuries but also opens up possibilities for achieving technological wonders, such as brain-machine interface and highly energy-efficient computing devices. Central to the brain's function is its basic functioning unit (i.e., the neuron). There has been a tremendous effort to understand the underlying mechanisms of neuronal growth on both biochemical and biophysical levels. In the past decade, this increased understanding has led to the possibility of controlling and modulating neuronal growth in vitro through external chemical and physical methods. We provide a detailed overview of the most fundamental aspects of neuronal growth and discuss how researchers are using interdisciplinary ideas to engineer neuronal networks in vitro. We first discuss the biochemical and biophysical mechanisms of neuronal growth as we stress the fact that the biochemical or biophysical processes during neuronal growth are not independent of each other but, rather, are complementary. Next, we discuss how utilizing these fundamental mechanisms can enable control over neuronal growth for advanced neuroengineering and biomedical applications. At the end of this review, we discuss some of the open questions and our perspectives on the challenges and possibilities related to controlling and engineering the growth of neuronal networks, specifically in relation to the materials, substrates, model systems, modulation techniques, data science, and artificial intelligence.

RevDate: 2024-03-19

Zou Q, Duan H, Fang S, et al (2024)

Fabrication of yeast β-glucan/sodium alginate/γ-polyglutamic acid composite particles for hemostasis and wound healing.

Biomaterials science [Epub ahead of print].

Particles with a porous structure can lead to quick hemostasis and provide a good matrix for cell proliferation during wound healing. Recently, many particle-based wound healing materials have been clinically applied. However, these products show good hemostatic ability but with poor wound healing ability. To solve this problem, this study fabricated APGG composite particles using yeast β-glucan (obtained from Saccharomyces cerevisiae), sodium alginate, and γ-polyglutamic acid as the starting materials. The structure of yeast β-glucan was modified with many carboxymethyl groups to obtain carboxymethylated β-glucan, which could coordinate with Ca[2+] ions to form a crosslinked structure. A morphology study indicated that the APGG particles showed an irregular spheroidal structure with a low density (<0.1 g cm[-3]) and high porosity (>40%). An in vitro study revealed that the particles exhibited a low BCI value, low hemolysis ratio, and good cytocompatibility against L929 cells. The APGG particles could quickly stop bleeding in a mouse liver injury model and exhibited better hemostatic ability than the commercially available product Celox. Furthermore, the APGG particles could accelerate the healing of non-infected wounds, and the expression levels of CD31, α-SMA, and VEGF related to angiogenesis were significantly enhanced.

RevDate: 2024-03-20

Sieghartsleitner S, Sebastián-Romagosa M, Cho W, et al (2024)

Upper extremity training followed by lower extremity training with a brain-computer interface rehabilitation system.

Frontiers in neuroscience, 18:1346607.

INTRODUCTION: Brain-computer interfaces (BCIs) based on functional electrical stimulation have been used for upper extremity motor rehabilitation after stroke. However, little is known about their efficacy for multiple BCI treatments. In this study, 19 stroke patients participated in 25 upper extremity followed by 25 lower extremity BCI training sessions.

METHODS: Patients' functional state was assessed using two sets of clinical scales for the two BCI treatments. The Upper Extremity Fugl-Meyer Assessment (FMA-UE) and the 10-Meter Walk Test (10MWT) were the primary outcome measures for the upper and lower extremity BCI treatments, respectively.

RESULTS: Patients' motor function as assessed by the FMA-UE improved by an average of 4.2 points (p < 0.001) following upper extremity BCI treatment. In addition, improvements in activities of daily living and clinically relevant improvements in hand and finger spasticity were observed. Patients showed further improvements after the lower extremity BCI treatment, with walking speed as measured by the 10MWT increasing by 0.15 m/s (p = 0.001), reflecting a substantial meaningful change. Furthermore, a clinically relevant improvement in ankle spasticity and balance and mobility were observed.

DISCUSSION: The results of the current study provide evidence that both upper and lower extremity BCI treatments, as well as their combination, are effective in facilitating functional improvements after stroke. In addition, and most importantly improvements did not stop after the first 25 upper extremity BCI sessions.

RevDate: 2024-03-19

Shiina T, Yunoki T, Tachino H, et al (2024)

Comparative study of surgical outcomes regarding tear meniscus area and high-order aberrations between two different interventional methods for primary acquired nasolacrimal duct obstruction.

Japanese journal of ophthalmology [Epub ahead of print].

PURPOSE: To compare endonasal dacryocystorhinostomy (EN-DCR) with sheath-guided dacryoendoscopic probing and bicanalicular intubation (SG-BCI) by evaluating tear meniscus area (TMA) and total high-order aberrations (HOAs) for primary acquired nasolacrimal duct obstruction (PANDO).

METHOD: We retrospectively reviewed 56 eyes of 42 patients (7 men, 35 women; age, 72.7±13.1 years) who underwent EN-DCR or SG-BCI for PANDO in Toyama University Hospital from February 2020 to June 2022. In the EN-DCR and SG-BCI groups, we measured the patency of the lacrimal passage, preoperative and postoperative TMA, and HOAs of the central 4 mm of the cornea using optical coherence tomography (AS-OCT), six months postoperatively.

RESULTS: There was a positive correlation between preoperative TMA and preoperative HOAs in all cases. Postoperative patency of lacrimal passage was 100% in the EN-DCR and 80.8% in the SG-BCI group. There was a significant difference in the number of passages between the two groups (p = 0.01). Preoperative TMA and HOAs showed a significant postoperative decrease in both groups (EN-DCR group: p<0.01, p<0.01, SG-BCI group: p<0.01, p=0.03, respectively). We then calculated the rate of change of preoperative and postoperative TMA and HOAs and compared them between the two groups. The rate of change was significantly higher in the EN-DCR group than that in the SG-BCI group (TMA, p=0.03; HOAs, p=0.02).

CONCLUSION: Although both EN-DCR and SG-BCI are effective for PANDO, our results suggest that EN-DCR is more effective in improving TMA and HOAs.

RevDate: 2024-03-21
CmpDate: 2024-03-20

Schreiner L, Jordan M, Sieghartsleitner S, et al (2024)

Mapping of the central sulcus using non-invasive ultra-high-density brain recordings.

Scientific reports, 14(1):6527.

Brain mapping is vital in understanding the brain's functional organization. Electroencephalography (EEG) is one of the most widely used brain mapping approaches, primarily because it is non-invasive, inexpensive, straightforward, and effective. Increasing the electrode density in EEG systems provides more neural information and can thereby enable more detailed and nuanced mapping procedures. Here, we show that the central sulcus can be clearly delineated using a novel ultra-high-density EEG system (uHD EEG) and somatosensory evoked potentials (SSEPs). This uHD EEG records from 256 channels with an inter-electrode distance of 8.6 mm and an electrode diameter of 5.9 mm. Reconstructed head models were generated from T1-weighted MRI scans, and electrode positions were co-registered to these models to create topographical plots of brain activity. EEG data were first analyzed with peak detection methods and then classified using unsupervised spectral clustering. Our topography plots of the spatial distribution from the SSEPs clearly delineate a division between channels above the somatosensory and motor cortex, thereby localizing the central sulcus. Individual EEG channels could be correctly classified as anterior or posterior to the central sulcus with 95.2% accuracy, which is comparable to accuracies from invasive intracranial recordings. Our findings demonstrate that uHD EEG can resolve the electrophysiological signatures of functional representation in the brain at a level previously only seen from surgically implanted electrodes. This novel approach could benefit numerous applications, including research, neurosurgical mapping, clinical monitoring, detection of conscious function, brain-computer interfacing (BCI), rehabilitation, and mental health.

RevDate: 2024-03-19

He Y, van der Ven S, Liaw HP, et al (2024)

An Event-based Neural Compressive Telemetry with >11× Loss-less Data Reduction for High-bandwidth Intracortical Brain Computer Interfaces.

IEEE transactions on biomedical circuits and systems, PP: [Epub ahead of print].

Intracortical brain-computer interfaces offer superior spatial and temporal resolutions, but face challenges as the increasing number of recording channels introduces high amounts of data to be transferred. This requires power-hungry data serialization and telemetry, leading to potential tissue damage risks. To address this challenge, this paper introduces an event-based neural compressive telemetry (NCT) consisting of 8 channel-rotating Δ-ADCs, an event-driven serializer supporting a proposed ternary address event representation protocol, and an event-based LVDS driver. Leveraging a high sparsity of extracellular spikes and high spatial correlation of the high-density recordings, the proposed NCT achieves a compression ratio of >11.4×, while consumes only 1 μW per channel, which is 127× more efficient than state of the art. The NCT well preserves the spike waveform fidelity, and has a low normalized RMS error <23% even with a spike amplitude down to only 31 μV.

RevDate: 2024-03-19

Klomchitcharoen S, Wechakarn P, Tangwattanasirikun T, et al (2024)

High-altitude balloon platform for studying the biological response of living organisms exposed to near-space environments.

Heliyon, 10(6):e27406.

The intangible desire to explore the mysteries of the universe has driven numerous advancements for humanity for centuries. Extraterrestrial journeys are becoming more realistic as a result of human curiosity and endeavors. Over the years, space biology research has played a significant role in understanding the hazardous effects of the space environment on human health during long-term space travel. The inevitable consequence of a space voyage is space ionizing radiation, which has deadly aftereffects on the human body. The paramount objective of this study is to provide a robust platform for performing biological experiments within the Earth's stratosphere by utilizing high-altitude balloons. This platform allows the use of a biological payload to simulate spaceflight missions within the unique properties of space that cannot be replicated in terrestrial facilities. This paper describes the feasibility and demonstration of a biological balloon mission suitable for students and scientists to perform space biology experiments within the boundary of the stratosphere. In this study, a high-altitude balloon was launched into the upper atmosphere (∼29 km altitude), where living microorganisms were exposed to a hazardous combination of UV irradiation, ultralow pressure and cold shock. The balloon carried the budding yeast Saccharomyces cerevisiae to investigate microbial survival potential under extreme conditions. The results indicated a notable reduction in biosample mortality two orders of magnitude (2-log) after exposure to 164.9 kJ m[-2] UV. Postflight experiments have shown strong evidence that the effect of UV irradiation on living organisms is stronger than that of other extreme conditions.

RevDate: 2024-03-18

Pun TK, Khoshnevis M, Hosman T, et al (2024)

Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces.

bioRxiv : the preprint server for biology pii:2024.02.29.582733.

Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method to measure instability in neural data without needing to label user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.

RevDate: 2024-03-18

Capadona J, Hoeferlin G, Grabinski S, et al (2024)

Bacteria Invade the Brain Following Sterile Intracortical Microelectrode Implantation.

Research square pii:rs.3.rs-3980065.

Brain-machine interface performance is largely affected by the neuroinflammatory responses resulting in large part from blood-brain barrier (BBB) damage following intracortical microelectrode implantation. Recent findings strongly suggest that certain gut bacterial constituents penetrate the BBB and are resident in various brain regions of rodents and humans, both in health and disease. Therefore, we hypothesized that damage to the BBB caused by microelectrode implantation could amplify dysregulation of the microbiome-gut-brain axis. Here, we report that bacteria, including those commonly found in the gut, enter the brain following intracortical microelectrode implantation in mice implanted with single-shank silicon microelectrodes. Systemic antibiotic treatment of mice implanted with microelectrodes to suppress bacteria resulted in differential expression of bacteria in the brain tissue and a reduced acute inflammatory response compared to untreated controls, correlating with temporary improvements in microelectrode recording performance. Long-term antibiotic treatment resulted in worsening microelectrode recording performance and dysregulation of neurodegenerative pathways. Fecal microbiome composition was similar between implanted mice and an implanted human, suggesting translational findings. However, a significant portion of invading bacteria was not resident in the brain or gut. Together, the current study established a paradigm-shifting mechanism that may contribute to chronic intracortical microelectrode recording performance and affect overall brain health following intracortical microelectrode implantation.

RevDate: 2024-03-18

Temmar H, Willsey MS, Costello JT, et al (2024)

Artificial neural network for brain-machine interface consistently produces more naturalistic finger movements than linear methods.

bioRxiv : the preprint server for biology pii:2024.03.01.583000.

UNLABELLED: Brain-machine interfaces (BMI) aim to restore function to persons living with spinal cord injuries by 'decoding' neural signals into behavior. Recently, nonlinear BMI decoders have outperformed previous state-of-the-art linear decoders, but few studies have investigated what specific improvements these nonlinear approaches provide. In this study, we compare how temporally convolved feedforward neural networks (tcFNNs) and linear approaches predict individuated finger movements in open and closed-loop settings. We show that nonlinear decoders generate more naturalistic movements, producing distributions of velocities 85.3% closer to true hand control than linear decoders. Addressing concerns that neural networks may come to inconsistent solutions, we find that regularization techniques improve the consistency of tcFNN convergence by 194.6%, along with improving average performance, and training speed. Finally, we show that tcFNN can leverage training data from multiple task variations to improve generalization. The results of this study show that nonlinear methods produce more naturalistic movements and show potential for generalizing over less constrained tasks.

TEASER: A neural network decoder produces consistent naturalistic movements and shows potential for real-world generalization through task variations.

RevDate: 2024-03-16

Borra D, Filippini M, Ursino M, et al (2024)

Convolutional neural networks reveal properties of reach-to-grasp encoding in posterior parietal cortex.

Computers in biology and medicine, 172:108188 pii:S0010-4825(24)00272-5 [Epub ahead of print].

Deep neural networks (DNNs) are widely adopted to decode motor states from both non-invasively and invasively recorded neural signals, e.g., for realizing brain-computer interfaces. However, the neurophysiological interpretation of how DNNs make the decision based on the input neural activity is limitedly addressed, especially when applied to invasively recorded data. This reduces decoder reliability and transparency, and prevents the exploitation of decoders to better comprehend motor neural encoding. Here, we adopted an explainable artificial intelligence approach - based on a convolutional neural network and an explanation technique - to reveal spatial and temporal neural properties of reach-to-grasping from single-neuron recordings of the posterior parietal area V6A. The network was able to accurately decode 5 different grip types, and the explanation technique automatically identified the cells and temporal samples that most influenced the network prediction. Grip encoding in V6A neurons already started at movement preparation, peaking during movement execution. A difference was found within V6A: dorsal V6A neurons progressively encoded more for increasingly advanced grips, while ventral V6A neurons for increasingly rudimentary grips, with both subareas following a linear trend between the amount of grip encoding and the level of grip skills. By revealing the elements of the neural activity most relevant for each grip with no a priori assumptions, our approach supports and advances current knowledge about reach-to-grasp encoding in V6A, and it may represent a general tool able to investigate neural correlates of motor or cognitive tasks (e.g., attention and memory tasks) from single-neuron recordings.

