@article {pmid38642251,
year = {2024},
author = {Youn, KI and Lee, JW and Song, Y and Lee, SY and Song, KH},
title = {Development of Cell Culture Platforms for Study of Trabecular Meshwork Cells and Glaucoma Development.},
journal = {Tissue engineering and regenerative medicine},
volume = {},
number = {},
pages = {},
pmid = {38642251},
issn = {2212-5469},
support = {Incheon National University (International Cooperative) Research Grant in 2019//Incheon National University/ ; },
abstract = {BACKGROUND: Various cell culture platforms that could display native environmental cue-mimicking stimuli were developed, and effects of environmental cues on cell behaviors were studied with the cell culture platforms. Likewise, various cell culture platforms mimicking native trabecular meshwork (TM) composed of juxtacanalicular, corneoscleral and uveal meshwork located in internal scleral sulcus were used to study effects of environmental cues and/or drug treatments on TM cells and glaucoma development. Glaucoma is a disease that could cause blindness, and cause of glaucoma is not clearly identified yet. It appears that aqueous humor (AH) outflow resistance increased by damages on pathway of AH outflow can elevate intraocular pressure (IOP). These overall possibly contribute to development of glaucoma.

METHODS: For the study of glaucoma, static and dynamic cell culture platforms were developed. Particularly, the dynamic platforms exploiting AH outflow-mimicking perfusion or increased IOP-mimicking increased pressure were used to study how perfusion or increased pressure could affect TM cells. Overall, potential mechanisms of glaucoma development, TM structures and compositions, TM cell culture platform types and researches on TM cells and glaucoma development with the platforms were described in this review.

RESULTS AND CONCLUSION: This will be useful to improve researches on TM cells and develop enhanced therapies targeting glaucoma.},
}

@article {pmid38641040,
year = {2024},
author = {Sun, R and Tang, MY and Yang, D and Zhang, YY and Xu, YH and Qiao, Y and Yu, B and Cao, SX and Wang, H and Huang, HQ and Zhang, H and Li, XM and Lian, H},
title = {C3aR in the medial prefrontal cortex modulates the susceptibility to LPS-induced depressive-like behaviors through glutamatergic neuronal excitability.},
journal = {Progress in neurobiology},
volume = {},
number = {},
pages = {102614},
doi = {10.1016/j.pneurobio.2024.102614},
pmid = {38641040},
issn = {1873-5118},
abstract = {Complement activation and prefrontal cortical dysfunction both contribute to the pathogenesis of major depressive disorder (MDD), but their interplay in MDD is unclear. We here studied the role of complement C3a receptor (C3aR) in the medial prefrontal cortex (mPFC) and its influence on depressive-like behaviors induced by systematic lipopolysaccharides (LPS) administration. C3aR knockout (KO) or intra-mPFC C3aR antagonism confers resilience, whereas C3aR expression in mPFC neurons makes KO mice susceptible to LPS-induced depressive-like behaviors. Importantly, the excitation and inhibition of mPFC neurons have opposing effects on depressive-like behaviors, aligning with increased and decreased excitability by C3aR deletion and activation in cortical neurons. In particular, inhibiting mPFC glutamatergic (mPFC[Glu]) neurons, the main neuronal subpopulation expresses C3aR, induces depressive-like behaviors in saline-treated WT and KO mice, but not in LPS-treated KO mice. Compared to hypoexcitable mPFC[Glu] neurons in LPS-treated WT mice, C3aR-null mPFC[Glu] neurons display hyperexcitability upon LPS treatment, and enhanced excitation of mPFC[Glu] neurons is anti-depressant, suggesting a protective role of C3aR deficiency in these circumstances. In conclusion, C3aR modulates susceptibility to LPS-induced depressive-like behaviors through mPFC[Glu] neuronal excitability. This study identifies C3aR as a pivotal intersection of complement activation, mPFC dysfunction, and depression and a promising therapeutic target for MDD.},
}

@article {pmid38640695,
year = {2024},
author = {Zhou, Y and Yang, B and Wang, C},
title = {Multiband task related components enhance rapid cognition decoding for both small and similar objects.},
journal = {Neural networks : the official journal of the International Neural Network Society},
volume = {175},
number = {},
pages = {106313},
doi = {10.1016/j.neunet.2024.106313},
pmid = {38640695},
issn = {1879-2782},
abstract = {The cortically-coupled target recognition system based on rapid serial visual presentation (RSVP) has a wide range of applications in brain computer interface (BCI) fields such as medical and military. However, in the complex natural environment backgrounds, the identification of event-related potentials (ERP) of both small and similar objects that are quickly presented is a research challenge. Therefore, we designed corresponding experimental paradigms and proposed a multi-band task related components matching (MTRCM) method to improve the rapid cognitive decoding of both small and similar objects. We compared the areas under the receiver operating characteristic curve (AUC) between MTRCM and other 9 methods under different numbers of training sample using RSVP-ERP data from 50 subjects. The results showed that MTRCM maintained an overall superiority and achieved the highest average AUC (0.6562 ± 0.0091). We also optimized the frequency band and the time parameters of the method. The verification on public data sets further showed the necessity of designing MTRCM method. The MTRCM method provides a new approach for neural decoding of both small and similar RSVP objects, which is conducive to promote the further development of RSVP-BCI.},
}

@article {pmid38639058,
year = {2024},
author = {Liu, Y and Dai, W and Liu, Y and Hu, D and Yang, B and Zhou, Z},
title = {An SSVEP-based BCI with 112 targets using frequency spatial multiplexing.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad4091},
pmid = {38639058},
issn = {1741-2552},
abstract = {OBJECTIVE: Brain-computer interface (BCI) systems with large directly accessible instruction sets are one of the difficulties in BCI research. Research to achieve high target resolution (≥ 100) has not yet entered a rapid development stage, which contradicts the application requirements. Steady-state visual evoked potential (SSVEP) based BCIs have an advantage in terms of the number of targets, but the competitive mechanism between the target stimulus and its neighboring stimuli is a key challenge that prevents the target resolution from being improved significantly.

APPROACH: In this paper, we reverse the competitive mechanism and propose a frequency spatial multiplexing method to produce more targets with limited frequencies. In the proposed paradigm, we replicated each flicker stimulus as a 2×2 matrix and arrange the matrices of all frequencies in a tiled fashion to form the interaction interface. With different arrangements, we designed and tested three example paradigms with different layouts. Further we designed a graph neural network that distinguishes between targets of the same frequency by recognizing the different electroencephalography (EEG) response distribution patterns evoked by each target and its neighboring targets.

MAIN RESULTS: Extensive experiment studies employing eleven subjects have been performed to verify the validity of the proposed method. The average classification accuracies in the offline validation experiments for the three paradigms are 89.16%, 91.38%, and 87.90%, with information transfer rates (ITR) of 51.66, 53.96, and 50.55 bits/min, respectively.

SIGNIFICANCE: This study utilized the positional relationship between stimuli and did not circumvent the competing response problem. Therefore, other state-of-the-art methods focusing on enhancing the efficiency of SSVEP detection can be used as a basis for the present method to achieve very promising improvements.},
}

@article {pmid38638416,
year = {2024},
author = {Proverbio, AM and Cesati, F},
title = {Neural correlates of recalled sadness, joy, and fear states: a source reconstruction EEG study.},
journal = {Frontiers in psychiatry},
volume = {15},
number = {},
pages = {1357770},
pmid = {38638416},
issn = {1664-0640},
abstract = {INTRODUCTION: The capacity to understand the others' emotional states, particularly if negative (e.g. sadness or fear), underpins the empathic and social brain. Patients who cannot express their emotional states experience social isolation and loneliness, exacerbating distress. We investigated the feasibility of detecting non-invasive scalp-recorded electrophysiological signals that correspond to recalled emotional states of sadness, fear, and joy for potential classification.

METHODS: The neural activation patterns of 20 healthy and right-handed participants were studied using an electrophysiological technique. Analyses were focused on the N400 component of Event-related potentials (ERPs) recorded during silent recall of subjective emotional states; Standardized weighted Low-resolution Electro-magnetic Tomography (swLORETA) was employed for source reconstruction. The study classified individual patterns of brain activation linked to the recollection of three distinct emotional states into seven regions of interest (ROIs).

RESULTS: Statistical analysis (ANOVA) of the individual magnitude values revealed the existence of a common emotional circuit, as well as distinct brain areas that were specifically active during recalled sad, happy and fearful states. In particular, the right temporal and left superior frontal areas were more active for sadness, the left limbic region for fear, and the right orbitofrontal cortex for happy affective states.

DISCUSSION: In conclusion, this study successfully demonstrated the feasibility of detecting scalp-recorded electrophysiological signals corresponding to internal and subjective affective states. These findings contribute to our understanding of the emotional brain, and have potential applications for future BCI classification and identification of emotional states in LIS patients who may be unable to express their emotions, thus helping to alleviate social isolation and sense of loneliness.},
}

@article {pmid38636376,
year = {2024},
author = {Yang, T and Zhang, P and Hu, J and Xu, W and Jiang, W and Feng, R and Lou, Y and Jin, X and Qian, Z and Gao, F and Gao, K and Liu, R and Yang, Y},
title = {Exploring the neural correlates of fat taste perception and discrimination: Insights from electroencephalogram analysis.},
journal = {Food chemistry},
volume = {450},
number = {},
pages = {139353},
doi = {10.1016/j.foodchem.2024.139353},
pmid = {38636376},
issn = {1873-7072},
abstract = {Understanding neural pathways and cognitive processes involved in the transformation of dietary fats into sensory experiences has profound implications for nutritional well-being. This study presents an efficient approach to comprehending the neural perception of fat taste using electroencephalogram (EEG). Through the examination of neural responses to different types of fatty acids (FAs) in 45 participants, we discerned distinct neural activation patterns associated with saturated versus unsaturated fatty acids. The spectrum analysis of averaged EEG signals revealed notable variations in δ and α-frequency bands across FA types. The topographical distribution and source localization results suggested that the brain encodes fat taste with specific activation timings in primary and secondary gustatory cortices. Saturated FAs elicited higher activation in cortical associated with emotion and reward processing. This electrophysiological evidence enhances our understanding of fundamental mechanisms behind fat perception, which is helpful for guiding strategies to manage hedonic eating and promote balanced fat consumption.},
}

@article {pmid38635379,
year = {2024},
author = {Danesh, AR and Pu, H and Safiallah, M and Do, AH and Nenadic, Z and Heydari, P},
title = {A CMOS BD-BCI: Neural Recorder with Two-Step Time-Domain Quantizer and Multi-Polar Stimulator with Dual-Mode Charge Balancing.},
journal = {IEEE transactions on biomedical circuits and systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBCAS.2024.3391190},
pmid = {38635379},
issn = {1940-9990},
abstract = {This work presents a bi-directional brain-computer interface (BD-BCI) including a high-dynamic-range (HDR) two-step time-domain neural acquisition (TTNA) system and a high-voltage (HV) multipolar neural stimulation system incorporating dual-mode time-based charge balancing (DTCB) technique. The proposed TTNA includes four independent recording modules that can sense microvolt neural signals while tolerating large stimulation artifacts. In addition, it exhibits an integrated input-referred noise of 2.3 μVrms from 0.1- to 250-Hz and can handle a linear input-signal swing of up to 340 mVPP. The multipolar stimulator is composed of four standalone stimulators each with a maximum current of up to 14 mA (±20-V of voltage compliance) and 8-bit resolution. An inter-channel interference cancellation circuitry is introduced to preserve the accuracy and effectiveness of the DTCB method in the multipolar-stimulation configuration. Fabricated in an HV 180-nm CMOS technology, the BD-BCI chipset undergoes extensive in-vitro and in-vivo evaluations. The recording system achieves a measured SNDR, SFDR, and CMRR of 84.8 dB, 89.6 dB, and >105 dB, respectively. The measurement results verify that the stimulation system is capable of performing high-precision charge balancing with ±2 mV and ±7.5 mV accuracy in the interpulse-bounded time-based charge balancing (TCB) and artifactless TCB modes, respectively.},
}

@article {pmid38635296,
year = {2024},
author = {Li, X and Wei, W and Wang, Q and Deng, W and Li, M and Ma, X and Zeng, J and Zhao, L and Guo, W and Hall, MH and Li, T},
title = {Identify Potential Causal Relationships Between Cortical Thickness, Mismatch Negativity, Neurocognition, and Psychosocial Functioning in Drug-Naïve First-Episode Psychosis Patients.},
journal = {Schizophrenia bulletin},
volume = {},
number = {},
pages = {},
doi = {10.1093/schbul/sbae026},
pmid = {38635296},
issn = {1745-1701},
support = {81920108018//National Natural Science Foundation of China/ ; 2022C03096//Key R & D Program of Zhejiang/ ; 202004A11//Project for Hangzhou Medical Disciplines of Excellence and Key Project for Hangzhou Medical Disciplines/ ; 401343//McLean Foundation Award/ ; },
abstract = {BACKGROUND: Cortical thickness (CT) alterations, mismatch negativity (MMN) reductions, and cognitive deficits are robust findings in first-episode psychosis (FEP). However, most studies focused on medicated patients, leaving gaps in our understanding of the interrelationships between CT, MMN, neurocognition, and psychosocial functioning in unmedicated FEP. This study aimed to employ multiple mediation analysis to investigate potential pathways among these variables in unmedicated drug-naïve FEP.

METHODS: We enrolled 28 drug-naïve FEP and 34 age and sex-matched healthy controls. Clinical symptoms, neurocognition, psychosocial functioning, auditory duration MMN, and T1 structural magnetic resonance imaging data were collected. We measured CT in the superior temporal gyrus (STG), a primary MMN-generating region.

RESULTS: We found a significant negative correlation between MMN amplitude and bilateral CT of STG (CT_STG) in FEP (left: r = -.709, P < .001; right: r = -.612, P = .008). Multiple mediation models revealed that a thinner left STG cortex affected functioning through both direct (24.66%) and indirect effects (75.34%). In contrast, the effects of the right CT_STG on functioning were mainly mediated through MMN and neurocognitive pathways.

CONCLUSIONS: Bilateral CT_STG showed significant association with MMN, and MMN plays a mediating role between CT and cognition. Both MMN alone and its interaction with cognition mediated the effects of structural alterations on psychosocial function. The decline in overall function in FEP may stem from decreased CT_STG, leading to subsequent MMN deficits and neurocognitive dysfunction. These findings underline the crucial role of MMN in elucidating how subtle structural alterations can impact neurocognition and psychosocial function in FEP.},
}

@article {pmid38635054,
year = {2024},
author = {Yan, Y and An, X and Ren, H and Luo, B and Jin, S and Liu, L and Di, Y and Li, T and Huang, Y},
title = {Nomogram-based geometric and hemodynamic parameters for predicting the growth of small untreated intracranial aneurysms.},
journal = {Neurosurgical review},
volume = {47},
number = {1},
pages = {169},
pmid = {38635054},
issn = {1437-2320},
support = {20JCZDJC00620//Tianjin Science and Technology Program/ ; 20JCZDJC00620//Tianjin Science and Technology Program/ ; },
mesh = {Humans ; *Intracranial Aneurysm ; Nomograms ; Retrospective Studies ; Angiography ; Hemodynamics ; },
abstract = {Previous studies have shown that the growth status of intracranial aneurysms (IAs) predisposes to rupture. This study aimed to construct a nomogram for predicting the growth of small IAs based on geometric and hemodynamic parameters. We retrospectively collected the baseline and follow-up angiographic images (CTA/ MRA) of 96 small untreated saccular IAs, created patient-specific vascular models and performed computational fluid dynamics (CFD) simulations. Geometric and hemodynamic parameters were calculated. A stepwise Cox proportional hazards regression analysis was employed to construct a nomogram. IAs were stratified into low-, intermediate-, and high-risk groups based on the total points from the nomogram. Receiver operating characteristic (ROC) analysis, calibration curves, decision curve analysis (DCA) and Kaplan-Meier curves were evaluated for internal validation. In total, 30 untreated saccular IAs were grown (31.3%; 95%CI 21.8%-40.7%). The PHASES, ELAPSS, and UIATS performed poorly in distinguishing growth status. Hypertension (hazard ratio [HR] 4.26, 95%CI 1.61-11.28; P = 0.004), nonsphericity index (95%CI 4.10-25.26; P = 0.003), max relative residence time (HR 1.01, 95%CI 1.00-1.01; P = 0.032) were independently related to the growth status. A nomogram was constructed with the above predictors and achieved a satisfactory prediction in the validation cohort. The log-rank test showed significant discrimination among the Kaplan-Meier curves of different risk groups in the training and validation cohorts. A nomogram consisting of geometric and hemodynamic parameters presented an accurate prediction for the growth status of small IAs and achieved risk stratification. It showed higher predictive efficacy than the assessment tools.},
}

Humans
*Intracranial Aneurysm
Nomograms
Retrospective Studies
Angiography
Hemodynamics

@article {pmid38633751,
year = {2024},
author = {Behboodi, A and Kline, J and Gravunder, A and Phillips, C and Parker, SM and Damiano, DL},
title = {Development and evaluation of a BCI-neurofeedback system with real-time EEG detection and electrical stimulation assistance during motor attempt for neurorehabilitation of children with cerebral palsy.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1346050},
pmid = {38633751},
issn = {1662-5161},
abstract = {In the realm of motor rehabilitation, Brain-Computer Interface Neurofeedback Training (BCI-NFT) emerges as a promising strategy. This aims to utilize an individual's brain activity to stimulate or assist movement, thereby strengthening sensorimotor pathways and promoting motor recovery. Employing various methodologies, BCI-NFT has been shown to be effective for enhancing motor function primarily of the upper limb in stroke, with very few studies reported in cerebral palsy (CP). Our main objective was to develop an electroencephalography (EEG)-based BCI-NFT system, employing an associative learning paradigm, to improve selective control of ankle dorsiflexion in CP and potentially other neurological populations. First, in a cohort of eight healthy volunteers, we successfully implemented a BCI-NFT system based on detection of slow movement-related cortical potentials (MRCP) from EEG generated by attempted dorsiflexion to simultaneously activate Neuromuscular Electrical Stimulation which assisted movement and served to enhance sensory feedback to the sensorimotor cortex. Participants also viewed a computer display that provided real-time visual feedback of ankle range of motion with an individualized target region displayed to encourage maximal effort. After evaluating several potential strategies, we employed a Long short-term memory (LSTM) neural network, a deep learning algorithm, to detect the motor intent prior to movement onset. We then evaluated the system in a 10-session ankle dorsiflexion training protocol on a child with CP. By employing transfer learning across sessions, we could significantly reduce the number of calibration trials from 50 to 20 without compromising detection accuracy, which was 80.8% on average. The participant was able to complete the required calibration trials and the 100 training trials per session for all 10 sessions and post-training demonstrated increased ankle dorsiflexion velocity, walking speed and step length. Based on exceptional system performance, feasibility and preliminary effectiveness in a child with CP, we are now pursuing a clinical trial in a larger cohort of children with CP.},
}

@article {pmid38632207,
year = {2024},
author = {Hossain, A and Khan, P and Kader, MF},
title = {Imagined speech classification exploiting EEG power spectrum features.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {38632207},
issn = {1741-0444},
abstract = {Imagined speech recognition has developed as a significant topic of research in the field of brain-computer interfaces. This innovative technique has great promise as a communication tool, providing essential help to those with impairments. An imagined speech recognition model is proposed in this paper to identify the ten most frequently used English alphabets (e.g., A, D, E, H, I, N, O, R, S, T) and numerals (e.g., 0 to 9). A novel electroencephalogram (EEG) dataset was created by measuring the brain activity of 30 people while they imagined these alphabets and digits. As part of signal preprocessing, EEG signals are filtered before extracting delta, theta, alpha, and beta band power features. These features are used as input for classification using support vector machines, k-nearest neighbors, and random forest (RF) classifiers. It is identified that the RF classifier outperformed the others in terms of classification accuracy. Classification accuracies of 99.38% and 95.39% were achieved at the coarse-level and fine-level, respectively with the RF classifier. From our study, it is also revealed that the beta frequency band and the frontal lobe of the brain played crucial roles in imagined speech recognition. Furthermore, a comparative analysis against state-of-the-art techniques is conducted to demonstrate the efficacy of our proposed model.},
}