RevDate: 2024-03-19

Fan L, Liu J, Hu W, et al (2024)

Targeting pro-inflammatory T cells as a novel therapeutic approach to potentially resolve atherosclerosis in humans.

Cell research [Epub ahead of print].

Atherosclerosis (AS), a leading cause of cardio-cerebrovascular disease worldwide, is driven by the accumulation of lipid contents and chronic inflammation. Traditional strategies primarily focus on lipid reduction to control AS progression, leaving residual inflammatory risks for major adverse cardiovascular events (MACEs). While anti-inflammatory therapies targeting innate immunity have reduced MACEs, many patients continue to face significant risks. Another key component in AS progression is adaptive immunity, but its potential role in preventing AS remains unclear. To investigate this, we conducted a retrospective cohort study on tumor patients with AS plaques. We found that anti-programmed cell death protein 1 (PD-1) monoclonal antibody (mAb) significantly reduces AS plaque size. With multi-omics single-cell analyses, we comprehensively characterized AS plaque-specific PD-1[+] T cells, which are activated and pro-inflammatory. We demonstrated that anti-PD-1 mAb, when captured by myeloid-expressed Fc gamma receptors (FcγRs), interacts with PD-1 expressed on T cells. This interaction turns the anti-PD-1 mAb into a substitute PD-1 ligand, suppressing T-cell functions in the PD-1 ligands-deficient context of AS plaques. Further, we conducted a prospective cohort study on tumor patients treated with anti-PD-1 mAb with or without Fc-binding capability. Our analysis shows that anti-PD-1 mAb with Fc-binding capability effectively reduces AS plaque size, while anti-PD-1 mAb without Fc-binding capability does not. Our work suggests that T cell-targeting immunotherapy can be an effective strategy to resolve AS in humans.

RevDate: 2024-03-15

Li S, Xu C, Hu S, et al (2024)

Efficacy and tolerability of FDA-approved atypical antipsychotics for the treatment of bipolar depression: a systematic review and network meta-analysis.

European psychiatry : the journal of the Association of European Psychiatrists pii:S0924933824000257 [Epub ahead of print].

RevDate: 2024-03-16

Zhang J, Li J, Huang Z, et al (2023)

Recent Progress in Wearable Brain-Computer Interface (BCI) Devices Based on Electroencephalogram (EEG) for Medical Applications: A Review.

Health data science, 3:0096.

Importance: Brain-computer interface (BCI) decodes and converts brain signals into machine instructions to interoperate with the external world. However, limited by the implantation risks of invasive BCIs and the operational complexity of conventional noninvasive BCIs, applications of BCIs are mainly used in laboratory or clinical environments, which are not conducive to the daily use of BCI devices. With the increasing demand for intelligent medical care, the development of wearable BCI systems is necessary. Highlights: Based on the scalp-electroencephalogram (EEG), forehead-EEG, and ear-EEG, the state-of-the-art wearable BCI devices for disease management and patient assistance are reviewed. This paper focuses on the EEG acquisition equipment of the novel wearable BCI devices and summarizes the development direction of wearable EEG-based BCI devices. Conclusions: BCI devices play an essential role in the medical field. This review briefly summarizes novel wearable EEG-based BCIs applied in the medical field and the latest progress in related technologies, emphasizing its potential to help doctors, patients, and caregivers better understand and utilize BCI devices.

RevDate: 2024-03-16

Wu H, Cai C, Ming W, et al (2024)

Speech decoding using cortical and subcortical electrophysiological signals.

Frontiers in neuroscience, 18:1345308.

INTRODUCTION: Language impairments often result from severe neurological disorders, driving the development of neural prosthetics utilizing electrophysiological signals to restore comprehensible language. Previous decoding efforts primarily focused on signals from the cerebral cortex, neglecting subcortical brain structures' potential contributions to speech decoding in brain-computer interfaces.

METHODS: In this study, stereotactic electroencephalography (sEEG) was employed to investigate subcortical structures' role in speech decoding. Two native Mandarin Chinese speakers, undergoing sEEG implantation for epilepsy treatment, participated. Participants read Chinese text, with 1-30, 30-70, and 70-150 Hz frequency band powers of sEEG signals extracted as key features. A deep learning model based on long short-term memory assessed the contribution of different brain structures to speech decoding, predicting consonant articulatory place, manner, and tone within single syllable.

RESULTS: Cortical signals excelled in articulatory place prediction (86.5% accuracy), while cortical and subcortical signals performed similarly for articulatory manner (51.5% vs. 51.7% accuracy). Subcortical signals provided superior tone prediction (58.3% accuracy). The superior temporal gyrus was consistently relevant in speech decoding for consonants and tone. Combining cortical and subcortical inputs yielded the highest prediction accuracy, especially for tone.

DISCUSSION: This study underscores the essential roles of both cortical and subcortical structures in different aspects of speech decoding.

RevDate: 2024-03-16

Juan JV, Martínez R, Iáñez E, et al (2024)

Exploring EEG-based motor imagery decoding: a dual approach using spatial features and spectro-spatial Deep Learning model IFNet.

Frontiers in neuroinformatics, 18:1345425.

INTRODUCTION: In recent years, the decoding of motor imagery (MI) from electroencephalography (EEG) signals has become a focus of research for brain-machine interfaces (BMIs) and neurorehabilitation. However, EEG signals present challenges due to their non-stationarity and the substantial presence of noise commonly found in recordings, making it difficult to design highly effective decoding algorithms. These algorithms are vital for controlling devices in neurorehabilitation tasks, as they activate the patient's motor cortex and contribute to their recovery.

METHODS: This study proposes a novel approach for decoding MI during pedalling tasks using EEG signals. A widespread approach is based on feature extraction using Common Spatial Patterns (CSP) followed by a linear discriminant analysis (LDA) as a classifier. The first approach covered in this work aims to investigate the efficacy of a task-discriminative feature extraction method based on CSP filter and LDA classifier. Additionally, the second alternative hypothesis explores the potential of a spectro-spatial Convolutional Neural Network (CNN) to further enhance the performance of the first approach. The proposed CNN architecture combines a preprocessing pipeline based on filter banks in the frequency domain with a convolutional neural network for spectro-temporal and spectro-spatial feature extraction.

RESULTS AND DISCUSSION: To evaluate the approaches and their advantages and disadvantages, EEG data has been recorded from several able-bodied users while pedalling in a cycle ergometer in order to train motor imagery decoding models. The results show levels of accuracy up to 80% in some cases. The CNN approach shows greater accuracy despite higher instability.

RevDate: 2024-03-21
CmpDate: 2024-03-21

Ke Y, Liu S, D Ming (2024)

Enhancing SSVEP Identification With Less Individual Calibration Data Using Periodically Repeated Component Analysis.

IEEE transactions on bio-medical engineering, 71(4):1319-1331.

OBJECTIVE: Spatial filtering and template matching-based steady-state visually evoked potentials (SSVEP) identification methods usually underperform in SSVEP identification with small-sample-size calibration data, especially when a single trial of data is available for each stimulation frequency.

METHODS: In contrast to the state-of-the-art task-related component analysis (TRCA)-based methods, which construct spatial filters and SSVEP templates based on the inter-trial task-related components in SSVEP, this study proposes a method called periodically repeated component analysis (PRCA), which constructs spatial filters to maximize the reproducibility across periods and constructs synthetic SSVEP templates by replicating the periodically repeated components (PRCs). We also introduced PRCs into two improved variants of TRCA. Performance evaluation was conducted in a self-collected 16-target dataset, a public 40-target dataset, and an online experiment.

RESULTS: The proposed methods show significant performance improvements with less training data and can achieve comparable performance to the baseline methods with 5 trials by using 2 or 3 training trials. Using a single trial of calibration data for each frequency, the PRCA-based methods achieved the highest average accuracies of over 95% and 90% with a data length of 1 s and maximum average information transfer rates (ITR) of 198.8±57.3 bits/min and 191.2±48.1 bits/min for the two datasets, respectively. Averaged online accuracy of 94.00 ± 7.35% and ITR of 139.73±21.04 bits/min were achieved with 0.5-s calibration data per frequency.

SIGNIFICANCE: Our results demonstrate the effectiveness and robustness of PRCA-based methods for SSVEP identification with reduced calibration effort and suggest its potential for practical applications in SSVEP-BCIs.

RevDate: 2024-03-17

Gao Y, Liu T, Hong T, et al (2024)

Subwavelength dielectric waveguide for efficient travelling-wave magnetic resonance imaging.

Nature communications, 15(1):2298.

Magnetic resonance imaging (MRI) has diverse applications in physics, biology, and medicine. Uniform excitation of nuclei spins through circular-polarized transverse magnetic component of electromagnetic field is vital for obtaining unbiased tissue contrasts. However, achieving this in the electrically large human body poses a significant challenge, especially at ultra-high fields (UHF) with increased working frequencies (≥297 MHz). Canonical volume resonators struggle to meet this challenge, while radiative excitation methods like travelling-wave (TW) show promise but often suffer from inadequate excitation efficiency. Here, we introduce a new technique using a subwavelength dielectric waveguide insert that enhances both efficiency and homogeneity at 7 T. Through TE11-to-TM11 mode conversion, power focusing, wave impedance matching, and phase velocity matching, we achieved a 114% improvement in TW efficiency and mitigated the center-brightening effect. This fundamental advancement in TW MRI through effective wave manipulation could promote the electromagnetic design of UHF MRI systems.

RevDate: 2024-03-14

Marchi A, Guex R, Denis M, et al (2024)

Neurofeedback and epilepsy: Renaissance of an old self-regulation method?.

Revue neurologique pii:S0035-3787(24)00469-7 [Epub ahead of print].

Neurofeedback is a brain-computer interface tool enabling the user to self-regulate their neuronal activity, and ultimately, induce long-term brain plasticity, making it an interesting instrument to cure brain disorders. Although this method has been used successfully in the past as an adjunctive therapy in drug-resistant epilepsy, this approach remains under-explored and deserves more rigorous scientific inquiry. In this review, we present early neurofeedback protocols employed in epilepsy and provide a critical overview of the main clinical studies. We also describe the potential neurophysiological mechanisms through which neurofeedback may produce its therapeutic effects. Finally, we discuss how to innovate and standardize future neurofeedback clinical trials in epilepsy based on evidence from recent research studies.

RevDate: 2024-03-15

Cai XL, Ye Q, Ni K, et al (2024)

Chinese version of the Perth Alexithymia Questionnaire: psychometric properties and clinical applications.

General psychiatry, 37(2):e101281.

BACKGROUND: The alexithymia trait is of high clinical interest. The Perth Alexithymia Questionnaire (PAQ) was recently developed to enable detailed facet-level and valence-specific assessments of alexithymia.

AIMS: In this paper, we introduce the first Chinese version of the PAQ and examine its psychometric properties and clinical applications.

METHODS: In Study 1, the PAQ was administered to 990 Chinese participants. We examined its factor structure, internal consistency, test-retest reliability, as well as convergent, concurrent and discriminant validity. In Study 2, four groups, including a major depressive disorder (MDD) group (n=50), a matched healthy control group for MDD (n=50), a subclinical depression group (n=50) and a matched healthy control group for subclinical depression (n=50), were recruited. Group comparisons were conducted to assess the clinical relevance of the PAQ.

RESULTS: In Study 1, the intended five-factor structure of the PAQ was found to fit the data well. The PAQ showed good internal consistency and test-retest reliability, as well as good convergent, concurrent and discriminant validity. In Study 2, the PAQ was able to successfully distinguish the MDD group and the subclinical depression group from their matched healthy controls.

CONCLUSIONS: The Chinese version of the PAQ is a valid and reliable instrument for comprehensively assessing alexithymia in the general population and adults with clinical/subclinical depression.

RevDate: 2024-03-17
CmpDate: 2024-03-15

Chen PC, Tsai TP, Liao YC, et al (2024)

Intestinal dual-specificity phosphatase 6 regulates the cold-induced gut microbiota remodeling to promote white adipose browning.

NPJ biofilms and microbiomes, 10(1):22.

Gut microbiota rearrangement induced by cold temperature is crucial for browning in murine white adipose tissue. This study provides evidence that DUSP6, a host factor, plays a critical role in regulating cold-induced gut microbiota rearrangement. When exposed to cold, the downregulation of intestinal DUSP6 increased the capacity of gut microbiota to produce ursodeoxycholic acid (UDCA). The DUSP6-UDCA axis is essential for driving Lachnospiraceae expansion in the cold microbiota. In mice experiencing cold-room temperature (CR) transitions, prolonged DUSP6 inhibition via the DUSP6 inhibitor (E/Z)-BCI maintained increased cecal UDCA levels and cold-like microbiota networks. By analyzing DUSP6-regulated microbiota dynamics in cold-exposed mice, we identified Marvinbryantia as a genus whose abundance increased in response to cold exposure. When inoculated with human-origin Marvinbryantia formatexigens, germ-free recipient mice exhibited significantly enhanced browning phenotypes in white adipose tissue. Moreover, M. formatexigens secreted the methylated amino acid Nε-methyl-L-lysine, an enriched cecal metabolite in Dusp6 knockout mice that reduces adiposity and ameliorates nonalcoholic steatohepatitis in mice. Our work revealed that host-microbiota coadaptation to cold environments is essential for regulating the browning-promoting gut microbiome.

RevDate: 2024-03-20

Ping A, Wang J, García-Cabezas MÁ, et al (2024)

Brainwide mesoscale functional networks revealed by focal infrared neural stimulation of the amygdala.

bioRxiv : the preprint server for biology.

The primate amygdala serves to evaluate emotional content of sensory inputs and modulate emotional and social behaviors; prefrontal, multisensory and autonomic aspects of these circuits are mediated predominantly via the basal (BA), lateral (LA), and central (CeA) nuclei, respectively. Based on recent electrophysiological evidence suggesting mesoscale (millimeters-scale) nature of intra-amygdala functional organization, we have investigated the connectivity of these nuclei using infrared neural stimulation (INS) of single mesoscale sites coupled with mapping in ultrahigh field 7T functional magnetic resonance imaging (fMRI), namely INS-fMRI. Following stimulation of multiple sites within amygdala of single individuals, a 'mesoscale functional connectome' of amygdala connectivity (of BA, LA, and CeA) was obtained. This revealed the mesoscale nature of connected sites, the spatial patterns of functional connectivity, and the topographic relationships (parallel, sequential, or interdigitating) of nucleus-specific connections. These findings provide novel perspectives on the brainwide circuits modulated by the amygdala.