@article {pmid38630669,
year = {2024},
author = {Zhu, HY and Chen, HT and Lin, CT},
title = {Understanding the effects of stress on the P300 response during naturalistic simulation of heights exposure.},
journal = {PloS one},
volume = {19},
number = {4},
pages = {e0301052},
pmid = {38630669},
issn = {1932-6203},
mesh = {Humans ; *Event-Related Potentials, P300/physiology ; *Brain/physiology ; Computer Simulation ; Alpha Rhythm ; Head ; Electroencephalography ; },
abstract = {Stress is a prevalent bodily response universally experienced and significantly affects a person's mental and cognitive state. The P300 response is a commonly observed brain behaviour that provides insight into a person's cognitive state. Previous works have documented the effects of stress on the P300 behaviour; however, only a few have explored the performance in a mobile and naturalistic experimental setup. Our study examined the effects of stress on the human brain's P300 behaviour through a height exposure experiment that incorporates complex visual, vestibular, and proprioceptive stimuli. A more complex sensory environment could produce translatable findings toward real-world behaviour and benefit emerging technologies such as brain-computer interfaces. Seventeen participants experienced our experiment that elicited the stress response through physical and virtual height exposure. We found two unique groups within our participants that exhibited contrasting behavioural performance and P300 target reaction response when exposed to stressors (from walking at heights). One group performed worse when exposed to heights and exhibited a significant decrease in parietal P300 peak amplitude and increased beta and gamma power. On the other hand, the group less affected by stress exhibited a change in their N170 peak amplitude and alpha/mu rhythm desynchronisation. The findings of our study suggest that a more individualised approach to assessing a person's behaviour performance under stress can aid in understanding P300 performance when experiencing stress.},
}

Humans
*Event-Related Potentials, P300/physiology
*Brain/physiology
Computer Simulation
Alpha Rhythm
Head
Electroencephalography

@article {pmid38628700,
year = {2024},
author = {Tao, G and Yang, S and Xu, J and Wang, L and Yang, B},
title = {Global research trends and hotspots of artificial intelligence research in spinal cord neural injury and restoration-a bibliometrics and visualization analysis.},
journal = {Frontiers in neurology},
volume = {15},
number = {},
pages = {1361235},
pmid = {38628700},
issn = {1664-2295},
abstract = {BACKGROUND: Artificial intelligence (AI) technology has made breakthroughs in spinal cord neural injury and restoration in recent years. It has a positive impact on clinical treatment. This study explores AI research's progress and hotspots in spinal cord neural injury and restoration. It also analyzes research shortcomings related to this area and proposes potential solutions.

METHODS: We used CiteSpace 6.1.R6 and VOSviewer 1.6.19 to research WOS articles on AI research in spinal cord neural injury and restoration.

RESULTS: A total of 1,502 articles were screened, in which the United States dominated; Kadone, Hideki (13 articles, University of Tsukuba, JAPAN) was the author with the highest number of publications; ARCH PHYS MED REHAB (IF = 4.3) was the most cited journal, and topics included molecular biology, immunology, neurology, sports, among other related areas.

CONCLUSION: We pinpointed three research hotspots for AI research in spinal cord neural injury and restoration: (1) intelligent robots and limb exoskeletons to assist rehabilitation training; (2) brain-computer interfaces; and (3) neuromodulation and noninvasive electrical stimulation. In addition, many new hotspots were discussed: (1) starting with image segmentation models based on convolutional neural networks; (2) the use of AI to fabricate polymeric biomaterials to provide the microenvironment required for neural stem cell-derived neural network tissues; (3) AI survival prediction tools, and transcription factor regulatory networks in the field of genetics were discussed. Although AI research in spinal cord neural injury and restoration has many benefits, the technology has several limitations (data and ethical issues). The data-gathering problem should be addressed in future research, which requires a significant sample of quality clinical data to build valid AI models. At the same time, research on genomics and other mechanisms in this field is fragile. In the future, machine learning techniques, such as AI survival prediction tools and transcription factor regulatory networks, can be utilized for studies related to the up-regulation of regeneration-related genes and the production of structural proteins for axonal growth.},
}

@article {pmid38626760,
year = {2024},
author = {Lopez-Bernal, D and Balderas, D and Ponce, P and Molina, A},
title = {Exploring inter-trial coherence for inner speech classification in EEG-based brain-computer interface.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad3f50},
pmid = {38626760},
issn = {1741-2552},
abstract = {OBJECTIVE: In recent years, EEG-based Brain-Computer Interfaces (BCIs) applied to inner speech classification have gathered attention for their potential to provide a communication channel for individuals with speech disabilities. However, existing methodologies for this task fall short in achieving acceptable accuracy for real-life implementation. This paper concentrated on exploring the possibility of using inter-trial coherence (ITC) as a feature extraction technique to enhance inner speech classification accuracy in EEG-based BCIs.

APPROACH: To address the objective, this work presents a novel methodology that employs ITC for feature extraction within a complex Morlet time-frequency representation. The study involves a dataset comprising EEG recordings of four different words for ten subjects, with three recording sessions per subject. The extracted features are then classified using k-Nearest-Neighbors (kNN) and Support Vector Machine (SVM).

MAIN RESULTS: The average classification accuracy achieved using the proposed methodology is 56.08% for kNN and 59.55% for SVM. These results demonstrate comparable or superior performance in comparison to previous works. The exploration of inter-trial phase coherence as a feature extraction technique proves promising for enhancing accuracy in inner speech classification within EEG-based BCIs.

SIGNIFICANCE: This study contributes to the advancement of EEG-based BCIs for inner speech classification by introducing a feature extraction methodology using ITC. The obtained results, on par or superior to previous works, highlight the potential significance of this approach in improving the accuracy of BCI systems. The exploration of this technique lays the groundwork for further research toward inner speech decoding.},
}

@article {pmid38625770,
year = {2024},
author = {Zhang, S and Cui, H and Li, Y and Chen, X and Gao, X and Guan, C},
title = {Improving SSVEP-BCI Performance Through Repetitive Anodal tDCS-Based Neuromodulation: Insights From Fractal EEG and Brain Functional Connectivity.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {32},
number = {},
pages = {1647-1656},
doi = {10.1109/TNSRE.2024.3389051},
pmid = {38625770},
issn = {1558-0210},
abstract = {This study embarks on a comprehensive investigation of the effectiveness of repetitive transcranial direct current stimulation (tDCS)-based neuromodulation in augmenting steady-state visual evoked potential (SSVEP) brain-computer interfaces (BCIs), alongside exploring pertinent electroencephalography (EEG) biomarkers for assessing brain states and evaluating tDCS efficacy. EEG data were garnered across three distinct task modes (eyes open, eyes closed, and SSVEP stimulation) and two neuromodulation patterns (sham-tDCS and anodal-tDCS). Brain arousal and brain functional connectivity were measured by extracting features of fractal EEG and information flow gain, respectively. Anodal-tDCS led to diminished offsets and enhanced information flow gains, indicating improvements in both brain arousal and brain information transmission capacity. Additionally, anodal-tDCS markedly enhanced SSVEP-BCIs performance as evidenced by increased amplitudes and accuracies, whereas sham-tDCS exhibited lesser efficacy. This study proffers invaluable insights into the application of neuromodulation methods for bolstering BCI performance, and concurrently authenticates two potent electrophysiological markers for multifaceted characterization of brain states.},
}

@article {pmid38625520,
year = {2024},
author = {Della Vedova, G and Proverbio, AM},
title = {Neural signatures of imaginary motivational states: desire for music, movement and social play.},
journal = {Brain topography},
volume = {},
number = {},
pages = {},
pmid = {38625520},
issn = {1573-6792},
abstract = {The literature has demonstrated the potential for detecting accurate electrical signals that correspond to the will or intention to move, as well as decoding the thoughts of individuals who imagine houses, faces or objects. This investigation examines the presence of precise neural markers of imagined motivational states through the combining of electrophysiological and neuroimaging methods. 20 participants were instructed to vividly imagine the desire to move, listen to music or engage in social activities. Their EEG was recorded from 128 scalp sites and analysed using individual standardized Low-Resolution Brain Electromagnetic Tomographies (LORETAs) in the N400 time window (400-600 ms). The activation of 1056 voxels was examined in relation to the 3 motivational states. The most active dipoles were grouped in eight regions of interest (ROI), including Occipital, Temporal, Fusiform, Premotor, Frontal, OBF/IF, Parietal, and Limbic areas. The statistical analysis revealed that all motivational imaginary states engaged the right hemisphere more than the left hemisphere. Distinct markers were identified for the three motivational states. Specifically, the right temporal area was more relevant for "Social Play", the orbitofrontal/inferior frontal cortex for listening to music, and the left premotor cortex for the "Movement" desire. This outcome is encouraging in terms of the potential use of neural indicators in the realm of brain-computer interface, for interpreting the thoughts and desires of individuals with locked-in syndrome.},
}

@article {pmid38624364,
year = {2024},
author = {Wang, Z and Xiang, L and Zhang, R},
title = {P300 intention recognition based on phase lag index (PLI)-rich-club brain functional network.},
journal = {The Review of scientific instruments},
volume = {95},
number = {4},
pages = {},
doi = {10.1063/5.0202770},
pmid = {38624364},
issn = {1089-7623},
mesh = {Humans ; *Intention ; *Brain ; Electrodes ; Support Vector Machine ; },
abstract = {Brain-computer interface (BCI) technology based on P300 signals has a broad application prospect in the assessment and diagnosis of clinical diseases and game control. The paper of selecting key electrodes to realize a wearable intention recognition system has become a hotspot for scholars at home and abroad. In this paper, based on the rich-club phenomenon that exists in the process of intention generation, a phase lag index (PLI)-rich-club-based intention recognition method for P300 is proposed. The rich-club structure is a network consisting of electrodes that are highly connected with other electrodes in the process of P300 generation. To construct the rich-club network, this paper uses PLI to construct the brain functional network, calculates rich-club coefficients of the network in the range of k degrees, initially identifies rich-club nodes based on the feature of node degree, and then performs a descending order of betweenness centrality and identifies the nodes with larger betweenness centrality as the specific rich-club nodes, extracts the non-linear features and frequency domain features of Rich-club nodes, and finally uses support vector machine for classification. The experimental results show that the range of rich-club coefficients is smaller with intent compared to that without intent. Validation was performed on the BCI Competition III dataset by reducing the number of channels to 17 and 16 for subject A and subject B, with recognition quasi-departure rates of 96.93% and 94.93%, respectively, and on the BCI Competition II dataset by reducing the number of channels to 17 for subjects, with a recognition accuracy of 95.50%.},
}