RevDate: 2024-03-13

Alsuradi H, Khattak A, Fakhry A, et al (2024)

Individual-finger motor imagery classification: a data-driven approach with Shapley-informed augmentation.

Journal of neural engineering [Epub ahead of print].

OBJECTIVE: Classifying motor imagery (MI) tasks that involve fine motor control of the individual five fingers presents unique challenges when utilizing electroencephalography (EEG) data. In this paper, we systematically assess the classification of MI functions for the individual five fingers using single-trial time-domain EEG signals. This assessment encompasses both within-subject and cross-subject scenarios, supported by data-driven analysis that provides statistical validation of the neural correlate that could potentially discriminate between the five fingers.

APPROACH: We present Shapley-informed augmentation, an informed approach to enhance within-subject classification accuracy. This method is rooted in insights gained from our data-driven analysis, which revealed inconsistent temporal features encoding the five fingers MI across sessions of the same subject. To evaluate its impact, we compare within-subject classification performance both before and after implementing this augmentation technique.

MAIN RESULTS: Both the data-driven approach and the model explainability analysis revealed that the parietal cortex contains neural information that helps discriminate the individual five fingers' MI apart. Shapley-informed augmentation successfully improved classification accuracy in sessions severely affected by inconsistent temporal features. The accuracy for sessions impacted by inconsistency in their temporal features increased by an average of 26.3% ± 6.70, thereby enabling a broader range of subjects to benefit from brain-computer interaction (BCI) applications involving five-fingers MI classification. Conversely, non-impacted sessions experienced only a negligible average accuracy decrease of 2.01 ± 5.44%. The average classification accuracy achieved is around 60.0% (within-session), 50.0% (within-subject) and 40.0% (leave-one-subject-out).

SIGNIFICANCE: This research offers data-driven evidence of neural correlates that could discriminate between the individual five fingers MI and introduces a novel Shapley-informed augmentation method to address temporal variability of features, ultimately contributing to the development of personalized systems.

RevDate: 2024-03-15

Liu W, Mei T, Cao Z, et al (2024)

Bioinspired carbon nanotube-based nanofluidic ionic transistor with ultrahigh switching capabilities for logic circuits.

Science advances, 10(11):eadj7867.

The voltage-gated ion channels, also known as ionic transistors, play substantial roles in biological systems and ion-ion selective separation. However, implementing the ultrafast switchable capabilities and polarity switching of ionic transistors remains a challenge. Here, we report a nanofluidic ionic transistor based on carbon nanotubes, which exhibits an on/off ratio of 10[4] at operational gate voltage as low as 1 V. By controlling the morphology of carbon nanotubes, both unipolar and ambipolar ionic transistors are realized, and their on/off ratio can be further improved by introducing an Al2O3 dielectric layer. Meanwhile, this ionic transistor enables the polarity switching between p-type and n-type by controlled surface properties of carbon nanotubes. The implementation of constructing ionic circuits based on ionic transistors is demonstrated, which enables the creation of NOT, NAND, and NOR logic gates. The ionic transistors are expected to have profound implications for low-energy consumption computing devices and brain-machine interfacing.

RevDate: 2024-03-14

Lv Z, Liu X, Dai M, et al (2024)

Investigating critical brain area for EEG-based binocular color fusion and rivalry with EEGNet.

Frontiers in neuroscience, 18:1361486.

INTRODUCTION: Binocular color fusion and rivalry are two specific phenomena in binocular vision, which could be used as experimental tools to study how the brain processes conflicting information. There is a lack of objective evaluation indexes to distinguish the fusion or rivalry for dichoptic color.

METHODS: This paper introduced EEGNet to construct an EEG-based model for binocular color fusion and rivalry classification. We developed an EEG dataset from 10 subjects.

RESULTS: By dividing the EEG data from five different brain areas to train the corresponding models, experimental results showed that: (1) the brain area represented by the back area had a large difference on EEG signals, the accuracy of model reached the highest of 81.98%, and more channels decreased the model performance; (2) there was a large effect of inter-subject variability, and the EEG-based recognition is still a very challenge across subjects; and (3) the statistics of EEG data are relatively stationary at different time for the same individual, the EEG-based recognition is highly reproducible for an individual.

DISCUSSION: The critical channels for EEG-based binocular color fusion and rivalry could be meaningful for developing the brain computer interfaces (BCIs) based on color-related visual evoked potential (CVEP).

RevDate: 2024-03-15
CmpDate: 2024-03-14

Huang J, Li G, Zhang Q, et al (2024)

Adaptive Time-Frequency Segment Optimization for Motor Imagery Classification.

Sensors (Basel, Switzerland), 24(5):.

Motor imagery (MI)-based brain-computer interface (BCI) has emerged as a crucial method for rehabilitating stroke patients. However, the variability in the time-frequency distribution of MI-electroencephalography (EEG) among individuals limits the generalizability of algorithms that rely on non-customized time-frequency segments. In this study, we propose a novel method for optimizing time-frequency segments of MI-EEG using the sparrow search algorithm (SSA). Additionally, we apply a correlation-based channel selection (CCS) method that considers the correlation coefficient of features between each pair of EEG channels. Subsequently, we utilize a regularized common spatial pattern method to extract effective features. Finally, a support vector machine is employed for signal classification. The results on three BCI datasets confirmed that our algorithm achieved better accuracy (99.11% vs. 94.00% for BCI Competition III Dataset IIIa, 87.70% vs. 81.10% for Chinese Academy of Medical Sciences dataset, and 87.94% vs. 81.97% for BCI Competition IV Dataset 1) compared to algorithms with non-customized time-frequency segments. Our proposed algorithm enables adaptive optimization of EEG time-frequency segments, which is crucial for the development of clinically effective motor rehabilitation.

RevDate: 2024-03-13

Jiao Y, Zheng Q, Qiao D, et al (2024)

EEG rhythm separation and time-frequency analysis of fast multivariate empirical mode decomposition for motor imagery BCI.

Biological cybernetics [Epub ahead of print].

Motor imagery electroencephalogram (EEG) is widely employed in brain-computer interface (BCI) systems. As a time-frequency analysis method for nonlinear and non-stationary signals, multivariate empirical mode decomposition (MEMD) and its noise-assisted version (NA-MEMD) has been widely used in the preprocessing step of BCI systems for separating EEG rhythms corresponding to specific brain activities. However, when applied to multichannel EEG signals, MEMD or NA-MEMD often demonstrate low robustness to noise and high computational complexity. To address these issues, we have explored the advantages of our recently proposed fast multivariate empirical mode decomposition (FMEMD) and its noise-assisted version (NA-FMEMD) for analyzing motor imagery data. We emphasize that FMEMD enables a more accurate estimation of EEG frequency information and exhibits a more noise-robust decomposition performance with improved computational efficiency. Comparative analysis with MEMD on simulation data and real-world EEG validates the above assertions. The joint average frequency measure is employed to automatically select intrinsic mode functions that correspond to specific frequency bands. Thus, FMEMD-based classification architecture is proposed. Using FMEMD as a preprocessing algorithm instead of MEMD can improve the classification accuracy by 2.3% on the BCI Competition IV dataset. On the Physiobank Motor/Mental Imagery dataset and BCI Competition IV Dataset 2a, FMEMD-based architecture also attained a comparable performance to complex algorithms. The results indicate that FMEMD proficiently extracts feature information from small benchmark datasets while mitigating dimensionality constraints resulting from computational complexity. Hence, FMEMD or NA-FMEMD can be a powerful time-frequency preprocessing method for BCI.

RevDate: 2024-03-18

Sintas JI, Bean RH, Zhang R, et al (2024)

Nonisocyanate Polyurethane Segmented Copolymers from Bis-Carbonylimidazolides.

Macromolecular rapid communications [Epub ahead of print].

Bis-carbonylimidazolide (BCI) functionalization enables an efficient synthetic strategy to generate high molecular weight segmented nonisocyanate polyurethanes (NIPUs). Melt phase polymerization of ED-2003 Jeffamine, 4,4'-methylenebis(cyclohexylamine), and a BCI monomer that mimics a 1,4-butanediol chain extender enables polyether NIPUs that contain varying concentrations of hard segments ranging from 40 to 80 wt. %. Dynamic mechanical analysis and differential scanning calorimetry reveal thermal transitions for soft, hard, and mixed phases. Hard segment incorporations between 40 and 60 wt. % display up to three distinct phases pertaining to the poly(ethylene glycol) (PEG) soft segment Tg , melting transition, and hard segment Tg , while higher hard segment concentrations prohibit soft segment crystallization, presumably due to restricted molecular mobility from the hard segment. Atomic force microscopy allows for visualization and size determination of nanophase-separated regimes, revealing a nanoscale rod-like assembly of HS. Small-angle X-ray scattering confirms nanophase separation within the NIPU, characterizing both nanoscale amorphous domains and varying degrees of crystallinity. These NIPUs, which are synthesized with BCI monomers, display expected phase separation that is comparable to isocyanate-derived analogues. This work demonstrates nanophase separation in BCI-derived NIPUs and the feasibility of this nonisocyanate synthetic pathway for the preparation of segmented PU copolymers.

RevDate: 2024-03-13

Pan H, Wang Y, Li Z, et al (2024)

A complete scheme for multi-character classification using EEG signals from speech imagery.

IEEE transactions on bio-medical engineering, PP: [Epub ahead of print].

Some classification studies of brain-computer interface (BCI) based on speech imagery show potential for improving communication skills in patients with amyotrophic lateral sclerosis (ALS). However, current research on speech imagery is limited in scope and primarily focuses on vowels or a few selected words. In this paper, we propose a complete research scheme for multi-character classification based on EEG signals derived from speech imagery. Firstly, we record 31 speech imagery contents, including 26 alphabets and 5 commonly used punctuation marks, from seven subjects using a 32-channel electroencephalogram (EEG) device. Secondly, we introduce the wavelet scattering transform (WST), which shares a structural resemblance to Convolutional Neural Networks (CNNs), for feature extraction. The WST is a knowledge-driven technique that preserves high-frequency information and maintains the deformation stability of EEG signals. To reduce the dimensionality of wavelet scattering coefficient features, we employ Kernel Principal Component Analysis (KPCA). Finally, the reduced features are fed into an Extreme Gradient Boosting (XGBoost) classifier within a multi-classification framework. The XGBoost classifier is optimized through hyperparameter tuning using grid search and 10-fold cross-validation, resulting in an average accuracy of 78.73% for the multi-character classification task. We utilize t-Distributed Stochastic Neighbor Embedding (t-SNE) technology to visualize the low-dimensional representation of multi-character speech imagery. This visualization effectively enables us to observe the clustering of similar characters. The experimental results demonstrate the effectiveness of our proposed multi-character classification scheme. Furthermore, our classification categories and accuracy exceed those reported in existing research.

RevDate: 2024-03-13

Larsen OFP, Tresselt WG, Lorenz EA, et al (2024)

A method for synchronized use of EEG and eye tracking in fully immersive VR.

Frontiers in human neuroscience, 18:1347974.

This study explores the synchronization of multimodal physiological data streams, in particular, the integration of electroencephalography (EEG) with a virtual reality (VR) headset featuring eye-tracking capabilities. A potential use case for the synchronized data streams is demonstrated by implementing a hybrid steady-state visually evoked potential (SSVEP) based brain-computer interface (BCI) speller within a fully immersive VR environment. The hardware latency analysis reveals an average offset of 36 ms between EEG and eye-tracking data streams and a mean jitter of 5.76 ms. The study further presents a proof of concept brain-computer interface (BCI) speller in VR, showcasing its potential for real-world applications. The findings highlight the feasibility of combining commercial EEG and VR technologies for neuroscientific research and open new avenues for studying brain activity in ecologically valid VR environments. Future research could focus on refining the synchronization methods and exploring applications in various contexts, such as learning and social interactions.

RevDate: 2024-03-14

Huang Q, Ellis CL, Leo SM, et al (2024)

Retinal GABAergic alterations in adults with autism spectrum disorder.

The Journal of neuroscience : the official journal of the Society for Neuroscience pii:JNEUROSCI.1218-23.2024 [Epub ahead of print].

Alterations in γ-aminobutyric acid (GABA) have been implicated in sensory differences in individuals with autism spectrum disorder (ASD). Visual signals are initially processed in the retina and in this study we explored the hypotheses that the GABA-dependent retinal response to light is altered in individuals with ASD. Light-adapted electroretinograms (ERGs) were recorded from 61 adults (38 males and 23 females; n = 22 ASD) in response to three stimulus protocols: i) the standard white flash; ii) the standard 30-Hz flickering protocol; iii) the photopic negative response (PhNR) protocol. Participants were administered an oral dose of placebo, 15 or 30 mg of arbaclofen (STX209, GABAB agonist) in a randomized, double-blind, cross-over order before the test. At baseline (placebo), the a-wave amplitudes in response to single white flashes were more prominent in ASD, relative to typically developed (TD) participants. Arbaclofen was associated with decrease in the a-wave amplitude in ASD, but an increase in TD, eliminating the group difference observed at baseline. The extent of this arbaclofen-elicited shift significantly correlated with the arbaclofen-elicited shift in cortical responses to auditory stimuli as measured by electroencephalogram in our prior study, and with broader autistic traits measured with the Autism Quotient across the whole cohort. Hence, GABA-dependent differences in retinal light processing in ASD appear to be an accessible component of a wider autistic difference in central processing of sensory information, which may be upstream of more complex autistic phenotypes.Significance Statement Our current study provides the first direct in vivo experimental confirmation that autistic alterations in central GABA function extend to the retina. We show that arbaclofen was associated with reduced flash elicited a-wave amplitude in the electroretinogram (ERG) of autistic individuals but increased amplitude in non-autistic people. The retinal arbaclofen response correlated with previously reported arbaclofen effects on cortical visual and auditory responses in the same individuals. The extent of this differential GABAergic function correlated with the extent of autistic traits captured using the Autism Quotient. Thus, sensory processing differences in autism appear to be upstream of more complex autistic traits and the ERG from the retina is a potentially useful proxy for cross-domain brain GABA function and target engagement.

RevDate: 2024-03-12

Zhang X, Zhang T, Jiang Y, et al (2024)

A novel brain-controlled prosthetic hand method integrating AR-SSVEP augmentation, asynchronous control, and machine vision assistance.

Heliyon, 10(5):e26521.

BACKGROUND AND OBJECTIVE: The brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) is expected to help disabled patients achieve alternative prosthetic hand assistance. However, the existing study still has some shortcomings in interaction aspects such as stimulus paradigm and control logic. The purpose of this study is to innovate the visual stimulus paradigm and asynchronous decoding/control strategy by integrating augmented reality technology, and propose an asynchronous pattern recognition algorithm, thereby improving the interaction logic and practical application capabilities of the prosthetic hand with the BCI system.

METHODS: An asynchronous visual stimulus paradigm based on an augmented reality (AR) interface was proposed in this paper, in which there were 8 control modes, including Grasp, Put down, Pinch, Point, Fist, Palm push, Hold pen, and Initial. According to the attentional orienting characteristics of the paradigm, a novel asynchronous pattern recognition algorithm that combines center extended canonical correlation analysis and support vector machine (Center-ECCA-SVM) was proposed. Then, this study proposed an intelligent BCI system switch based on a deep learning object detection algorithm (YOLOv4) to improve the level of user interaction. Finally, two experiments were designed to test the performance of the brain-controlled prosthetic hand system and its practical performance in real scenarios.

RESULTS: Under the AR paradigm of this study, compared with the liquid crystal display (LCD) paradigm, the average SSVEP spectrum amplitude of multiple subjects increased by 17.41%, and the signal-noise ratio (SNR) increased by 3.52%. The average stimulus pattern recognition accuracy was 96.71 ± 3.91%, which was 2.62% higher than the LCD paradigm. Under the data analysis time of 2s, the Center-ECCA-SVM classifier obtained 94.66 ± 3.87% and 97.40 ± 2.78% asynchronous pattern recognition accuracy under the Normal metric and the Tolerant metric, respectively. And the YOLOv4-tiny model achieves a speed of 25.29fps and a 96.4% confidence in the prosthetic hand in real-time detection. Finally, the brain-controlled prosthetic hand helped the subjects to complete 4 kinds of daily life tasks in the real scene, and the time-consuming were all within an acceptable range, which verified the effectiveness and practicability of the system.

CONCLUSION: This research is based on improving the user interaction level of the prosthetic hand with the BCI system, and has made improvements in the SSVEP paradigm, asynchronous pattern recognition, interaction, and control logic. Furthermore, it also provides support for BCI areas for alternative prosthetic control, and movement disorder rehabilitation programs.

RevDate: 2024-03-08

da Cruz SP, da Cruz SP, Pereira S, et al (2024)

Vitamin D and the Metabolic Phenotype in Weight Loss After Bariatric Surgery: A Longitudinal Study.

Obesity surgery [Epub ahead of print].

PURPOSE: To evaluate the influence of vitamin D (VD) concentrations coupled with metabolic phenotypes preoperatively and 6 months after Roux-en-Y gastric bypass (RYGB) on body variables and weight loss.

MATERIALS AND METHODS: A longitudinal, retrospective, analytical study comprising 30 adult individuals assessed preoperatively (T0) and 6 months (T1) after undergoing Roux-en-Y gastric bypass. The participants were distributed preoperatively into metabolically healthy obese (MHO) and metabolically unhealthy obese individuals (MUHO) according to the HOMA-IR classification, as well as the adequacy and inadequacy of vitamin D concentrations in the form of 25(OH)D. All participants were assessed for weight, height, body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHtR), visceral adiposity index (VAI), body circularity index (BCI), body adiposity index (BAI), weight loss, and assessment of 25(OH)D and 1,25(OH)2D concentrations using high-performance liquid chromatography with an ultraviolet detector (HPLC-UV). The statistical program used was SPSS version 21.

RESULTS: VD adequacy and a healthy phenotype in the preoperative period may play an important role concerning body fat distribution, as the body averages for WHtR (0.020*) and BCI (0.020*) were lower in MHO participants. In comparison, those with VD inadequacy and MUHOs had higher BAI averages (0.000*) in the postoperative period. Furthermore, it is possible that VD inadequacy before and after RYGB, even in the presence of an unhealthy phenotype, may contribute to the increase in VAI values (0.029*) after this surgery. Only those with inadequate VD and MUHOs had higher 25(OH)D concentrations. Besides, this unhealthy phenotype had a greater reduction in BMI in the early postoperative period (p < 0.001).

CONCLUSION: This study suggests that VD adequacy and the presence of a healthy phenotype appear to have a positive impact on the reduction of visceral fat in the context of pre- and postoperative obesity. In addition, there was a greater weight reduction in those with VD inadequacy and in MUHO, which suggests that the volumetric dilution effect of VD and catabolism after bariatric surgery is more pronounced in this specific metabolic phenotype.

RevDate: 2024-03-12

Lin S, Fan CY, Wang HR, et al (2024)

Frontostriatal circuit dysfunction leads to cognitive inflexibility in neuroligin-3 R451C knockin mice.

Molecular psychiatry [Epub ahead of print].

Cognitive and behavioral rigidity are observed in various psychiatric diseases, including in autism spectrum disorder (ASD). However, the underlying mechanism remains to be elucidated. In this study, we found that neuroligin-3 (NL3) R451C knockin mouse model of autism (KI mice) exhibited deficits in behavioral flexibility in choice selection tasks. Single-unit recording of medium spiny neuron (MSN) activity in the nucleus accumbens (NAc) revealed altered encoding of decision-related cue and impaired updating of choice anticipation in KI mice. Additionally, fiber photometry demonstrated significant disruption in dynamic mesolimbic dopamine (DA) signaling for reward prediction errors (RPEs), along with reduced activity in medial prefrontal cortex (mPFC) neurons projecting to the NAc in KI mice. Interestingly, NL3 re-expression in the mPFC, but not in the NAc, rescued the deficit of flexible behaviors and simultaneously restored NAc-MSN encoding, DA dynamics, and mPFC-NAc output in KI mice. Taken together, this study reveals the frontostriatal circuit dysfunction underlying cognitive inflexibility and establishes a critical role of the mPFC NL3 deficiency in this deficit in KI mice. Therefore, these findings provide new insights into the mechanisms of cognitive and behavioral inflexibility and potential intervention strategies.

RevDate: 2024-03-09

Song SS, Druschel LN, Conard JH, et al (2024)

Depletion of complement factor 3 delays the neuroinflammatory response to intracortical microelectrodes.

Brain, behavior, and immunity, 118:221-235 pii:S0889-1591(24)00284-8 [Epub ahead of print].

The neuroinflammatory response to intracortical microelectrodes (IMEs) used with brain-machine interfacing (BMI) applications is regarded as the primary contributor to poor chronic performance. Recent developments in high-plex gene expression technologies have allowed for an evolution in the investigation of individual proteins or genes to be able to identify specific pathways of upregulated genes that may contribute to the neuroinflammatory response. Several key pathways that are upregulated following IME implantation are involved with the complement system. The complement system is part of the innate immune system involved in recognizing and eliminating pathogens - a significant contributor to the foreign body response against biomaterials. Specifically, we have identified Complement 3 (C3) as a gene of interest because it is the intersection of several key complement pathways. In this study, we investigated the role of C3 in the IME inflammatory response by comparing the neuroinflammatory gene expression at the microelectrode implant site between C3 knockout (C3[-/-]) and wild-type (WT) mice. We have found that, like in WT mice, implantation of intracortical microelectrodes in C3[-/-] mice yields a dramatic increase in the neuroinflammatory gene expression at all post-surgery time points investigated. However, compared to WT mice, C3 depletion showed reduced expression of many neuroinflammatory genes pre-surgery and 4 weeks post-surgery. Conversely, depletion of C3 increased the expression of many neuroinflammatory genes at 8 weeks and 16 weeks post-surgery, compared to WT mice. Our results suggest that C3 depletion may be a promising therapeutic target for acute, but not chronic, relief of the neuroinflammatory response to IME implantation. Additional compensatory targets may also be required for comprehensive long-term reduction of the neuroinflammatory response for improved intracortical microelectrode performance.

RevDate: 2024-03-10

Deng H, Li M, Li J, et al (2024)

A robust multi-branch multi-attention-mechanism EEGNet for motor imagery BCI decoding.

Journal of neuroscience methods, 405:110108 pii:S0165-0270(24)00053-0 [Epub ahead of print].

BACKGROUND: Motor-Imagery-based Brain-Computer Interface (MI-BCI) is a promising technology to assist communication, movement, and neurological rehabilitation for motor-impaired individuals. Electroencephalography (EEG) decoding techniques using deep learning (DL) possess noteworthy advantages due to automatic feature extraction and end-to-end learning. However, the DL-based EEG decoding models tend to show large variations due to intersubject variability of EEG, which results from inconsistencies of different subjects' optimal hyperparameters.

NEW METHODS: This study proposes a multi-branch multi-attention mechanism EEGNet model (MBMANet) for robust decoding. It applies the multi-branch EEGNet structure to achieve various feature extractions. Further, the different attention mechanisms introduced in each branch attain diverse adaptive weight adjustments. This combination of multi-branch and multi-attention mechanisms allows for multi-level feature fusion to provide robust decoding for different subjects.

RESULTS: The MBMANet model has a four-classification accuracy of 83.18% and kappa of 0.776 on the BCI Competition IV-2a dataset, which outperforms other eight CNN-based decoding models. This consistently satisfactory performance across all nine subjects indicates that the proposed model is robust.

CONCLUSIONS: The combine of multi-branch and multi-attention mechanisms empowers the DL-based models to adaptively learn different EEG features, which provides a feasible solution for dealing with data variability. It also gives the MBMANet model more accurate decoding of motion intentions and lower training costs, thus improving the MI-BCI's utility and robustness.

RevDate: 2024-03-08

Cao HL, Meng YJ, Zhang YM, et al (2024)

The volume of gray matter mediates the relationship between glucolipid metabolism and neurocognition in first-episode, drug-naïve patients with schizophrenia.

Journal of psychiatric research, 172:402-410 pii:S0022-3956(24)00127-4 [Epub ahead of print].

We aimed to examine the hypotheses that glucolipid metabolism is linked to neurocognition and gray matter volume (GMV) and that GMV mediates the association of glucolipid metabolism with neurocognition in first-episode, drug-naïve (FEDN) patients with schizophrenia. Parameters of glucolipid metabolism, neurocognition, and magnetic resonance imaging were assessed in 63 patients and 31 controls. Compared to controls, patients exhibited higher levels of fasting glucose, triglyceride, and insulin resistance index, lower levels of cholesterol and high-density lipoprotein cholesterol, poorer neurocognitive functions, and decreased GMV in the bilateral insula, left middle occipital gyrus, and left postcentral gyrus. In the patient group, triglyceride levels and the insulin resistance index exhibited a negative correlation with Rapid Visual Information Processing (RVP) mean latency, a measure of attention within the Cambridge Neurocognitive Test Automated Battery (CANTAB), while showing a positive association with GMV in the right insula. The mediation model revealed that triglyceride and insulin resistance index had a significant positive indirect (mediated) influence on RVP mean latency through GMV in the right insula. Glucolipid metabolism was linked to both neurocognitive functions and GMV in FEDN patients with schizophrenia, with the effect pattern differing from that observed in chronic schizophrenia or schizophrenia comorbid with metabolic syndrome. Moreover, glucolipid metabolism might indirectly contribute to neurocognitive deficits through the mediating role of GMV in these patients.

RevDate: 2024-03-08

Ma C, Li W, Ke S, et al (2024)

Identification of autism spectrum disorder using multiple functional connectivity-based graph convolutional network.

Medical & biological engineering & computing [Epub ahead of print].

Presently, the combination of graph convolutional networks (GCN) with resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising approach for early diagnosis of autism spectrum disorder (ASD). However, the prevalent approach involves exclusively full-brain functional connectivity data for disease classification using GCN, while overlooking the prior information related to the functional connectivity of brain subnetworks associated with ASD. Therefore, in this study, the multiple functional connectivity-based graph convolutional network (MFC-GCN) framework is proposed, using not only full brain functional connectivity data but also the established functional connectivity data from networks of key brain subnetworks associated with ASD, and the GCN is adopted to acquire complementary feature information for the final classification task. Given the heterogeneity within the Autism Brain Imaging Data Exchange (ABIDE) dataset, a novel External Attention Network Readout (EANReadout) is introduced. This design enables the exploration of potential subject associations, effectively addressing the dataset's heterogeneity. Experiments were conducted on the ABIDE dataset using the proposed framework, involving 714 subjects, and the average accuracy of the framework was 70.31%. The experimental results show that the proposed EANReadout outperforms the best traditional readout layer and improves the average accuracy of the framework by 4.32%.

RevDate: 2024-03-08

Majdi H, Azarnoosh M, Ghoshuni M, et al (2024)

Direct lingam and visibility graphs for analyzing brain connectivity in BCI.

Medical & biological engineering & computing [Epub ahead of print].

The brain-computer interface (BCI) is a direct pathway of communication between the electrical activity of the brain and an external device. The present paper was aimed to investigate directed connectivity between different areas of the brain during motor imagery (MI)-based BCI. For this purpose, two methods were implemented including, Limited Penetrable Horizontal Visibility Graph (LPHVG) and Direct Lingam. The visibility graph (VG) is a robust algorithm for analyzing complex systems such as the brain. Direct Lingam uses a non-Gaussian model to extract causal links which is appropriate for analyzing large-scale connectivity. First, LPHVG map MI-EEG (electroencephalogram) signals into networks. After extracting the topological features of the networks, a support vector machine classifier was applied to categorize multi-classes MI. The network of all classes was found to be different from one another, and the kappa value of classification was 0.68. The degree sequence of LPHVG was calculated for each channel in order to obtain the direction of brain information flow. Transfer entropy (TE) is used to compute the relations of the channel degree sequence. Therefore, the directed graph between channels was formed. This method is called LPHVG_TE directed graph. The Bayesian network, also known as the Direct LiNGAM model, was implemented for the second method. Finally, images of the LPHVG and Direct Lingam were classified by convolutional neural network (CNN). In this study, Data sets 2a of BCI competition IV was used. The outcomes reveal that the brain network developed by LPHVG (92.7%) might be more effective to distinguish 4 classes of MI than the Direct Lingam (90.6%) and it was shown that graph theory has the potential to get better efficiency of BCI.