Humans
*Intention
*Brain
Electrodes
Support Vector Machine

@article {pmid38621380,
year = {2024},
author = {Barmpas, K and Panagakis, Y and Zoumpourlis, G and Adamos, DA and Laskaris, N and Zafeiriou, S},
title = {A causal perspective on brainwave modeling for brain-computer interfaces.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad3eb5},
pmid = {38621380},
issn = {1741-2552},
abstract = {Machine learning models have opened up enormous opportunities in the field of Brain-Computer Interfaces (BCIs). Despite their great success, they usually face severe limitations when they are employed in real-life applications outside a controlled laboratory setting. Mixing causal reasoning, identifying causal relationships between variables of interest, with brainwave modeling can change one's viewpoint on some of these major challenges which can be found in various stages in the machine learning pipeline, ranging from data collection and data pre-processing to training methods and techniques. In this work, we employ causal reasoning and present a framework aiming to breakdown and analyze important challenges of brainwave modeling for BCIs. Furthermore, we present how general machine learning practices as well as brainwave-specific techniques can be utilized and solve some of these identified challenges. And finally, we discuss appropriate evaluation schemes in order to measure these techniques' performance and efficiently compare them with other methods that will be developed in the future.},
}

@article {pmid38624425,
year = {2021},
author = {Bergsman, KC and Chudler, EH},
title = {Adapting a Neural Engineering Summer Camp for High School Students to a Fully Online Experience.},
journal = {Biomedical engineering education},
volume = {1},
number = {1},
pages = {37-42},
pmid = {38624425},
issn = {2730-5945},
abstract = {The COVID-19 pandemic and its resulting health and safety concerns caused the cancellation of many engineering education opportunities for high school students. To expose high school students to the field of neural engineering and encourage them to pursue academic pathways in biomedical engineering, the Center for Neurotechnology (CNT) at the University of Washington converted an in-person summer camp to a fully online program (Virtual REACH Program, VRP) offering both synchronous and asynchronous resources. The VRP is a five-day online program that focuses on a different daily theme (neuroscience, brain-computer interfaces, electrical stimulation, neuroethics, career/academic pathways). Each day, the VRP starts with a live videoconference meeting (lecture and interactive discussion) with a CNT faculty member. The online lectures are supported by at-home learning resources (e.g., text, videos, activities, quizzes) embedded within a digital book created using the Pressbook platform. An online bulletin board (Padlet) is also used by students to share artifacts and build community. Program evaluation will be conducted by an external evaluator. A summative survey will collect information on participants' experiences in the VRP and will help inform future iterations of the program. Although significant time was required to create a digital book, the VRP will reach a larger audience than the prior in-person program and resulted in the creation of learning tools that can be used in the future.},
}

@article {pmid38619940,
year = {2024},
author = {Pang, M and Wang, H and Huang, J and Vong, CM and Zeng, Z and Chen, C},
title = {Multi-Scale Masked Autoencoders for Cross-Session Emotion Recognition.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3389037},
pmid = {38619940},
issn = {1558-0210},
abstract = {Affective brain-computer interfaces (aBCIs) have garnered widespread applications, with remarkable advancements in utilizing electroencephalogram (EEG) technology for emotion recognition. However, the time-consuming process of annotating EEG data, inherent individual differences, non-stationary characteristics of EEG data, and noise artifacts in EEG data collection pose formidable challenges in developing subject-specific cross-session emotion recognition models. To simultaneously address these challenges, we propose a unified pre-training framework based on multi-scale masked autoencoders (MSMAE), which utilizes large-scale unlabeled EEG signals from multiple subjects and sessions to extract noise-robust, subject-invariant, and temporal-invariant features. We subsequently fine-tune the obtained generalized features with only a small amount of labeled data from a specific subject for personalization and enable cross-session emotion recognition. Our framework emphasizes: 1) Multi-scale representation to capture diverse aspects of EEG signals, obtaining comprehensive information; 2) An improved masking mechanism for robust channel-level representation learning, addressing missing channel issues while preserving inter-channel relationships; and 3) Invariance learning for regional correlations in spatial-level representation, minimizing inter-subject and inter-session variances. Under these elaborate designs, the proposed MSMAE exhibits a remarkable ability to decode emotional states from a different session of EEG data during the testing phase. Extensive experiments conducted on the two publicly available datasets, i.e., SEED and SEED-IV, demonstrate that the proposed MSMAE consistently achieves stable results and outperforms competitive baseline methods in cross-session emotion recognition.},
}

@article {pmid38618604,
year = {2024},
author = {Di Gregorio, MF and Der, C and Bravo-Torres, S and Zernotti, ME},
title = {Active Bone Conduction Implant and Adhesive Bone Conduction Device: A Comparison of Audiological Performance and Subjective Satisfaction.},
journal = {International archives of otorhinolaryngology},
volume = {28},
number = {2},
pages = {e332-e338},
pmid = {38618604},
issn = {1809-9777},
abstract = {Introduction Atresia of the external auditory canal affects 1 in every 10 thousand to 20 thousand live births, with a much higher prevalence in Latin America, at 5 to 21 out of every 10 thousand newborns. The treatment involves esthetic and functional aspects. Regarding the functional treatment, there are surgical and nonsurgical alternatives like spectacle frames and rigid and softband systems. Active transcutaneous bone conduction implants (BCIs) achieve good sound transmission and directly stimulate the bone. Objective To assess the audiological performance and subjective satisfaction of children implanted with an active transcutaneous BCI for more than one year and to compare the outcomes with a nonsurgical adhesive bone conduction device (aBCD) in the same users. Methods The present is a prospective, multicentric study. The audiological performance was evaluated at 1, 6, and 12 months postactivation, and after a 1-month trial with the nonsurgical device. Results Ten patients completed all tests. The 4-frequency pure-tone average (4PTA) in the unaided condition was of 65 dB HL, which improved significantly to 20 dB HL after using the BCI for 12 months. The speech recognition in quiet in the unaided condition was of 33% on average, which improved significantly, to 99% with the BCI, and to 91% with the aBCD. Conclusion The aBCD demonstrated sufficient hearing improvement and subjective satisfaction; thus, it is a good solution for hearing rehabilitation if surgery is not desired or not possible. If surgery is an option, the BCI is the superior device in terms of hearing outcomes, particularly background noise and subjective satisfaction.},
}

@article {pmid38618207,
year = {2024},
author = {Kong, L and Wang, H and Yan, N and Xu, C and Chen, Y and Zeng, Y and Guo, X and Lu, J and Hu, S},
title = {Effect of antipsychotics and mood stabilisers on metabolism in bipolar disorder: a network meta-analysis of randomised-controlled trials.},
journal = {EClinicalMedicine},
volume = {71},
number = {},
pages = {102581},
pmid = {38618207},
issn = {2589-5370},
abstract = {BACKGROUND: Antipsychotics and mood stabilisers are gathering attention for the disturbance of metabolism. This network meta-analysis aims to evaluate and rank the metabolic effects of the commonly used antipsychotics and mood stabilisers in treating bipolar disorder (BD).

METHODS: Registries including PubMed, Embase, Cochrane Library, Web of Science, Ovid, and Google Scholar were searched before February 15th, 2024, for randomised controlled trials (RCTs) applying antipsychotics or mood stabilisers for BD treatment. The observed outcomes were twelve metabolic indicators. The data were extracted by two reviewers independently, and confirmed by another four reviewers and a corresponding author. The above six reviewers all participated in data analyses. Data extraction was based on PRISMA guidelines, and quality assessment was conducted according to the Cochrane Handbook. Use a random effects model for data pooling. The PROSPERO registration number is CRD42023466669.

FINDINGS: Together, 5421 records were identified, and 41 publications with 11,678 complete-trial participants were confirmed eligible. After eliminating possible sensitivity, risperidone ranked 1st in elevating fasting serum glucose (SUCRA = 90.7%) and serum insulin (SUCRA = 96.6%). Lurasidone was most likely to elevate HbA1c (SUCRA = 82.1%). Olanzapine ranked 1st in elevating serum TC (SUCRA = 93.3%), TG (SUCRA = 89.6%), and LDL (SUCRA = 94.7%). Lamotrigine ranked 1st in reducing HDL (SUCRA = 82.6%). Amisulpride ranked 1st in elevating body weight (SUCRA = 100.0%). For subgroup analyses, quetiapine is more likely to affect indicators of glucose metabolism among male adult patients with bipolar mania, while long-term lurasidone tended to affect glucose metabolism among female patients with bipolar depression. Among patients under 18, divalproex tended to affect glucose metabolism, with lithium affecting lipid metabolism. In addition, most observed antipsychotics performed higher response and remission rates than placebo, and displayed a similar dropout rate with placebo, while no between-group significance of rate was observed among mood stabilisers.

INTERPRETATION: Our findings suggest that overall, antipsychotics are effective in treating BD, while they are also more likely to disturb metabolism than mood stabilisers. Attention should be paid to individual applicability in clinical practice. The results put forward evidence-based information and clinical inspiration for drug compatibility and further research of the BD mechanism.

FUNDING: The National Key Research and Development Program of China (2023YFC2506200), and the Research Project of Jinan Microecological Biomedicine Shandong Laboratory (No. JNL-2023001B).},
}

@article {pmid38617349,
year = {2024},
author = {Sharma, D and Lupkin, SM and McGinty, VB},
title = {Orbitofrontal high-gamma reflects spike-dissociable value and decision mechanisms.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
pmid = {38617349},
abstract = {The orbitofrontal cortex (OFC) plays a crucial role in value-based decision-making. While previous research has focused on spiking activity in OFC neurons, the role of OFC local field potentials (LFPs) in decision-making remains unclear. LFPs are important because they can reflect synaptic and subthreshold activity not directly coupled to spiking, and because they are potential targets for less invasive forms of brain-machine interface (BMI). We recorded LFPs and spiking activity using multi-channel vertical probes while monkeys performed a two-option value-based decision-making task. We compared the value- and decision-coding properties of high-gamma range LFPs (HG, 50-150 Hz) to the coding properties of spiking multi-unit activity (MUA) recorded concurrently on the same electrodes. Results show that HG and MUA both represent the values of decision targets, and that their representations have similar temporal profiles in a trial. However, we also identified value-coding properties of HG that were dissociable from the concurrently-measured MUA. On average across channels, HG amplitude increased monotonically with value, whereas the average value encoding in MUA was net neutral. HG also encoded a signal consistent with a comparison between the values of the two targets, a signal which was much weaker in MUA. In individual channels, HG was better able to predict choice outcomes than MUA; however, when simultaneously recorded channels were combined in population-based decoder, MUA provided more accurate predictions than HG. Interestingly, HG value representations were accentuated in channels in or near shallow cortical layers, suggesting a dissociation between neuronal sources of HG and MUA. In summary, we find that HG signals are dissociable from MUA with respect to cognitive variables encoded in prefrontal cortex, evident in the monotonic encoding of value, stronger encoding of value comparisons, and more accurate predictions about behavior. High-frequency LFPs may therefore be a viable - or even preferable - target for BMIs to assist cognitive function, opening the possibility for less invasive access to mental contents that would otherwise be observable only with spike-based measures.},
}

@article {pmid38617132,
year = {2024},
author = {Qiao, Y and Mu, J and Xie, J and Hu, B and Liu, G},
title = {Music emotion recognition based on temporal convolutional attention network using EEG.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1324897},
pmid = {38617132},
issn = {1662-5161},
abstract = {Music is one of the primary ways to evoke human emotions. However, the feeling of music is subjective, making it difficult to determine which emotions music triggers in a given individual. In order to correctly identify emotional problems caused by different types of music, we first created an electroencephalogram (EEG) data set stimulated by four different types of music (fear, happiness, calm, and sadness). Secondly, the differential entropy features of EEG were extracted, and then the emotion recognition model CNN-SA-BiLSTM was established to extract the temporal features of EEG, and the recognition performance of the model was improved by using the global perception ability of the self-attention mechanism. The effectiveness of the model was further verified by the ablation experiment. The classification accuracy of this method in the valence and arousal dimensions is 93.45% and 96.36%, respectively. By applying our method to a publicly available EEG dataset DEAP, we evaluated the generalization and reliability of our method. In addition, we further investigate the effects of different EEG bands and multi-band combinations on music emotion recognition, and the results confirm relevant neuroscience studies. Compared with other representative music emotion recognition works, this method has better classification performance, and provides a promising framework for the future research of emotion recognition system based on brain computer interface.},
}

@article {pmid38616969,
year = {2024},
author = {Zhang, Y and Zheng, Y and Ni, P and Liang, S and Li, X and Yu, H and Wei, W and Qi, X and Yu, X and Xue, R and Zhao, L and Deng, W and Wang, Q and Guo, W and Li, T},
title = {New role of platelets in schizophrenia: predicting drug response.},
journal = {General psychiatry},
volume = {37},
number = {2},
pages = {e101347},
pmid = {38616969},
issn = {2517-729X},
abstract = {BACKGROUND: Elevated platelet count (PLTc) is associated with first-episode schizophrenia and adverse outcomes in individuals with precursory psychosis. However, the impact of antipsychotic medications on PLTc and its association with symptom improvement remain unclear.

AIMS: We aimed to investigate changes in PLTc levels following antipsychotic treatment and assess whether PLTc can predict antipsychotic responses and metabolic changes after accounting for other related variables.

METHODS: A total of 2985 patients with schizophrenia were randomised into seven groups. Each group received one of seven antipsychotic treatments and was assessed at 2, 4 and 6 weeks. Clinical symptoms were evaluated using the positive and negative syndrome scale (PANSS). Additionally, we measured blood cell counts and metabolic parameters, such as blood lipids. Repeated measures analysis of variance was used to examine the effect of antipsychotics on PLTc changes, while structural equation modelling was used to assess the predictive value of PLTc on PANSS changes.

RESULTS: PLTc significantly increased in patients treated with aripiprazole (F=6.00, p=0.003), ziprasidone (F=7.10, p<0.001) and haloperidol (F=3.59, p=0.029). It exhibited a positive association with white blood cell count and metabolic indicators. Higher baseline PLTc was observed in non-responders, particularly in those defined by the PANSS-negative subscale. In the structural equation model, PLTc, white blood cell count and a latent metabolic variable predicted the rate of change in the PANSS-negative subscale scores. Moreover, higher baseline PLTc was observed in individuals with less metabolic change, although this association was no longer significant after accounting for baseline metabolic values.

CONCLUSIONS: Platelet parameters, specifically PLTc, are influenced by antipsychotic treatment and could potentially elevate the risk of venous thromboembolism in patients with schizophrenia. Elevated PLTc levels and associated factors may impede symptom improvement by promoting inflammation. Given PLTc's easy measurement and clinical relevance, it warrants increased attention from psychiatrists.

TRIAL REGISTRATION NUMBER: ChiCTR-TRC-10000934.},
}

@article {pmid38616204,
year = {2024},
author = {Lei, D and Dong, C and Guo, H and Ma, P and Liu, H and Bao, N and Kang, H and Chen, X and Wu, Y},
title = {A fused multi-subfrequency bands and CBAM SSVEP-BCI classification method based on convolutional neural network.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {8616},
pmid = {38616204},
issn = {2045-2322},
support = {S20231148Z//Inner Mongolia Autonomous Region Graduate Research Innovation Project/ ; 61364018//the National Natural Science Foundation of China/ ; 61863029//the National Natural Science Foundation of China/ ; 2016JQ07//Inner Mongolia Natural Science Foundation/ ; 2020MS06020//Inner Mongolia Natural Science Foundation/ ; 2021MS06017//Inner Mongolia Natural Science Foundation/ ; CGZH2018129//Inner Mongolia Scientific and Technological Achievements Transformation Project/ ; 2023JSYD01006//Industrial Technology Innovation Program of IMAST/ ; 2021GG0264//Science and Technology Plan Project of Inner Mongolia Autonomous Region/ ; 2020GG0268//Science and Technology Plan Project of Inner Mongolia Autonomous Region/ ; },
abstract = {For the brain-computer interface (BCI) system based on steady-state visual evoked potential (SSVEP), it is difficult to obtain satisfactory classification performance for short-time window SSVEP signals by traditional methods. In this paper, a fused multi-subfrequency bands and convolutional block attention module (CBAM) classification method based on convolutional neural network (CBAM-CNN) is proposed for discerning SSVEP-BCI tasks. This method extracts multi-subfrequency bands SSVEP signals as the initial input of the network model, and then carries out feature fusion on all feature inputs. In addition, CBAM is embedded in both parts of the initial input and feature fusion for adaptive feature refinement. To verify the effectiveness of the proposed method, this study uses the datasets of Inner Mongolia University of Technology (IMUT) and Tsinghua University (THU) to evaluate the performance of the proposed method. The experimental results show that the highest accuracy of CBAM-CNN reaches 0.9813 percentage point (pp). Within 0.1-2 s time window, the accuracy of CBAM-CNN is 0.0201-0.5388 (pp) higher than that of CNN, CCA-CWT-SVM, CCA-SVM, CCA-GNB, FBCCA, and CCA. Especially in the short-time window range of 0.1-1 s, the performance advantage of CBAM-CNN is more significant. The maximum information transmission rate (ITR) of CBAM-CNN is 503.87 bit/min, which is 227.53 bit/min-503.41 bit/min higher than the above six EEG decoding methods. The study further results show that CBAM-CNN has potential application value in SSVEP decoding.},
}

@article {pmid38616136,
year = {2024},
author = {Huang, T and Guo, X and Huang, X and Yi, C and Cui, Y and Dong, Y},
title = {Input-output specific orchestration of aversive valence in lateral habenula during stress dynamics.},
journal = {Journal of Zhejiang University. Science. B},
volume = {},
number = {},
pages = {1-11},
doi = {10.1631/jzus.B2300933},
pmid = {38616136},
issn = {1862-1783},
support = {2022ZD0211700//the STI2030-Major Projects/ ; 32371057, 31922031, 32071017, 81971309, 32170980, 82201707 and 82200562//the National Natural Science Foundation of China/ ; LDQ24C090001//the Zhejiang Provincial Natural Science Foundation of China/ ; 2023-PT310-01//the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences/ ; JCTD-2022-11//the CAS Youth Interdisciplinary Team/ ; BX20230319//the China Postdoctoral Science Foundation/ ; 2022B1515020012 and 2021A1515110121//the Guangdong Basic and Applied Basic Research Foundation/ ; 2023B1212060018//the Science and Technology Planning Project of Guangdong Province/ ; JCYJ20210324123212035, RCYX20200714114644167, ZDSYS20220606100801003, JCYJ20210324122809025 and JCYJ20230807110315031//the Shenzhen Fundamental Research Program/ ; },
abstract = {Stress has been considered as a major risk factor for depressive disorders, triggering depression onset via inducing persistent dysfunctions in specialized brain regions and neural circuits. Among various regions across the brain, the lateral habenula (LHb) serves as a critical hub for processing aversive information during the dynamic process of stress accumulation, thus having been implicated in the pathogenesis of depression. LHb neurons integrate aversive valence conveyed by distinct upstream inputs, many of which selectively innervate the medial part (LHbM) or lateral part (LHbL) of LHb. LHb subregions also separately assign aversive valence via dissociable projections to the downstream targets in the midbrain which provides feedback loops. Despite these strides, the spatiotemporal dynamics of LHb-centric neural circuits remain elusive during the progression of depression-like state under stress. In this review, we attempt to describe a framework in which LHb orchestrates aversive valence via the input-output specific neuronal architecture. Notably, a physiological form of Hebbian plasticity in LHb under multiple stressors has been unveiled to incubate neuronal hyperactivity in an input-specific manner, which causally encodes chronic stress experience and drives depression onset. Collectively, the recent progress and future efforts in elucidating LHb circuits shed light on early interventions and circuit-specific antidepressant therapies.},
}

@article {pmid38615362,
year = {2024},
author = {Qi, X and Yu, X and Wei, L and Jiang, H and Dong, J and Li, H and Wei, Y and Zhao, L and Deng, W and Guo, W and Hu, X and Li, T},
title = {Novel α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid receptor (AMPAR) potentiator LT-102: A promising therapeutic agent for treating cognitive impairment associated with schizophrenia.},
journal = {CNS neuroscience & therapeutics},
volume = {30},
number = {4},
pages = {e14713},
pmid = {38615362},
issn = {1755-5949},
support = {81920108018//National Natural Science Foundation of China/ ; 82371524//National Natural Science Foundation of China/ ; 82371503//National Natural Science Foundation of China/ ; 2022C03096//Key R&D Program of Zhejiang Province/ ; 2018B030334001//Special Foundation for Brain Research from Science and Technology Program of Guangdong Province/ ; LY22H090009//Natural Science Foundation of Zhejiang Province/ ; 2019HXCX02//Clinical Research Innovation Project, West China Hospital, Sichuan University/ ; //Project for Hangzhou Medical Disciplines of Excellence & Key Project for Hangzhou Medical Disciplines/ ; },
mesh = {Animals ; Mice ; Rats ; Phencyclidine ; *Schizophrenia/complications/drug therapy ; *Cognitive Dysfunction/drug therapy ; Isoxazoles ; *Propionates ; },
abstract = {AIMS: We aimed to evaluate the potential of a novel selective α-amino-3-hydroxy-5-methyl-4-isoxazole-propionic acid receptor (AMPAR) potentiator, LT-102, in treating cognitive impairments associated with schizophrenia (CIAS) and elucidating its mechanism of action.