RevDate: 2024-03-08

Meier K, de Vos CC, Bordeleau M, et al (2024)

Examining the Duration of Carryover Effect in Patients With Chronic Pain Treated With Spinal Cord Stimulation (EChO Study): An Open, Interventional, Investigator-Initiated, International Multicenter Study.

Neuromodulation : journal of the International Neuromodulation Society pii:S1094-7159(24)00036-9 [Epub ahead of print].

OBJECTIVES: Spinal cord stimulation (SCS) is a surgical treatment for severe, chronic, neuropathic pain. It is based on one to two lead(s) implanted in the epidural space, stimulating the dorsal column. It has long been assumed that when deactivating SCS, there is a variable interval before the patient perceives the return of the pain, a phenomenon often termed echo or carryover effect. Although the carryover effect has been problematized as a source of error in crossover studies, no experimental investigation of the effect has been published. This open, prospective, international multicenter study aimed to systematically document, quantify, and investigate the carryover effect in SCS.

MATERIALS AND METHODS: Eligible patients with a beneficial effect from their SCS treatment were instructed to deactivate their SCS device in a home setting and to reactivate it when their pain returned. The primary outcome was duration of carryover time defined as the time interval from deactivation to reactivation. Central clinical parameters (age, sex, indication for SCS, SCS treatment details, pain score) were registered and correlated with carryover time using nonparametric tests (Mann-Whitney/Kruskal-Wallis) for categorical data and linear regression for continuous data.

RESULTS: In total, 158 patients were included in the analyses. A median carryover time of five hours was found (interquartile range 2.5;21 hours). Back pain as primary indication for SCS, high-frequency stimulation, and higher pain score at the time of deactivation were correlated with longer carryover time.

CONCLUSIONS: This study confirms the existence of the carryover effect and indicates a remarkably high degree of interindividual variation. The results suggest that the magnitude of carryover may be correlated to the nature of the pain condition and possibly stimulation paradigms.

CLINICAL TRIAL REGISTRATION: The Clinicaltrials.gov registration number for the study is NCT03386058.

RevDate: 2024-03-11
CmpDate: 2024-03-11

Zhang Y, Wu X, Sun J, et al (2024)

Exploring changes in brain function in IBD patients using SPCCA: a study of simultaneous EEG-fMRI.

Mathematical biosciences and engineering : MBE, 21(2):2646-2670.

Research on functional changes in the brain of inflammatory bowel disease (IBD) patients is emerging around the world, which brings new perspectives to medical research. In this paper, the methods of canonical correlation analysis (CCA), kernel canonical correlation analysis (KCCA), and sparsity preserving canonical correlation analysis (SPCCA) were applied to the fusion of simultaneous EEG-fMRI data from 25 IBD patients and 15 healthy individuals. The CCA, KCCA and SPCCA fusion methods were used for data processing to compare the results obtained by the three methods. The results clearly show that there is a significant difference in the activation intensity between IBD and healthy control (HC), not only in the frontal lobe (p < 0.01) and temporal lobe (p < 0.01) regions, but also in the posterior cingulate gyrus (p < 0.01), gyrus rectus (p < 0.01), and amygdala (p < 0.01) regions, which are usually neglected. The mean difference in the SPCCA activation intensity was 60.1. However, the mean difference in activation intensity was only 36.9 and 49.8 by using CCA and KCCA. In addition, the correlation of the relevant components selected during the SPCCA calculation was high, with correlation components of up to 0.955; alternatively, the correlations obtained from CCA and KCCA calculations were only 0.917 and 0.926, respectively. It can be seen that SPCCA is indeed superior to CCA and KCCA in processing high-dimensional multimodal data. This work reveals the process of analyzing the brain activation state in IBD disease, provides a further perspective for the study of brain function, and opens up a new avenue for studying the SPCCA method and the change in the intensity of brain activation in IBD disease.

RevDate: 2024-03-11
CmpDate: 2024-03-11

Chen Y, Stephani T, Bagdasarian MT, et al (2024)

Realness of face images can be decoded from non-linear modulation of EEG responses.

Scientific reports, 14(1):5683.

Artificially created human faces play an increasingly important role in our digital world. However, the so-called uncanny valley effect may cause people to perceive highly, yet not perfectly human-like faces as eerie, bringing challenges to the interaction with virtual agents. At the same time, the neurocognitive underpinnings of the uncanny valley effect remain elusive. Here, we utilized an electroencephalography (EEG) dataset of steady-state visual evoked potentials (SSVEP) in which participants were presented with human face images of different stylization levels ranging from simplistic cartoons to actual photographs. Assessing neuronal responses both in frequency and time domain, we found a non-linear relationship between SSVEP amplitudes and stylization level, that is, the most stylized cartoon images and the real photographs evoked stronger responses than images with medium stylization. Moreover, realness of even highly similar stylization levels could be decoded from the EEG data with task-related component analysis (TRCA). Importantly, we also account for confounding factors, such as the size of the stimulus face's eyes, which previously have not been adequately addressed. Together, this study provides a basis for future research and neuronal benchmarking of real-time detection of face realness regarding three aspects: SSVEP-based neural markers, efficient classification methods, and low-level stimulus confounders.

RevDate: 2024-03-07

Yu Q, Liu L, Du M, et al (2024)

Sacral neural crest-independent origin of the enteric nervous system in mouse.

Gastroenterology pii:S0016-5085(24)00241-5 [Epub ahead of print].

BACKGROUND & AIMS: The enteric nervous system (ENS), the gut's intrinsic nervous system critical for gastrointestinal function and gut-brain communication, is believed to mainly originate from vagal neural crest cells (vNCCs) and partially from sacral NCCs (sNCCs). Resolving the exact origins of the ENS is critical for understanding congenital ENS diseases but has been confounded by the inability to distinguish between both NCC populations in situ. Here, we aimed to resolve the exact origins of the mammalian ENS.

METHODS: We genetically engineered mouse embryos facilitating comparative lineage-tracing of either all (pan-) NCCs including vNCCs or caudal trunk and sNCCs (s/tNCCs) excluding vNCCs. This was combined with dual-lineage tracing and 3D-reconstruction of pelvic plexus and hindgut to precisely pinpoint sNCC and vNCC contributions. We further employed co-culture assays to determine the specificity of cell migration from different neural tissues into the hindgut.

RESULTS: Both pan-NCCs and s/tNCCs contributed to established NCC derivatives but only pan-NCCs contributed to the ENS. Dual lineage-tracing combined with 3D-reconstruction revealed that s/tNCCs settle in complex patterns in pelvic plexus and hindgut-surrounding tissues, explaining previous confusion regarding their contributions. Co-culture experiments revealed unspecific cell migration from autonomic, sensory, and neural tube explants into the hindgut. Lineage-tracing of ENS precursors lastly provided complimentary evidence for an exclusive vNCC origin of the murine ENS.

CONCLUSIONS: sNCCs do not contribute to the murine ENS, suggesting that the mammalian ENS exclusively originates from vNCCs. These results have immediate implications for comprehending (and devising treatments for) congenital ENS disorders, including Hirschsprung's disease.

RevDate: 2024-03-08

Attallah O, Al-Kabbany A, Zaghlool SB, et al (2024)

Editorial: Immersive technology and ambient intelligence for assistive living, medical, and healthcare solutions.

Frontiers in human neuroscience, 18:1376959.

RevDate: 2024-03-08

Welter M, F Lotte (2024)

Ecological decoding of visual aesthetic preference with oscillatory electroencephalogram features-A mini-review.

Frontiers in neuroergonomics, 5:1341790.

In today's digital information age, human exposure to visual artifacts has reached an unprecedented quasi-omnipresence. Some of these cultural artifacts are elevated to the status of artworks which indicates a special appreciation of these objects. For many persons, the perception of such artworks coincides with aesthetic experiences (AE) that can positively affect health and wellbeing. AEs are composed of complex cognitive and affective mental and physiological states. More profound scientific understanding of the neural dynamics behind AEs would allow the development of passive Brain-Computer-Interfaces (BCI) that offer personalized art presentation to improve AE without the necessity of explicit user feedback. However, previous empirical research in visual neuroaesthetics predominantly investigated functional Magnetic Resonance Imaging and Event-Related-Potentials correlates of AE in unnaturalistic laboratory conditions which might not be the best features for practical neuroaesthetic BCIs. Furthermore, AE has, until recently, largely been framed as the experience of beauty or pleasantness. Yet, these concepts do not encompass all types of AE. Thus, the scope of these concepts is too narrow to allow personalized and optimal art experience across individuals and cultures. This narrative mini-review summarizes the state-of-the-art in oscillatory Electroencephalography (EEG) based visual neuroaesthetics and paints a road map toward the development of ecologically valid neuroaesthetic passive BCI systems that could optimize AEs, as well as their beneficial consequences. We detail reported oscillatory EEG correlates of AEs, as well as machine learning approaches to classify AE. We also highlight current limitations in neuroaesthetics and suggest future directions to improve EEG decoding of AE.

RevDate: 2024-03-08

Hu J (2024)

Augmented-reality based brain-computer interface of robot control.

Heliyon, 10(5):e26255.

Brain Computer Interface (BCI) is a new approach to human-computer interaction. It can control the external devices directly with the brain without words and body movements. Brain-controlled robot is a major research area in the field of BCI, which organically integrates BCI with robotic systems to achieve safe and effective real-time control of robots using the user's electroencephalogram (EEG). Currently, there are two types of control methods for brain-controlled robots. One is direct control and the other is shared control. Direct brain control has its shortcomings, namely, low control efficiency and easy user fatigue. Shared control technique can effectively improve the control of brain-controlled robots and reduce the thinking ability of brain-controlled robots, thus making it the main control method of brain-controlled robots. The brain-computer collaborative control system based on augmented reality (AR) technology studied in this paper is a human-computer shared control method. In the experimental analysis of virtual reality (VR) systems and AR systems, this paper processes polylines through a series of control vertices with specific coordinates, using the relative distance measured between each point and the starting point as the relative coordinates, and calculates the operational errors of the two types of systems. In the system error of machining broken lines, when the relative coordinates are (10, 20), (40, 50), and (70, 80), the error values of the VR system are 0.17 mm, 0.36 mm, and 0.55 mm, respectively, while the error values of the AR system are 0.11 mm, 0.24 mm, and 0.41 mm, respectively. Therefore, the studies have illustrated the importance of AR systems for the study of brain-computer collaborative control of robots.

RevDate: 2024-03-06

Vogel AP, Spencer C, Burke K, et al (2024)

Optimizing Communication in Ataxia: A Multifaceted Approach to Alternative and Augmentative Communication (AAC).

Cerebellum (London, England) [Epub ahead of print].

The progression of multisystem neurodegenerative diseases such as ataxia significantly impacts speech and communication, necessitating adaptive clinical care strategies. With the deterioration of speech, Alternative and Augmentative Communication (AAC) can play an ever increasing role in daily life for individuals with ataxia. This review describes the spectrum of AAC resources available, ranging from unaided gestures and sign language to high-tech solutions like speech-generating devices (SGDs) and eye-tracking technology. Despite the availability of various AAC tools, their efficacy is often compromised by the physical limitations inherent in ataxia, including upper limb ataxia and visual disturbances. Traditional speech-to-text algorithms and eye gaze technology face challenges in accuracy and efficiency due to the atypical speech and movement patterns associated with the disease.In addressing these challenges, maintaining existing speech abilities through rehabilitation is prioritized, complemented by advances in digital therapeutics to provide home-based treatments. Simultaneously, projects incorporating AI driven solutions aim to enhance the intelligibility of dysarthric speech through improved speech-to-text accuracy.This review discusses the complex needs assessment for AAC in ataxia, emphasizing the dynamic nature of the disease and the importance of regular reassessment to tailor communication strategies to the changing abilities of the individual. It also highlights the necessity of multidisciplinary involvement for effective AAC assessment and intervention. The future of AAC looks promising with developments in brain-computer interfaces and the potential of voice banking, although their application in ataxia requires further exploration.

RevDate: 2024-03-06

Cheng H, Chen D, Li X, et al (2024)

Phasic/tonic glial GABA differentially transduce for olfactory adaptation and neuronal aging.

Neuron pii:S0896-6273(24)00090-4 [Epub ahead of print].

Phasic (fast) and tonic (sustained) inhibition of γ-aminobutyric acid (GABA) are fundamental for regulating day-to-day activities, neuronal excitability, and plasticity. However, the mechanisms and physiological functions of glial GABA transductions remain poorly understood. Here, we report that the AMsh glia in Caenorhabditis elegans exhibit both phasic and tonic GABAergic signaling, which distinctively regulate olfactory adaptation and neuronal aging. Through genetic screening, we find that GABA permeates through bestrophin-9/-13/-14 anion channels from AMsh glia, which primarily activate the metabolic GABAB receptor GBB-1 in the neighboring ASH sensory neurons. This tonic action of glial GABA regulates the age-associated changes of ASH neurons and olfactory responses via a conserved signaling pathway, inducing neuroprotection. In addition, the calcium-evoked, vesicular glial GABA release acts upon the ionotropic GABAA receptor LGC-38 in ASH neurons to regulate olfactory adaptation. These findings underscore the fundamental significance of glial GABA in maintaining healthy aging and neuronal stability.

RevDate: 2024-03-08
CmpDate: 2024-03-08

Sabio J, Williams NS, McArthur GM, et al (2024)

A scoping review on the use of consumer-grade EEG devices for research.

PloS one, 19(3):e0291186.

BACKGROUND: Commercial electroencephalography (EEG) devices have become increasingly available over the last decade. These devices have been used in a wide variety of fields ranging from engineering to cognitive neuroscience.

PURPOSE: The aim of this study was to chart peer-review articles that used consumer-grade EEG devices to collect neural data. We provide an overview of the research conducted with these relatively more affordable and user-friendly devices. We also inform future research by exploring the current and potential scope of consumer-grade EEG.

METHODS: We followed a five-stage methodological framework for a scoping review that included a systematic search using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. We searched the following online databases: PsycINFO, MEDLINE, Embase, Web of Science, and IEEE Xplore. We charted study data according to application (BCI, experimental research, validation, signal processing, and clinical) and location of use as indexed by the first author's country.

RESULTS: We identified 916 studies that used data recorded with consumer-grade EEG: 531 were reported in journal articles and 385 in conference papers. Emotiv devices were used most, followed by the NeuroSky MindWave, OpenBCI, interaXon Muse, and MyndPlay Mindband. The most common usage was for brain-computer interfaces, followed by experimental research, signal processing, validation, and clinical purposes.