METHODS: The activity of LT-102 was examined by Ca[2+] influx assays and patch-clamp in rat primary hippocampal neurons. The structure of the complex was determined by X-ray crystallography. The selectivity of LT-102 was evaluated by hERG tail current recording and kinase-inhibition assays. The electrophysiological characterization of LT-102 was characterized by patch-clamp recording in mouse hippocampal slices. The expression and phosphorylation levels of proteins were examined by Western blotting. Cognitive function was assessed using the Morris water maze and novel object recognition tests.

RESULTS: LT-102 is a novel and selective AMPAR potentiator with little agonistic effect, which binds to the allosteric site formed by the intradimer interface of AMPAR's GluA2 subunit. Treatment with LT-102 facilitated long-term potentiation in mouse hippocampal slices and reversed cognitive deficits in a phencyclidine-induced mouse model. Additionally, LT-102 treatment increased the protein level of brain-derived neurotrophic factor and the phosphorylation of GluA1 in primary neurons and hippocampal tissues.

CONCLUSION: We conclude that LT-102 ameliorates cognitive impairments in a phencyclidine-induced model of schizophrenia by enhancing synaptic function, which could make it a potential therapeutic candidate for CIAS.},
}

Animals
Mice
Rats
Phencyclidine
*Schizophrenia/complications/drug therapy
*Cognitive Dysfunction/drug therapy
Isoxazoles
*Propionates

@article {pmid38614892,
year = {2024},
author = {He, X and Li, W and Ma, H},
title = {Orchestrating neuronal activity-dependent translation via the integrated stress response protein GADD34.},
journal = {Trends in neurosciences},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.tins.2024.03.008},
pmid = {38614892},
issn = {1878-108X},
abstract = {In a recent study, Oliveira and colleagues revealed how growth arrest and DNA damage-inducible protein 34 (GADD34), an effector of the integrated stress response, initiates the translation of synaptic plasticity-related mRNAs following brain-derived neurotrophic factor (BDNF) stimulation. This work suggests that GADD34 may link transcriptional products with translation control upon neuronal activation, illuminating how protein synthesis is orchestrated in neuronal plasticity.},
}

@article {pmid38612334,
year = {2024},
author = {Knozowski, P and Nowakowski, JJ and Stawicka, AM and Dulisz, B and Górski, A},
title = {Effect of Management of Grassland on Prey Availability and Physiological Condition of Nestling of Red-Backed Shrike Lanius collurio.},
journal = {Animals : an open access journal from MDPI},
volume = {14},
number = {7},
pages = {},
pmid = {38612334},
issn = {2076-2615},
abstract = {The study aimed to determine the influence of grassland management on the potential food base of the red-backed shrike Lanius collurio and the condition of chicks in the population inhabiting semi-natural grasslands in the Narew floodplain. The grassland area was divided into three groups: extensively used meadows, intensively used meadows fertilised with mineral fertilisers, and intensively used meadows fertilised with liquid manure, and selected environmental factors that may influence food availability were determined. Using Barber traps, 1825 samples containing 53,739 arthropods were collected, and the diversity, abundance, and proportion of large arthropods in the samples were analysed depending on the grassland use type. In the bird population, the condition of the chicks was characterised by the BCI (Body Condition Index) and haematological parameters (glucose level, haemoglobin level, haematocrit, and H:L ratio). The diversity of arthropods was highest in extensively used meadows. Still, the mean abundance and proportion of arthropods over 1 cm in length differed significantly for Orthoptera, Hymenoptera, Arachne, and Carabidae between grassland use types, with the highest proportion of large arthropods and the highest abundance recorded in manure-fertilised meadows. The highest Body Condition Indexes and blood glucose levels of nestlings indicating good nestling nutrition were recorded in nests of birds associated with extensive land use. The H:L ratio as an indicator of the physiological condition of nestlings was high on manure-fertilised and extensively managed meadows, indicating stress factors associated with these environments. This suggests that consideration should be given to the effects of chemicals, such as pesticides or drug residues, that may come from slurry poured onto fields on the fitness of red-backed shrike chicks.},
}

@article {pmid38610540,
year = {2024},
author = {Clemente, L and La Rocca, M and Paparella, G and Delussi, M and Tancredi, G and Ricci, K and Procida, G and Introna, A and Brunetti, A and Taurisano, P and Bevilacqua, V and de Tommaso, M},
title = {Exploring Aesthetic Perception in Impaired Aging: A Multimodal Brain-Computer Interface Study.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {7},
pages = {},
pmid = {38610540},
issn = {1424-8220},
support = {POR Puglia FESR-FSE 2014-2020 - Asse I - Azione 1.4.b//Regione Puglia/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; Aging ; Brain ; Esthetics ; Perception ; },
abstract = {In the field of neuroscience, brain-computer interfaces (BCIs) are used to connect the human brain with external devices, providing insights into the neural mechanisms underlying cognitive processes, including aesthetic perception. Non-invasive BCIs, such as EEG and fNIRS, are critical for studying central nervous system activity and understanding how individuals with cognitive deficits process and respond to aesthetic stimuli. This study assessed twenty participants who were divided into control and impaired aging (AI) groups based on MMSE scores. EEG and fNIRS were used to measure their neurophysiological responses to aesthetic stimuli that varied in pleasantness and dynamism. Significant differences were identified between the groups in P300 amplitude and late positive potential (LPP), with controls showing greater reactivity. AI subjects showed an increase in oxyhemoglobin in response to pleasurable stimuli, suggesting hemodynamic compensation. This study highlights the effectiveness of multimodal BCIs in identifying the neural basis of aesthetic appreciation and impaired aging. Despite its limitations, such as sample size and the subjective nature of aesthetic appreciation, this research lays the groundwork for cognitive rehabilitation tailored to aesthetic perception, improving the comprehension of cognitive disorders through integrated BCI methodologies.},
}

Humans
*Brain-Computer Interfaces
Aging
Brain
Esthetics
Perception

@article {pmid38609642,
year = {2023},
author = {Zeng, J and Zhang, Y and Xiang, Y and Liang, S and Xue, C and Zhang, J and Ran, Y and Cao, M and Huang, F and Huang, S and Deng, W and Li, T},
title = {Optimizing multi-domain hematologic biomarkers and clinical features for the differential diagnosis of unipolar depression and bipolar depression.},
journal = {Npj mental health research},
volume = {2},
number = {1},
pages = {4},
pmid = {38609642},
issn = {2731-4251},
abstract = {There is a lack of objective features for the differential diagnosis of unipolar and bipolar depression, especially those that are readily available in practical settings. We investigated whether clinical features of disease course, biomarkers from complete blood count, and blood biochemical markers could accurately classify unipolar and bipolar depression using machine learning methods. This retrospective study included 1160 eligible patients (918 with unipolar depression and 242 with bipolar depression). Patient data were randomly split into training (85%) and open test (15%) sets 1000 times, and the average performance was reported. XGBoost achieved the optimal open-test performance using selected biomarkers and clinical features-AUC 0.889, sensitivity 0.831, specificity 0.839, and accuracy 0.863. The importance of features for differential diagnosis was measured using SHapley Additive exPlanations (SHAP) values. The most informative features include (1) clinical features of disease duration and age of onset, (2) biochemical markers of albumin, low density lipoprotein (LDL), and potassium, and (3) complete blood count-derived biomarkers of white blood cell count (WBC), platelet-to-lymphocyte ratio (PLR), and monocytes (MONO). Overall, onset features and hematologic biomarkers appear to be reliable information that can be readily obtained in clinical settings to facilitate the differential diagnosis of unipolar and bipolar depression.},
}

@article {pmid38608677,
year = {2024},
author = {Wang, Y and Wang, X and Wang, L and Zheng, L and Meng, S and Zhu, N and An, X and Wang, L and Yang, J and Zheng, C and Ming, D},
title = {Dynamic prediction of goal location by coordinated representation of prefrontal-hippocampal theta sequences.},
journal = {Current biology : CB},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.cub.2024.03.032},
pmid = {38608677},
issn = {1879-0445},
abstract = {Prefrontal (PFC) and hippocampal (HPC) sequences of neuronal firing modulated by theta rhythms could represent upcoming choices during spatial memory-guided decision-making. How the PFC-HPC network dynamically coordinates theta sequences to predict specific goal locations and how it is interrupted in memory impairments induced by amyloid beta (Aβ) remain unclear. Here, we detected theta sequences of firing activities of PFC neurons and HPC place cells during goal-directed spatial memory tasks. We found that PFC ensembles exhibited predictive representation of the specific goal location since the starting phase of memory retrieval, earlier than the hippocampus. High predictive accuracy of PFC theta sequences existed during successful memory retrieval and positively correlated with memory performance. Coordinated PFC-HPC sequences showed PFC-dominant prediction of goal locations during successful memory retrieval. Furthermore, we found that theta sequences of both regions still existed under Aβ accumulation, whereas their predictive representation of goal locations was weakened with disrupted spatial representation of HPC place cells and PFC neurons. These findings highlight the essential role of coordinated PFC-HPC sequences in successful memory retrieval of a precise goal location.},
}

@article {pmid38608331,
year = {2024},
author = {Inguscio, BMS and Cartocci, G and Sciaraffa, N and Nicastri, M and Giallini, I and Aricò, P and Greco, A and Babiloni, F and Mancini, P},
title = {Two are better than one: Differences in cortical EEG patterns during auditory and visual verbal working memory processing between Unilateral and Bilateral Cochlear Implanted children.},
journal = {Hearing research},
volume = {446},
number = {},
pages = {109007},
doi = {10.1016/j.heares.2024.109007},
pmid = {38608331},
issn = {1878-5891},
abstract = {Despite the proven effectiveness of cochlear implant (CI) in the hearing restoration of deaf or hard-of-hearing (DHH) children, to date, extreme variability in verbal working memory (VWM) abilities is observed in both unilateral and bilateral CI user children (CIs). Although clinical experience has long observed deficits in this fundamental executive function in CIs, the cause to date is still unknown. Here, we have set out to investigate differences in brain functioning regarding the impact of monaural and binaural listening in CIs compared with normal hearing (NH) peers during a three-level difficulty n-back task undertaken in two sensory modalities (auditory and visual). The objective of this pioneering study was to identify electroencephalographic (EEG) marker pattern differences in visual and auditory VWM performances in CIs compared to NH peers and possible differences between unilateral cochlear implant (UCI) and bilateral cochlear implant (BCI) users. The main results revealed differences in theta and gamma EEG bands. Compared with hearing controls and BCIs, UCIs showed hypoactivation of theta in the frontal area during the most complex condition of the auditory task and a correlation of the same activation with VWM performance. Hypoactivation in theta was also observed, again for UCIs, in the left hemisphere when compared to BCIs and in the gamma band in UCIs compared to both BCIs and NHs. For the latter two, a correlation was found between left hemispheric gamma oscillation and performance in the audio task. These findings, discussed in the light of recent research, suggest that unilateral CI is deficient in supporting auditory VWM in DHH. At the same time, bilateral CI would allow the DHH child to approach the VWM benchmark for NH children. The present study suggests the possible effectiveness of EEG in supporting, through a targeted approach, the diagnosis and rehabilitation of VWM in DHH children.},
}

@article {pmid38608024,
year = {2024},
author = {Abbasi, A and Rangwani, R and Bowen, DW and Fealy, AW and Danielsen, NP and Gulati, T},
title = {Cortico-cerebellar coordination facilitates neuroprosthetic control.},
journal = {Science advances},
volume = {10},
number = {15},
pages = {eadm8246},
pmid = {38608024},
issn = {2375-2548},
mesh = {Animals ; Rats ; *Cerebellum ; *Brain-Computer Interfaces ; Cell Nucleus ; Learning ; Movement ; },
abstract = {Temporally coordinated neural activity is central to nervous system function and purposeful behavior. Still, there is a paucity of evidence demonstrating how this coordinated activity within cortical and subcortical regions governs behavior. We investigated this between the primary motor (M1) and contralateral cerebellar cortex as rats learned a neuroprosthetic/brain-machine interface (BMI) task. In neuroprosthetic task, actuator movements are causally linked to M1 "direct" neurons that drive the decoder for successful task execution. However, it is unknown how task-related M1 activity interacts with the cerebellum. We observed a notable 3 to 6 hertz coherence that emerged between these regions' local field potentials (LFPs) with learning that also modulated task-related spiking. We identified robust task-related indirect modulation in the cerebellum, which developed a preferential relationship with M1 task-related activity. Inhibiting cerebellar cortical and deep nuclei activity through optogenetics led to performance impairments in M1-driven neuroprosthetic control. Together, these results demonstrate that cerebellar influence is necessary for M1-driven neuroprosthetic control.},
}

Animals
Rats
*Cerebellum
*Brain-Computer Interfaces
Cell Nucleus
Learning
Movement

@article {pmid38607193,
year = {2024},
author = {Kong, D and Chen, Y and Wang, L and Lu, Y and Luo, S and Chai, H and Chen, L},
title = {Adoption of Rehabilitation Climbing Wall Combined with Brain-computer Fusion Interface in Adolescent Idiopathic Scoliosis.},
journal = {Alternative therapies in health and medicine},
volume = {},
number = {},
pages = {},
pmid = {38607193},
issn = {1078-6791},
abstract = {BACKGROUND: As the adoption of brain-computer interface (BCI) technology in rehabilitation training is gradually maturing, the rehabilitation climbing walls combined with BCI technology are applied in adolescent idiopathic scoliosis (AIS) adoption research.

METHODS: From January 2022 to January 2023, a total of 100 AIS patients were assigned into a control group (group C, rehabilitation climbing wall training) and an observation group (group B, rehabilitation climbing wall training based on BCI technology) equally and randomly. The therapeutic effects of the patients were analyzed, including the Cobb angle, waist range of motion, and quality of life.

RESULTS: The Cobb angles of all patients after three months of treatment were obviously smaller than those preoperatively, and the Cobb angle of patients in group B was smaller than that of group C. The improvement rate of the Cobb angle of patients in group B was substantially superior to that in group C (95%CI 17.8-42.6, P = .034). Moreover, patients in groups C and B had more extensive waist flexion, tension, and left ranges. Suitable lateral regions after three months of treatment than before and lower lumbar dysfunction scores, and group B was significantly better than group C (95%CI 20.3-35.4, P = .042). After three months of treatment, all patients' general condition, physical pain, physiological function, and mental health scores were higher than those preoperatively, and the scores in group B were substantially superior to those in group C (95%CI 51.3-84.2, P = .022). Furthermore, the total effective rate of patients in group B after three months was markedly superior to that in group C (96% vs. 82%) (95%CI 79.3-97.2, P = .014).

CONCLUSION: The results of the study suggest that the rehabilitation climbing wall training method combined with brain-computer interface (BCI) technology has significant therapeutic effects on adolescent idiopathic scoliosis (AIS) patients. The intervention was found to effectively reduce the Cobb angle, increase the lumbar range of motion, improve lumbar function, and enhance the quality of life of the patients. These findings indicate that the adoption of rehabilitation climbing walls combined with BCI technology can be clinically valuable in the treatment of AIS. This approach holds promise in improving the rehabilitation outcomes for AIS patients, providing a non-invasive alternative to surgical interventions.},
}

@article {pmid38606614,
year = {2024},
author = {Xu, S and Xiao, X and Manshaii, F and Chen, J},
title = {Injectable Fluorescent Neural Interfaces for Cell-Specific Stimulating and Imaging.},
journal = {Nano letters},
volume = {},
number = {},
pages = {},
doi = {10.1021/acs.nanolett.4c00815},
pmid = {38606614},
issn = {1530-6992},
abstract = {Building on current explorations in chronic optical neural interfaces, it is essential to address the risk of photothermal damage in traditional optogenetics. By focusing on calcium fluorescence for imaging rather than stimulation, injectable fluorescent neural interfaces significantly minimize photothermal damage and improve the accuracy of neuronal imaging. Key advancements including the use of injectable microelectronics for targeted electrical stimulation and their integration with cell-specific genetically encoded calcium indicators have been discussed. These injectable electronics that allow for post-treatment retrieval offer a minimally invasive solution, enhancing both usability and reliability. Furthermore, the integration of genetically encoded fluorescent calcium indicators with injectable bioelectronics enables precise neuronal recording and imaging of individual neurons. This shift not only minimizes risks such as photothermal conversion but also boosts safety, specificity, and effectiveness of neural imaging. Embracing these advancements represents a significant leap forward in biomedical engineering and neuroscience, paving the way for advanced brain-machine interfaces.},
}

@article {pmid38606309,
year = {2024},
author = {Jeong, CH and Lim, H and Lee, J and Lee, HS and Ku, J and Kang, YJ},
title = {Attentional state-synchronous peripheral electrical stimulation during action observation induced distinct modulation of corticospinal plasticity after stroke.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1373589},
pmid = {38606309},
issn = {1662-4548},
abstract = {INTRODUCTION: Brain computer interface-based action observation (BCI-AO) is a promising technique in detecting the user's cortical state of visual attention and providing feedback to assist rehabilitation. Peripheral nerve electrical stimulation (PES) is a conventional method used to enhance outcomes in upper extremity function by increasing activation in the motor cortex. In this study, we examined the effects of different pairings of peripheral nerve electrical stimulation (PES) during BCI-AO tasks and their impact on corticospinal plasticity.

MATERIALS AND METHODS: Our innovative BCI-AO interventions decoded user's attentive watching during task completion. This process involved providing rewarding visual cues while simultaneously activating afferent pathways through PES. Fifteen stroke patients were included in the analysis. All patients underwent a 15 min BCI-AO program under four different experimental conditions: BCI-AO without PES, BCI-AO with continuous PES, BCI-AO with triggered PES, and BCI-AO with reverse PES application. PES was applied at the ulnar nerve of the wrist at an intensity equivalent to 120% of the sensory threshold and a frequency of 50 Hz. The experiment was conducted randomly at least 3 days apart. To assess corticospinal and peripheral nerve excitability, we compared pre and post-task (post 0, post 20 min) parameters of motor evoked potential and F waves under the four conditions in the muscle of the affected hand.

RESULTS: The findings indicated that corticospinal excitability in the affected hemisphere was higher when PES was synchronously applied with AO training, using BCI during a state of attentive watching. In contrast, there was no effect on corticospinal activation when PES was applied continuously or in the reverse manner. This paradigm promoted corticospinal plasticity for up to 20 min after task completion. Importantly, the effect was more evident in patients over 65 years of age.

CONCLUSION: The results showed that task-driven corticospinal plasticity was higher when PES was applied synchronously with a highly attentive brain state during the action observation task, compared to continuous or asynchronous application. This study provides insight into how optimized BCI technologies dependent on brain state used in conjunction with other rehabilitation training could enhance treatment-induced neural plasticity.},
}

@article {pmid38606308,
year = {2024},
author = {Xue, Q and Song, Y and Wu, H and Cheng, Y and Pan, H},
title = {Graph neural network based on brain inspired forward-forward mechanism for motor imagery classification in brain-computer interfaces.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1309594},
pmid = {38606308},
issn = {1662-4548},
abstract = {INTRODUCTION: Within the development of brain-computer interface (BCI) systems, it is crucial to consider the impact of brain network dynamics and neural signal transmission mechanisms on electroencephalogram-based motor imagery (MI-EEG) tasks. However, conventional deep learning (DL) methods cannot reflect the topological relationship among electrodes, thereby hindering the effective decoding of brain activity.

METHODS: Inspired by the concept of brain neuronal forward-forward (F-F) mechanism, a novel DL framework based on Graph Neural Network combined forward-forward mechanism (F-FGCN) is presented. F-FGCN framework aims to enhance EEG signal decoding performance by applying functional topological relationships and signal propagation mechanism. The fusion process involves converting the multi-channel EEG into a sequence of signals and constructing a network grounded on the Pearson correlation coeffcient, effectively representing the associations between channels. Our model initially pre-trains the Graph Convolutional Network (GCN), and fine-tunes the output layer to obtain the feature vector. Moreover, the F-F model is used for advanced feature extraction and classification.