CONCLUSIONS: Consumer-grade EEG is a useful tool for neuroscientific research and will likely continue to be used well into the future. Our study provides a comprehensive review of their application, as well as future directions for researchers who plan to use these devices.

RevDate: 2024-03-06

Liu Y, Ren H, Zhang Y, et al (2024)

Temporal changes in brain morphology related to inflammation and schizophrenia: an omnigenic Mendelian randomization study.

Psychological medicine pii:S003329172400014X [Epub ahead of print].

BACKGROUND: Over the past several decades, more research focuses have been made on the inflammation/immune hypothesis of schizophrenia. Building upon synaptic plasticity hypothesis, inflammation may contribute the underlying pathophysiology of schizophrenia. Yet, pinpointing the specific inflammatory agents responsible for schizophrenia remains a complex challenge, mainly due to medication and metabolic status. Multiple lines of evidence point to a wide-spread genetic association across genome underlying the phenotypic variations of schizophrenia.

METHOD: We collected the latest genome-wide association analysis (GWAS) summary data of schizophrenia, cytokines, and longitudinal change of brain. We utilized the omnigenic model which takes into account all genomic SNPs included in the GWAS of trait, instead of traditional Mendelian randomization (MR) methods. We conducted two round MR to investigate the inflammatory triggers of schizophrenia and the resulting longitudinal changes in the brain.

RESULTS: We identified seven inflammation markers linked to schizophrenia onset, which all passed the Bonferroni correction for multiple comparisons (bNGF, GROA(CXCL1), IL-8, M-CSF, MCP-3 (CCL7), TNF-β, CRP). Moreover, CRP were found to significantly influence the linear rate of brain morphology changes, predominantly in the white matter of the cerebrum and cerebellum.

CONCLUSION: With an omnigenic approach, our study sheds light on the immune pathology of schizophrenia. Although these findings need confirmation from future studies employing different methodologies, our work provides substantial evidence that pervasive, low-level neuroinflammation may play a pivotal role in schizophrenia, potentially leading to notable longitudinal changes in brain morphology.

RevDate: 2024-03-07
CmpDate: 2024-03-07

Guo Z, Tian C, Shi Y, et al (2024)

Blood-based CNS regionally and neuronally enriched extracellular vesicles carrying pTau217 for Alzheimer's disease diagnosis and differential diagnosis.

Acta neuropathologica communications, 12(1):38.

Accurate differential diagnosis among various dementias is crucial for effective treatment of Alzheimer's disease (AD). The study began with searching for novel blood-based neuronal extracellular vesicles (EVs) that are more enriched in the brain regions vulnerable to AD development and progression. With extensive proteomic profiling, GABRD and GPR162 were identified as novel brain regionally enriched plasma EVs markers. The performance of GABRD and GPR162, along with the AD molecule pTau217, was tested using the self-developed and optimized nanoflow cytometry-based technology, which not only detected the positive ratio of EVs but also concurrently presented the corresponding particle size of the EVs, in discovery (n = 310) and validation (n = 213) cohorts. Plasma GABRD[+]- or GPR162[+]-carrying pTau217-EVs were significantly reduced in AD compared with healthy control (HC). Additionally, the size distribution of GABRD[+]- and GPR162[+]-carrying pTau217-EVs were significantly different between AD and non-AD dementia (NAD). An integrative model, combining age, the number and corresponding size of the distribution of GABRD[+]- or GPR162[+]-carrying pTau217-EVs, accurately and sensitively discriminated AD from HC [discovery cohort, area under the curve (AUC) = 0.96; validation cohort, AUC = 0.93] and effectively differentiated AD from NAD (discovery cohort, AUC = 0.91; validation cohort, AUC = 0.90). This study showed that brain regionally enriched neuronal EVs carrying pTau217 in plasma may serve as a robust diagnostic and differential diagnostic tool in both clinical practice and trials for AD.

RevDate: 2024-03-05

Lai E, Mai X, Ji M, et al (2024)

High-Frequency Discrete-Interval Binary Sequence in Asynchronous c-VEP-based BCI for Visual Fatigue Reduction.

IEEE journal of biomedical and health informatics, PP: [Epub ahead of print].

In code-modulated visual evoked potential (c-VEP) based BCI systems, flickering visual stimuli may result in visual fatigue. Thus, we introduced a discrete-interval binary sequence (DIBS) as visual stimulus modulation, with its power spectrum optimized to emphasize high-frequency components (40 Hz-60 Hz). 8 and 17 subjects participated, respectively, in offline and online experiments on a 4-target asynchronous c-VEP-based BCI system designed to realize a high positive predictive value (PPV), a low false positive rate (FPR) during idle states, and a high true positive rate (TPR) in control states, while minimizing visual fatigue level. Two visual stimuli modulations were introduced and compared: a maximum length sequence (m-sequence) and the high-frequency discrete-interval binary sequence (DIBS). The decoding algorithm was compared among the canonical correlation analysis (CCA), the task-related component analysis (TRCA), and two approaches of sub-band component weight calculation (the traditional method and the proportional method) for FBCCA and FBTRCA. In the online experiments, the average PPV, FPR and TPR achieved, respectively 99.70 %, 6.13 ×10[-2] min[-1], 20.53 min[-1] with m-sequence, while 99.2 %, 7.35 ×10[-2] min[-1] and 16.63 min[-1]with DIBS. Estimated by objective eye-related metrics and a subjective questionnaire, the visual fatigue in DIBS cases is significantly smaller than that in m-sequence cases. In this study, the feasibility of a novel modulation approach for visual fatigue reduction was proved in an asynchronous c-VEP system, while maintaining comparable performance to existing methods, which provides further insights towards enhancing this field's long-term viability and user-friendliness.

RevDate: 2024-03-05

Patel P, Balasubramanian S, RN Annavarapu (2024)

Cross subject emotion identification from multichannel EEG sub-bands using Tsallis entropy feature and KNN classifier.

Brain informatics, 11(1):7.

Human emotion recognition remains a challenging and prominent issue, situated at the convergence of diverse fields, such as brain-computer interfaces, neuroscience, and psychology. This study utilizes an EEG data set for investigating human emotion, presenting novel findings and a refined approach for EEG-based emotion detection. Tsallis entropy features, computed for q values of 2, 3, and 4, are extracted from signal bands, including theta-θ (4-7 Hz), alpha-α (8-15 Hz), beta-β (16-31 Hz), gamma-γ (32-55 Hz), and the overall frequency range (0-75 Hz). These Tsallis entropy features are employed to train and test a KNN classifier, aiming for accurate identification of two emotional states: positive and negative. In this study, the best average accuracy of 79% and an F-score of 0.81 were achieved in the gamma frequency range for the Tsallis parameter q = 3. In addition, the highest accuracy and F-score of 84% and 0.87 were observed. Notably, superior performance was noted in the anterior and left hemispheres compared to the posterior and right hemispheres in the context of emotion studies. The findings show that the proposed method exhibits enhanced performance, making it a highly competitive alternative to existing techniques. Furthermore, we identify and discuss the shortcomings of the proposed approach, offering valuable insights into potential avenues for improvements.

RevDate: 2024-03-06

Fischer-Janzen A, Wendt TM, K Van Laerhoven (2024)

A scoping review of gaze and eye tracking-based control methods for assistive robotic arms.

Frontiers in robotics and AI, 11:1326670.

Background: Assistive Robotic Arms are designed to assist physically disabled people with daily activities. Existing joysticks and head controls are not applicable for severely disabled people such as people with Locked-in Syndrome. Therefore, eye tracking control is part of ongoing research. The related literature spans many disciplines, creating a heterogeneous field that makes it difficult to gain an overview. Objectives: This work focuses on ARAs that are controlled by gaze and eye movements. By answering the research questions, this paper provides details on the design of the systems, a comparison of input modalities, methods for measuring the performance of these controls, and an outlook on research areas that gained interest in recent years. Methods: This review was conducted as outlined in the PRISMA 2020 Statement. After identifying a wide range of approaches in use the authors decided to use the PRISMA-ScR extension for a scoping review to present the results. The identification process was carried out by screening three databases. After the screening process, a snowball search was conducted. Results: 39 articles and 6 reviews were included in this article. Characteristics related to the system and study design were extracted and presented divided into three groups based on the use of eye tracking. Conclusion: This paper aims to provide an overview for researchers new to the field by offering insight into eye tracking based robot controllers. We have identified open questions that need to be answered in order to provide people with severe motor function loss with systems that are highly useable and accessible.

RevDate: 2024-03-06

Wang Z, Ding J, Tan J, et al (2024)

UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF.

Frontiers in plant science, 15:1358965.

Secondary salinization is a crucial constraint on agricultural progress in arid regions. The specific mulching irrigation technique not only exacerbates secondary salinization but also complicates field-scale soil salinity monitoring. UAV hyperspectral remote sensing offers a monitoring method that is high-precision, high-efficiency, and short-cycle. In this study, UAV hyperspectral images were used to derive one-dimensional, textural, and three-dimensional feature variables using Competitive adaptive reweighted sampling (CARS), Gray-Level Co-occurrence Matrix (GLCM), Boruta Feature Selection (Boruta), and Brightness-Color-Index (BCI) with Fractional-order differentiation (FOD) processing. Additionally, three modeling strategies were developed (Strategy 1 involves constructing the model solely with the 20 single-band variable inputs screened by the CARS algorithm. In Strategy 2, 25 texture features augment Strategy 1, resulting in 45 feature variables for model construction. Strategy 3, building upon Strategy 2, incorporates six triple-band indices, totaling 51 variables used in the model's construction) and integrated with the Seagull Optimization Algorithm for Random Forest (SOA-RF) models to predict soil electrical conductivity (EC) and delineate spatial distribution. The results demonstrated that fractional order differentiation highlights spectral features in noisy spectra, and different orders of differentiation reveal different hidden information. The correlation between soil EC and spectra varies with the order. 1.9th order differentiation is proved to be the best order for constructing one-dimensional indices; although the addition of texture features slightly improves the accuracy of the model, the integration of the three-waveband indices significantly improves the accuracy of the estimation, with an R[2] of 0.9476. In contrast to the conventional RF model, the SOA-RF algorithm optimizes its parameters thereby significantly improving the accuracy and model stability. The optimal soil salinity prediction model proposed in this study can accurately, non-invasively and rapidly identify excessive salt accumulation in drip irrigation under membrane. It is of great significance to improve the growing conditions of cotton, increase the cotton yield, and promote the sustainable development of Xinjiang's agricultural economy, and also provides a reference for the prevention and control of regional soil salinization.

RevDate: 2024-03-05

Zheng M, Lou F, Huang Y, et al (2024)

MR-based electrical property tomography using a physics-informed network at 3 and 7 T.

NMR in biomedicine [Epub ahead of print].

Magnetic resonance electrical propert tomography promises to retrieve electrical properties (EPs) quantitatively and non-invasively in vivo, providing valuable information for tissue characterization and pathology diagnosis. However, its clinical implementation has been hindered by, for example, B1 measurement accuracy, reconstruction artifacts resulting from inaccuracies in underlying models, and stringent hardware/software requirements. To address these challenges, we present a novel approach aimed at accurate and high-resolution EPs reconstruction based on water content maps by using a physics-informed network (PIN-wEPT). The proposed method utilizes standard clinical protocols and conventional multi-channel receive arrays that have been routinely equipped in clinical settings, thus eliminating the need for specialized RF sequence/coil configurations. Compared with the original wEPT method, the network generates accurate water content maps that effectively eliminate the influence of B → 1 + $$ {\overrightarrow{B}}_1^{+} $$ and B → 1 - $$ {\overrightarrow{B}}_1^{-} $$ by incorporating data mismatch with electrodynamic constraints derived from the Helmholtz equation. Subsequent regression analysis develops a broad relationship between water content and EPs across various types of brain tissue. A series of numerical simulations was conducted at 7 T to assess the feasibility and performance of the method, which encompassed four normal head models and models with tumorous tissues incorporated, and the results showed normalized mean square error below 1.0% in water content, below 11.7% in conductivity, and below 1.1% in permittivity reconstructions for normal brain tissues. Moreover, in vivo validations conducted over five healthy subjects at both 3 and 7 T showed reasonably good consistency with empirical EPs values across the white matter, gray matter, and cerebrospinal fluid. The PIN-wEPT method, with its demonstrated efficacy, flexibility, and compatibility with current MRI scanners, holds promising potential for future clinical application.

RevDate: 2024-03-07
CmpDate: 2024-03-06

Papaioannou D, Sprange K, Hamer-Kiwacz S, et al (2024)

Recording harms in randomised controlled trials of behaviour change interventions: a qualitative study of UK clinical trials units and NIHR trial investigators.

Trials, 25(1):163.

BACKGROUND: Harms, also known as adverse events (AEs), are recorded and monitored in randomised controlled trials (RCTs) to ensure participants' safety. Harms are recorded poorly or inconsistently in RCTs of Behaviour Change Interventions (BCI); however, limited guidance exists on how to record harms in BCI trials. This qualitative study explored experiences and perspectives from multi-disciplinary trial experts on recording harms in BCI trials.

METHODS: Data were collected through fifteen in-depth semi-structured qualitative interviews and three focus groups with thirty-two participants who work in the delivery and oversight of clinical trials. Participants included multi-disciplinary staff from eight CTUs, Chief investigators, and patient and public representatives. Interviews and focus group recordings were transcribed verbatim and thematic analysis was used to analyse the transcripts.

RESULTS: Five themes were identified, namely perception and understanding of harm, proportionate reporting and plausibility, the need for a multi-disciplinary approach, language of BCI harms and complex harms for complex interventions. Participants strongly believed harms should be recorded in BCI trials; however, making decisions on "how and what to record as harms" was difficult. Recording irrelevant harms placed a high burden on trial staff and participants, drained trial resources and was perceived as for little purpose. Participants believed proportionate recording was required that focused on events with a strong plausible link to the intervention. Multi-disciplinary trial team input was essential for identifying and collecting harms; however, this was difficult in practice due to lack of knowledge on harms from BCIs, lack of input or difference in opinion. The medical language of harms was recognised as a poor fit for BCI trial harms but was familiar and established within internal processes. Future guidance on this topic would be welcomed and could include summarised literature.

CONCLUSIONS: Recording harms or adverse events in behaviour change intervention trials is complex and challenging; multi-disciplinary experts in trial design and implementation welcome forthcoming guidance on this topic. Issues include the high burden of recording irrelevant harms and use of definitions originally designed for drug trials. Proportionate recording of harms focused on events with a strong plausible link to the intervention and multi-disciplinary team input into decision making are essential.

RevDate: 2024-03-04

Sanft TB, Wong J, O'Neal B, et al (2024)

Impact of the Breast Cancer Index for Extended Endocrine Decision-Making: First Results of the Prospective BCI Registry Study.

Journal of the National Comprehensive Cancer Network : JNCCN [Epub ahead of print].

BACKGROUND: The Breast Cancer Index (BCI) test assay provides an individualized risk of late distant recurrence (5-10 years) and predicts the likelihood of benefitting from extended endocrine therapy (EET) in hormone receptor-positive early-stage breast cancer. This analysis aimed to assess the impact of BCI on EET decision-making in current clinical practice.

METHODS: The BCI Registry study evaluates long-term outcomes, decision impact, and medication adherence in patients receiving BCI testing as part of routine clinical care. Physicians and patients completed pre-BCI and post-BCI test questionnaires to assess a range of questions, including physician decision-making and confidence regarding EET; patient preferences and concerns about the cost, side effects, drug safety, and benefit of EET; and patient satisfaction regarding treatment recommendations. Pre-BCI and post-BCI test responses were compared using McNemar's test and Wilcoxon signed rank test.

RESULTS: Pre-BCI and post-BCI questionnaires were completed for 843 physicians and 823 patients. The mean age at enrollment was 65 years, and 88.4% of patients were postmenopausal. Of the tumors, 74.7% were T1, 53.4% were grade 2, 76.0% were N0, and 13.8% were HER2-positive. Following BCI testing, physicians changed EET recommendations in 40.1% of patients (P<.0001), and 45.1% of patients changed their preferences for EET (P<.0001). In addition, 38.8% of physicians felt more confident in their recommendation (P<.0001), and 41.4% of patients felt more comfortable with their EET decision (P<.0001). Compared with baseline, significantly more patients were less concerned about the cost (20.9%; P<.0001), drug safety (25.4%; P=.0014), and benefit of EET (29.3%; P=.0002).

CONCLUSIONS: This analysis in a large patient cohort of the BCI Registry confirms and extends previous findings on the significant decision-making impact of BCI on EET. Incorporating BCI into clinical practice resulted in changes in physician recommendations, increased physician confidence, improved patient satisfaction, and reduced patient concerns regarding the cost, drug safety, and benefit of EET.

RevDate: 2024-03-07

Gu M, Pei W, Gao X, et al (2024)

Optimizing Visual Stimulation Paradigms for User-Friendly SSVEP-Based BCIs.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, PP: [Epub ahead of print].

In steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) systems, traditional flickering stimulation patterns face challenges in achieving a trade-off in both BCI performance and visual comfort across various frequency bands. To investigate the optimal stimulation paradigms with high performance and high comfort for each frequency band, this study systematically compared the characteristics of SSVEP and user experience of different stimulation paradigms with a wide stimulation frequency range of 1-60 Hz. The findings suggest that, for a better balance between system performance and user experience, ON and OFF grid stimuli with a Weber contrast of 50% can be utilized as alternatives to traditional flickering stimulation paradigms in the frequency band of 1-25 Hz. In the 25-35 Hz range, uniform flicker stimuli with the same 50% contrast are more suitable. In the higher frequency band, traditional uniform flicker stimuli with a high 300% contrast are preferred. These results are significant for developing high performance and user-friendly SSVEP-based BCI systems.

RevDate: 2024-03-05

Shi C, He Y, Gourdouparis M, et al (2024)

A Spatially Diverse 2TX-3RX Galvanic-Coupled Transdural Telemetry for Tether-Less Distributed Brain-Computer Interfaces.

IEEE transactions on biomedical circuits and systems, PP: [Epub ahead of print].

A near-field galvanic coupled transdural telemetry ASICs for intracortical brain-computer interfaces is presented. The proposed design features a two channels transmitter and three channels receiver (2TX-3RX) topology, which introduces spatial diversity to effectively mitigate misalignments (both lateral and rotational) between the brain and the skull and recovers the path loss by 13 dB when the RX is in the worst-case blind spot. This spatial diversity also allows the presented telemetry to support the spatial division multiplexing required for a high-capacity multi-implant distributed network. It achieves a signal-to-interference ratio of 12 dB, even with the adjacent interference node placed only 8 mm away from the desired link. While consuming only 0.33 mW for each channel, the presented RX achieves a wide bandwidth of 360 MHz and a low input referred noise of 13.21 nV/√Hz. The presented telemetry achieves a 270 Mbps data rate with a BER<10[-6] and an energy efficiency of 3.4 pJ/b and 3.7 pJ/b, respectively. The core footprint of the TX and RX modules is only 100 and 52 mm2, respectively, minimizing the invasiveness of the surgery. The proposed transdural telemetry system has been characterized ex-vivo with a 7-mm thick porcine tissue.

RevDate: 2024-03-05

Khan H, Khadka R, Sultan MS, et al (2024)

Unleashing the potential of fNIRS with machine learning: classification of fine anatomical movements to empower future brain-computer interface.

Frontiers in human neuroscience, 18:1354143.

In this study, we explore the potential of using functional near-infrared spectroscopy (fNIRS) signals in conjunction with modern machine-learning techniques to classify specific anatomical movements to increase the number of control commands for a possible fNIRS-based brain-computer interface (BCI) applications. The study focuses on novel individual finger-tapping, a well-known task in fNIRS and fMRI studies, but limited to left/right or few fingers. Twenty-four right-handed participants performed the individual finger-tapping task. Data were recorded by using sixteen sources and detectors placed over the motor cortex according to the 10-10 international system. The event's average oxygenated Δ HbO and deoxygenated Δ HbR hemoglobin data were utilized as features to assess the performance of diverse machine learning (ML) models in a challenging multi-class classification setting. These methods include LDA, QDA, MNLR, XGBoost, and RF. A new DL-based model named "Hemo-Net" has been proposed which consists of multiple parallel convolution layers with different filters to extract the features. This paper aims to explore the efficacy of using fNRIS along with ML/DL methods in a multi-class classification task. Complex models like RF, XGBoost, and Hemo-Net produce relatively higher test set accuracy when compared to LDA, MNLR, and QDA. Hemo-Net has depicted a superior performance achieving the highest test set accuracy of 76%, however, in this work, we do not aim at improving the accuracies of models rather we are interested in exploring if fNIRS has the neural signatures to help modern ML/DL methods in multi-class classification which can lead to applications like brain-computer interfaces. Multi-class classification of fine anatomical movements, such as individual finger movements, is difficult to classify with fNIRS data. Traditional ML models like MNLR and LDA show inferior performance compared to the ensemble-based methods of RF and XGBoost. DL-based method Hemo-Net outperforms all methods evaluated in this study and demonstrates a promising future for fNIRS-based BCI applications.

RevDate: 2024-03-05

Dong K, Liu WC, Su Y, et al (2023)

Scalable Electrophysiology of Millimeter-Scale Animals with Electrode Devices.

BME frontiers, 4:0034.

Millimeter-scale animals such as Caenorhabditis elegans, Drosophila larvae, zebrafish, and bees serve as powerful model organisms in the fields of neurobiology and neuroethology. Various methods exist for recording large-scale electrophysiological signals from these animals. Existing approaches often lack, however, real-time, uninterrupted investigations due to their rigid constructs, geometric constraints, and mechanical mismatch in integration with soft organisms. The recent research establishes the foundations for 3-dimensional flexible bioelectronic interfaces that incorporate microfabricated components and nanoelectronic function with adjustable mechanical properties and multidimensional variability, offering unique capabilities for chronic, stable interrogation and stimulation of millimeter-scale animals and miniature tissue constructs. This review summarizes the most advanced technologies for electrophysiological studies, based on methods of 3-dimensional flexible bioelectronics. A concluding section addresses the challenges of these devices in achieving freestanding, robust, and multifunctional biointerfaces.

RevDate: 2024-03-05

Herbert C (2023)

Analyzing and computing humans by means of the brain using Brain-Computer Interfaces - understanding the user - previous evidence, self-relevance and the user's self-concept as potential superordinate human factors of relevance.

Frontiers in human neuroscience, 17:1286895.

Brain-computer interfaces (BCIs) are well-known instances of how technology can convert a user's brain activity taken from non-invasive electroencephalography (EEG) into computer commands for the purpose of computer-assisted communication and interaction. However, not all users are attaining the accuracy required to use a BCI consistently, despite advancements in technology. Accordingly, previous research suggests that human factors could be responsible for the variance in BCI performance among users. Therefore, the user's internal mental states and traits including motivation, affect or cognition, personality traits, or the user's satisfaction, beliefs or trust in the technology have been investigated. Going a step further, this manuscript aims to discuss which human factors could be potential superordinate factors that influence BCI performance, implicitly, explicitly as well as inter- and intraindividually. Based on the results of previous studies that used comparable protocols to examine the motivational, affective, cognitive state or personality traits of healthy and vulnerable EEG-BCI users within and across well-investigated BCIs (P300-BCIs or SMR-BCIs, respectively), it is proposed that the self-relevance of tasks and stimuli and the user's self-concept provide a huge potential for BCI applications. As potential key human factors self-relevance and the user's self-concept (self-referential knowledge and beliefs about one's self) guide information processing and modulate the user's motivation, attention, or feelings of ownership, agency, and autonomy. Changes in the self-relevance of tasks and stimuli as well as self-referential processing related to one's self (self-concept) trigger changes in neurophysiological activity in specific brain networks relevant to BCI. Accordingly, concrete examples will be provided to discuss how past and future research could incorporate self-relevance and the user's self-concept in the BCI setting - including paradigms, user instructions, and training sessions.

RevDate: 2024-03-06

Thota AK, R Jung (2024)

Accelerating neurotechnology development using an Agile methodology.

Frontiers in neuroscience, 18:1328540.

Novel bioelectronic medical devices that target neural control of visceral organs (e.g., liver, gut, spleen) or inflammatory reflex pathways are innovative class III medical devices like implantable cardiac pacemakers that are lifesaving and life-sustaining medical devices. Bringing innovative neurotechnologies early into the market and the hands of treatment providers would benefit a large population of patients inflicted with autonomic and chronic immune disorders. Medical device manufacturers and software developers widely use the Waterfall methodology to implement design controls through verification and validation. In the Waterfall methodology, after identifying user needs, a functional unit is fabricated following the verification loop (design, build, and verify) and then validated against user needs. Considerable time can lapse in building, verifying, and validating the product because this methodology has limitations for adjusting to unanticipated changes. The time lost in device development can cause significant delays in final production, increase costs, and may even result in the abandonment of the device development. Software developers have successfully implemented an Agile methodology that overcomes these limitations in developing medical software. However, Agile methodology is not routinely used to develop medical devices with implantable hardware because of the increased regulatory burden of the need to conduct animal and human studies. Here, we provide the pros and cons of the Waterfall methodology and make a case for adopting the Agile methodology in developing medical devices with physical components. We utilize a peripheral nerve interface as an example device to illustrate the use of the Agile approach to develop neurotechnologies.

RevDate: 2024-03-06
CmpDate: 2024-03-05

Ma X, Qi Y, Xu C, et al (2024)

How well do neural signatures of resting-state EEG detect consciousness? A large-scale clinical study.

Human brain mapping, 45(4):e26586.

The assessment of consciousness states, especially distinguishing minimally conscious states (MCS) from unresponsive wakefulness states (UWS), constitutes a pivotal role in clinical therapies. Despite that numerous neural signatures of consciousness have been proposed, the effectiveness and reliability of such signatures for clinical consciousness assessment still remains an intense debate. Through a comprehensive review of the literature, inconsistent findings are observed about the effectiveness of diverse neural signatures. Notably, the majority of existing studies have evaluated neural signatures on a limited number of subjects (usually below 30), which may result in uncertain conclusions due to small data bias. This study presents a systematic evaluation of neural signatures with large-scale clinical resting-state electroencephalography (EEG) signals containing 99 UWS, 129 MCS, 36 emergence from the minimally conscious state, and 32 healthy subjects (296 total) collected over 3 years. A total of 380 EEG-based metrics for consciousness detection, including spectrum features, nonlinear measures, functional connectivity, and graph-based measures, are summarized and evaluated. To further mitigate the effect of data bias, the evaluation is performed with bootstrap sampling so that reliable measures can be obtained. The results of this study suggest that relative power in alpha and delta serve as dependable indicators of consciousness. With the MCS group, there is a notable increase in the phase lag index-related connectivity measures and enhanced functional connectivity between brain regions in comparison to the UWS group. A combination of features enables the development of an automatic detector of conscious states.

RevDate: 2024-03-07
CmpDate: 2024-03-07

Drew L (2024)

Neuralink brain chip: advance sparks safety and secrecy concerns.

Nature, 627(8002):19.

RevDate: 2024-03-02

Bekhelifi O, Berrached NE, A Bendahmane (2024)

Effects of the presentation order of stimulations in sequential ERP/SSVEP Hybrid Brain-Computer Interface.

Biomedical physics & engineering express [Epub ahead of print].

Hybrid Brain-Computer Interface (hBCI) combines multiple neurophysiology modalities or paradigms to speed up the output of a single command or produce multiple ones simultaneously. Concurrent hBCIs that employ endogenous and exogenous paradigms are limited by the reduced set of possible commands. Conversely, the fusion of different exogenous visual evoked potentials demonstrated impressive performances; however, they suffer from limited portability. Yet, sequential hBCIs did not receive much attention mainly due to slower transfer rate and user fatigue high potential during prolonged BCI use (Lorenz, Pascual, Blankertz & Vidaurre 2014). Moreover, the crucial factors for optimizing the hybridization remain under-explored. In this paper, we test the feasibility of sequential Event Related-Potentials (ERP) and Steady-State Visual Evoked Potentials (SSVEP) hBCI and study the effect of stimulus order presentation between ERP-SSVEP and SSVEP-ERP for the control of directions and speed of powered wheelchairs or mobile robots with 15 commands. Exploiting the fast single trial face stimulus ERP, SSVEP and modern efficient convolutional neural networks, the configuration with SSVEP presented at first achieved significantly (p < 0.05) higher average accuracy rate with 76.39% (± 7.30 standard deviation) hybrid command accuracy and an average Information Transfer Rate (ITR) of 25.05 (± 5.32 standard deviation) bits per minute (bpm). The results of the study demonstrate the suitability of a sequential SSVEP-ERP hBCI with challenging dry electroencephalography (EEG) electrodes and low-compute capacity. Although it presents lower ITR than concurrent hBCIs, our system presents an alternative in small screen settings when the conditions for concurrent hBCIs are hardly satisfied.

RevDate: 2024-03-04
CmpDate: 2024-03-04

Du X, Liang K, Lv Y, et al (2024)

Fast reconstruction of EEG signal compression sensing based on deep learning.

Scientific reports, 14(1):5087.

When traditional EEG signals are collected based on the Nyquist theorem, long-time recordings of EEG signals will produce a large amount of data. At the same time, limited bandwidth, end-to-end delay, and memory space will bring great pressure on the effective transmission of data. The birth of compressed sensing alleviates this transmission pressure. However, using an iterative compressed sensing reconstruction algorithm for EEG signal reconstruction faces complex calculation problems and slow data processing speed, limiting the application of compressed sensing in EEG signal rapid monitoring systems. As such, this paper presents a non-iterative and fast algorithm for reconstructing EEG signals using compressed sensing and deep learning techniques. This algorithm uses the improved residual network model, extracts the feature information of the EEG signal by one-dimensional dilated convolution, directly learns the nonlinear mapping relationship between the measured value and the original signal, and can quickly and accurately reconstruct the EEG signal. The method proposed in this paper has been verified by simulation on the open BCI contest dataset. Overall, it is proved that the proposed method has higher reconstruction accuracy and faster reconstruction speed than the traditional CS reconstruction algorithm and the existing deep learning reconstruction algorithm. In addition, it can realize the rapid reconstruction of EEG signals.

RevDate: 2024-03-01

Wang M, Deng Y, Liu Y, et al (2024)

The common and distinct brain basis associated with adult and adolescent risk-taking behavior: Evidence from the neuroimaging meta-analysis.

Neuroscience and biobehavioral reviews pii:S0149-7634(24)00076-9 [Epub ahead of print].

Risk-taking is a common, complex, and multidimensional behavior construct that has significant implications for human health and well-being. Previous research has identified the neural mechanisms underlying risk-taking behavior in both adolescents and adults, yet the differences between adolescents' and adults' risk-taking in the brain remain elusive. This study firstly employs a comprehensive meta-analysis approach that includes 73 adult and 20 adolescent whole-brain experiments, incorporating observations from 1986 adults and 789 adolescents obtained from online databases, including Web of Science, PubMed, ScienceDirect, Google Scholar, EBSCO PsycINFO, Scopus, Medline and PsycARTICLES. It then combines functional decoding methods to identify common and distinct brain regions and corresponding psychological processes associated with risk-taking behavior in these two cohorts. The results indicated that the neural bases underlying risk-taking behavior in both age groups are situated within the cognitive control, reward, and sensory networks. Subsequent contrast analysis revealed that adolescents and adults risk-taking engaged frontal pole within the fronto-parietal control network (FPN), but the former recruited more ventrolateral area and the latter recruited more dorsolateral area. Moreover, adolescents' risk-taking evoked brain area activity within the ventral attention network (VAN) and the default mode network (DMN) compared with adults, consistent with the functional decoding analyses. These findings provide new insights into the similarities and disparities of risk-taking neural substrates underlying different age cohorts, supporting future neuroimaging research on the dynamic changes of risk-taking.

RevDate: 2024-03-01

Wang WW, Ji SY, Zhang W, et al (2024)

Structure-based design of non-hypertrophic apelin receptor modulator.

Cell pii:S0092-8674(24)00125-9 [Epub ahead of print].

Apelin is a key hormone in cardiovascular homeostasis that activates the apelin receptor (APLNR), which is regarded as a promising therapeutic target for cardiovascular disease. However, adverse effects through the β-arrestin pathway limit its pharmacological use. Here, we report cryoelectron microscopy (cryo-EM) structures of APLNR-Gi1 complexes bound to three agonists with divergent signaling profiles. Combined with functional assays, we have identified "twin hotspots" in APLNR as key determinants for signaling bias, guiding the rational design of two exclusive G-protein-biased agonists WN353 and WN561. Cryo-EM structures of WN353- and WN561-stimulated APLNR-G protein complexes further confirm that the designed ligands adopt the desired poses. Pathophysiological experiments have provided evidence that WN561 demonstrates superior therapeutic effects against cardiac hypertrophy and reduced adverse effects compared with the established APLNR agonists. In summary, our designed APLNR modulator may facilitate the development of next-generation cardiovascular medications.

RevDate: 2024-03-02

Kumar S, Alawieh H, Racz FS, et al (2024)

Transfer learning promotes acquisition of individual BCI skills.

PNAS nexus, 3(2):pgae076.

Subject training is crucial for acquiring brain-computer interface (BCI) control. Typically, this requires collecting user-specific calibration data due to high inter-subject neural variability that limits the usability of generic decoders. However, calibration is cumbersome and may produce inadequate data for building decoders, especially with naïve subjects. Here, we show that a decoder trained on the data of a single expert is readily transferrable to inexperienced users via domain adaptation techniques allowing calibration-free BCI training. We introduce two real-time frameworks, (i) Generic Recentering (GR) through unsupervised adaptation and (ii) Personally Assisted Recentering (PAR) that extends GR by employing supervised recalibration of the decoder parameters. We evaluated our frameworks on 18 healthy naïve subjects over five online sessions, who operated a customary synchronous bar task with continuous feedback and a more challenging car racing game with asynchronous control and discrete feedback. We show that along with improved task-oriented BCI performance in both tasks, our frameworks promoted subjects' ability to acquire individual BCI skills, as the initial neurophysiological control features of an expert subject evolved and became subject specific. Furthermore, those features were task-specific and were learned in parallel as participants practiced the two tasks in every session. Contrary to previous findings implying that supervised methods lead to improved online BCI control, we observed that longitudinal training coupled with unsupervised domain matching (GR) achieved similar performance to supervised recalibration (PAR). Therefore, our presented frameworks facilitate calibration-free BCIs and have immediate implications for broader populations-such as patients with neurological pathologies-who might struggle to provide suitable initial calibration data.

RevDate: 2024-03-04
CmpDate: 2024-03-04

Shi C, Zhang C, Chen JF, et al (2024)

Enhancement of low gamma oscillations by volitional conditioning of local field potential in the primary motor and visual cortex of mice.

Cerebral cortex (New York, N.Y. : 1991), 34(2):.

Volitional control of local field potential oscillations in low gamma band via brain machine interface can not only uncover the relationship between low gamma oscillation and neural synchrony but also suggest a therapeutic potential to reverse abnormal local field potential oscillation in neurocognitive disorders. In nonhuman primates, the volitional control of low gamma oscillations has been demonstrated by brain machine interface techniques in the primary motor and visual cortex. However, it is not clear whether this holds in other brain regions and other species, for which gamma rhythms might involve in highly different neural processes. Here, we established a closed-loop brain-machine interface and succeeded in training mice to volitionally elevate low gamma power of local field potential in the primary motor and visual cortex. We found that the mice accomplished the task in a goal-directed manner and spiking activity exhibited phase-locking to the oscillation in local field potential in both areas. Moreover, long-term training made the power enhancement specific to direct and adjacent channel, and increased the transcriptional levels of NMDA receptors as well as that of hypoxia-inducible factor relevant to metabolism. Our results suggest that volitionally generated low gamma rhythms in different brain regions share similar mechanisms and pave the way for employing brain machine interface in therapy of neurocognitive disorders.

RevDate: 2024-02-29

Wang H, Zhu Z, Bi H, et al (2024)

Changes in Community Structure of Brain Dynamic Functional Connectivity States in Mild Cognitive Impairment.

Neuroscience pii:S0306-4522(24)00089-7 [Epub ahead of print].

Recent researches have noted many changes of short-term dynamic modalities in mild cognitive impairment (MCI) patients' brain functional networks. In this study, the dynamic functional brain networks of 82 MCI patients and 85 individuals in the normal control (NC) group were constructed using the sliding window method and Pearson correlation. The window size was determined using single-scale time-dependent (SSTD) method. Subsequently, k-means was applied to cluster all window samples, identifying three dynamic functional connectivity (DFC) states. Collective sparse symmetric non-negative matrix factorization (cssNMF) was then used to perform community detection on these states and quantify differences in brain regions. Finally, metrics such as within-community connectivity strength, community strength, and node diversity were calculated for further analysis. The results indicated high similarity between the two groups in state 2, with no significant differences in optimal community quantity and functional segregation (p<0.05). However, for state 1 and state 3, the optimal community quantity was smaller in MCI patients compared to the NC group. In state 1, MCI patients had lower within-community connectivity strength and overall strength than the NC group, whereas state 3 showed results opposite to state 1. Brain regions with statistical difference included MFG.L, ORBinf.R, STG.R, IFGtriang.L, CUN.L, CUN.R, LING.R, SOG.L, and PCUN.R. This study on DFC states explores changes in the brain functional networks of patients with MCI from the perspective of alterations in the community structures of DFC states. The findings could provide new insights into the pathological changes in the brains of MCI patients.

RevDate: 2024-02-28

Yang J, Zhao S, Fu Z, et al (2024)

PMF-CNN: Parallel multi-band fusion convolutional neural network for SSVEP-EEG decoding.

Biomedical physics & engineering express [Epub ahead of print].

Steady-state visual evoked potential (SSVEP) is a key technique of electroencephalography (EEG)-based brain-computer interfaces (BCI), which has been widely applied to neurological function assessment and postoperative rehabilitation. However, accurate decoding of the user's intended based on the SSVEP-EEG signals is challenging due to the low signal-to-noise ratio and large individual variability of the signals. To address these issues, we proposed a parallel multi-band fusion convolutional neural network (PMF-CNN). Multi frequency band signals were served as the input of PMF-CNN to fully utilize the time-frequency information of EEG. Three parallel modules, spatial self-attention (SAM), temporal self-attention (TAM), and squeeze-excitation (SEM), were proposed to automatically extract multi-dimensional features from spatial, temporal, and frequency domains, respectively. A novel spatial-temporal-frequency representation were designed to capture the correlation of electrode channels, time intervals, and different sub-harmonics by using SAM, TAM, and SEM, respectively. The three parallel modules operate independently and simultaneously. A four layers CNN classification module was designed to fuse parallel multi-dimensional features and achieve the accurate classification of SSVEP-EEG signals. The PMF-CNN was further interpreted by using brain functional connectivity analysis. The proposed method was validated using two large publicly available datasets. After trained using our proposed dual-stage training pattern, the classification accuracies were 99.37% and 93.96%, respectively, which are superior to the current state-of-the-art SSVEP-EEG classification algorithms. The algorithm exhibits high classification accuracy and good robustness, which has the potential to be applied to postoperative rehabilitation.

RevDate: 2024-02-28

Blanco-Díaz CF, Guerrero Mendez CDD, Delisle-Rodriguez D, et al (2024)

Evaluation of Temporal, Spatial and Spectral Filtering in CSP-based Methods for Decoding Pedaling-Based Motor Tasks Using EEG signals.

Biomedical physics & engineering express [Epub ahead of print].

Stroke is a neurological syndrome that usually causes a loss of voluntary control of lower/upper body movements, making it difficult for affected individuals to perform Activities of Daily Living (ADLs). Brain-Computer Interfaces (BCIs) combined with robotic systems, such as Motorized Mini Exercise Bikes (MMEB), have enabled the rehabilitation of people with disabilities by decoding their actions and executing a motor task. However, Electroencephalography (EEG)-based BCIs are affected by the presence of physiological and non-physiological artifacts. Thus, movement discrimination using EEG become challenging, even in pedaling tasks, which have not been well explored in the literature. In this study, Common Spatial Patterns (CSP)-based methods were proposed to classify pedaling motor tasks. To address this, Filter Bank Common Spatial Patterns (FBCSP) and Filter Bank Common Spatial-Spectral Patterns (FBCSSP) were implemented with different spatial filtering configurations by varying the time segment with different filter bank combinations for the three methods to decode pedaling tasks. An in-house EEG dataset during pedaling tasks was registered for 8 participants. As results, the best configuration corresponds to a filter bank with two filters (8-19 Hz and 19-30 Hz) using a time window between 1.5 and 2.5 s after the cue and implementing two spatial filters, which provide accuracy of approximately 0.81, False Positive Rates lower than 0.19, and \textit{Kappa} index of 0.61. This work implies that EEG oscillatory patterns during pedaling can be accurately classified using machine learning. Therefore, our method can be applied in the rehabilitation context, such as MMEB-based BCIs, in the future.

RevDate: 2024-02-28

Subramaniam S, Akay M, Anastasio MA, et al (2024)

Grand Challenges at the Interface of Engineering and Medicine.

IEEE open journal of engineering in medicine and biology, 5:1-13.

Over the past two decades Biomedical Engineering has emerged as a major discipline that bridges societal needs of human health care with the development of novel technologies. Every medical institution is now equipped at varying degrees of sophistication with the ability to monitor human health in both non-invasive and invasive modes. The multiple scales at which human physiology can be interrogated provide a profound perspective on health and disease. We are at the nexus of creating "avatars" (herein defined as an extension of "digital twins") of human patho/physiology to serve as paradigms for interrogation and potential intervention. Motivated by the emergence of these new capabilities, the IEEE Engineering in Medicine and Biology Society, the Departments of Biomedical Engineering at Johns Hopkins University and Bioengineering at University of California at San Diego sponsored an interdisciplinary workshop to define the grand challenges that face biomedical engineering and the mechanisms to address these challenges. The Workshop identified five grand challenges with cross-cutting themes and provided a roadmap for new technologies, identified new training needs, and defined the types of interdisciplinary teams needed for addressing these challenges. The themes presented in this paper include: 1) accumedicine through creation of avatars of cells, tissues, organs and whole human; 2) development of smart and responsive devices for human function augmentation; 3) exocortical technologies to understand brain function and treat neuropathologies; 4) the development of approaches to harness the human immune system for health and wellness; and 5) new strategies to engineer genomes and cells.

RevDate: 2024-02-28

Chen S, Yao L, Cao L, et al (2024)

Editorial: Exploration of the non-invasive brain-computer interface and neurorehabilitation.

Frontiers in neuroscience, 18:1377665.

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Rajesh Rao has written the perfect introduction to the exciting world of brain-computer interfaces. The book is remarkably comprehensive — not only including full descriptions of classic and current experiments but also covering essential background concepts, from the brain to Bayes and back. Brain-Computer Interfacing will be welcomed by a wide range of intelligent readers interested in understanding the first steps toward the symbiotic merger of brains and computers. Eberhard E. Fetz, UW

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