RESULTS AND DISCUSSION: Achievement of F-FGCN is assessed on the PhysioNet dataset for a four-class categorization, compared with various classical and state-of-the-art models. The learned features of the F-FGCN substantially amplify the performance of downstream classifiers, achieving the highest accuracy of 96.11% and 82.37% at the subject and group levels, respectively. Experimental results affirm the potency of FFGCN in enhancing EEG decoding performance, thus paving the way for BCI applications.},
}

@article {pmid38604523,
year = {2024},
author = {Liu, L and Li, J and Ouyang, R and Zhou, D and Fan, C and Liang, W and Li, F and Lv, Z and Wu, X},
title = {Multimodal brain-controlled system for rehabilitation training: Combining asynchronous online brain-computer interface and exoskeleton.},
journal = {Journal of neuroscience methods},
volume = {406},
number = {},
pages = {110132},
doi = {10.1016/j.jneumeth.2024.110132},
pmid = {38604523},
issn = {1872-678X},
abstract = {BACKGROUND: Traditional therapist-based rehabilitation training for patients with movement impairment is laborious and expensive. In order to reduce the cost and improve the treatment effect of rehabilitation, many methods based on human-computer interaction (HCI) technology have been proposed, such as robot-assisted therapy and functional electrical stimulation (FES). However, due to the lack of active participation of brain, these methods have limited effects on the promotion of damaged nerve remodeling.

NEW METHOD: Based on the neurofeedback training provided by the combination of brain-computer interface (BCI) and exoskeleton, this paper proposes a multimodal brain-controlled active rehabilitation system to help improve limb function. The joint control mode of steady-state visual evoked potential (SSVEP) and motor imagery (MI) is adopted to achieve self-paced control and thus maximize the degree of brain involvement, and a requirement selection function based on SSVEP design is added to facilitate communication with aphasia patients.

In addition, the Transformer is introduced as the MI decoder in the asynchronous online BCI to improve the global perception of electroencephalogram (EEG) signals and maintain the sensitivity and efficiency of the system.

RESULTS: In two multi-task online experiments for left hand, right hand, foot and idle states, subject achieves 91.25% and 92.50% best accuracy, respectively.

CONCLUSION: Compared with previous studies, this paper aims to establish a high-performance and low-latency brain-controlled rehabilitation system, and provide an independent and autonomous control mode of the brain, so as to improve the effect of neural remodeling. The performance of the proposed method is evaluated through offline and online experiments.},
}

@article {pmid38603901,
year = {2024},
author = {Shi, X and She, Q and Fang, F and Meng, M and Tan, T and Zhang, Y},
title = {Enhancing cross-subject EEG emotion recognition through multi-source manifold metric transfer learning.},
journal = {Computers in biology and medicine},
volume = {174},
number = {},
pages = {108445},
doi = {10.1016/j.compbiomed.2024.108445},
pmid = {38603901},
issn = {1879-0534},
abstract = {Transfer learning (TL) has demonstrated its efficacy in addressing the cross-subject domain adaptation challenges in affective brain-computer interfaces (aBCI). However, previous TL methods usually use a stationary distance, such as Euclidean distance, to quantify the distribution dissimilarity between two domains, overlooking the inherent links among similar samples, potentially leading to suboptimal feature mapping. In this study, we introduced a novel algorithm called multi-source manifold metric transfer learning (MSMMTL) to enhance the efficacy of conventional TL. Specifically, we first selected the source domain based on Mahalanobis distance to enhance the quality of the source domains and then used manifold feature mapping approach to map the source and target domains on the Grassmann manifold to mitigate data drift between domains. In this newly established shared space, we optimized the Mahalanobis metric by maximizing the inter-class distances while minimizing the intra-class distances in the target domain. Recognizing that significant distribution discrepancies might persist across different domains even on the manifold, to ensure similar distributions between the source and target domains, we further imposed constraints on both domains under the Mahalanobis metric. This approach aims to reduce distributional disparities and enhance the electroencephalogram (EEG) emotion recognition performance. In cross-subject experiments, the MSMMTL model exhibits average classification accuracies of 88.83 % and 65.04 % for SEED and DEAP, respectively, underscoring the superiority of our proposed MSMMTL over other state-of-the-art methods. MSMMTL can effectively solve the problem of individual differences in EEG-based affective computing.},
}

@article {pmid38603841,
year = {2024},
author = {De Rubis, G and Paudel, KR and Yeung, S and Mohamad, S and Sudhakar, S and Singh, SK and Gupta, G and Hansbro, PM and Chellappan, DK and Oliver, BGG and Dua, K},
title = {18-β-glycyrrhetinic acid-loaded polymeric nanoparticles attenuate cigarette smoke-induced markers of impaired antiviral response in vitro.},
journal = {Pathology, research and practice},
volume = {257},
number = {},
pages = {155295},
doi = {10.1016/j.prp.2024.155295},
pmid = {38603841},
issn = {1618-0631},
abstract = {Tobacco smoking is a leading cause of preventable mortality, and it is the major contributor to diseases such as COPD and lung cancer. Cigarette smoke compromises the pulmonary antiviral immune response, increasing susceptibility to viral infections. There is currently no therapy that specifically addresses the problem of impaired antiviral response in cigarette smokers and COPD patients, highlighting the necessity to develop novel treatment strategies. 18-β-glycyrrhetinic acid (18-β-gly) is a phytoceutical derived from licorice with promising anti-inflammatory, antioxidant, and antiviral activities whose clinical application is hampered by poor solubility. This study explores the therapeutic potential of an advanced drug delivery system encapsulating 18-β-gly in poly lactic-co-glycolic acid (PLGA) nanoparticles in addressing the impaired antiviral immunity observed in smokers and COPD patients. Exposure of BCi-NS1.1 human bronchial epithelial cells to cigarette smoke extract (CSE) resulted in reduced expression of critical antiviral chemokines (IP-10, I-TAC, MIP-1α/1β), mimicking what happens in smokers and COPD patients. Treatment with 18-β-gly-PLGA nanoparticles partially restored the expression of these chemokines, demonstrating promising therapeutic impact. The nanoparticles increased IP-10, I-TAC, and MIP-1α/1β levels, exhibiting potential in attenuating the negative effects of cigarette smoke on the antiviral response. This study provides a novel approach to address the impaired antiviral immune response in vulnerable populations, offering a foundation for further investigations and potential therapeutic interventions. Further studies, including a comprehensive in vitro characterization and in vivo testing, are warranted to validate the therapeutic efficacy of 18-β-gly-PLGA nanoparticles in respiratory disorders associated with compromised antiviral immunity.},
}

@article {pmid38602850,
year = {2024},
author = {Wang, Z and Hu, H and Zhou, T and Xu, T and Zhao, X},
title = {Average Time Consumption Per Character - a Practical Performance Metric for Generic Synchronous BCI Spellers.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2024.3387469},
pmid = {38602850},
issn = {1558-2531},
abstract = {OBJECTIVE: The information transfer rate (ITR) is widely accepted as a performance metric for generic brain-computer interface (BCI) spellers, while it is noticeable that the communication speed given by ITR is actually an upper bound which however can never be reached in real systems. A new performance metric is therefore needed.

METHODS: In this paper, a new metric named average time consumption per character (ATCPC) is proposed. It quantifies how long it takes on average to type one character using a typical synchronous BCI speller. To analytically derive ATCPC, the real typing process is modelled with a random walk on a graph. Misclassification and backspace are carefully characterized. A close-form formula of ATCPC is obtained through computing the hitting time of the random walk. The new metric is validated through simulated typing experiments and compared with ITR.

RESULTS: Firstly, the formula and simulation show a good consistency. Secondly, ITR always tends to overestimate the communication speed, while ATCPC is more realistic.

CONCLUSION: The proposed ATCPC metric is valid.

SIGNIFICANCE: ATCPC is a qualified substitute for ITR. ATCPC also reveals the great potential of keyboard optimization to further enhance the performance of BCI spellers, which was hardly investigated before.},
}

@article {pmid38602573,
year = {2024},
author = {Waisberg, E and Ong, J and Lee, AG},
title = {Ethical Considerations of Neuralink and Brain-Computer Interfaces: Balancing Innovation and Responsibility.},
journal = {Annals of biomedical engineering},
volume = {},
number = {},
pages = {},
pmid = {38602573},
issn = {1573-9686},
abstract = {Neuralink is a neurotechnology company founded by Elon Musk in 2016, which has been quietly developing revolutionary technology allowing for ultra-high precision bidirectional communication between external devices and the brain. In this paper, we explore the multifaceted ethical considerations surrounding neural interfaces, analyzing potential societal impacts, risks, and call for a need for responsible innovation. Despite the technological, medical, medicolegal, and ethical challenges ahead, neural interface technology remains extremely promising and has the potential to create a new era of medicine.},
}

@article {pmid38601924,
year = {2024},
author = {Anger, JT and Case, LK and Baranowski, AP and Berger, A and Craft, RM and Damitz, LA and Gabriel, R and Harrison, T and Kaptein, K and Lee, S and Murphy, AZ and Said, E and Smith, SA and Thomas, DA and Valdés Hernández, MDC and Trasvina, V and Wesselmann, U and Yaksh, TL},
title = {Pain mechanisms in the transgender individual: a review.},
journal = {Frontiers in pain research (Lausanne, Switzerland)},
volume = {5},
number = {},
pages = {1241015},
pmid = {38601924},
issn = {2673-561X},
abstract = {SPECIFIC AIM: Provide an overview of the literature addressing major areas pertinent to pain in transgender persons and to identify areas of primary relevance for future research.

METHODS: A team of scholars that have previously published on different areas of related research met periodically though zoom conferencing between April 2021 and February 2023 to discuss relevant literature with the goal of providing an overview on the incidence, phenotype, and mechanisms of pain in transgender patients. Review sections were written after gathering information from systematic literature searches of published or publicly available electronic literature to be compiled for publication as part of a topical series on gender and pain in the Frontiers in Pain Research.

RESULTS: While transgender individuals represent a significant and increasingly visible component of the population, many researchers and clinicians are not well informed about the diversity in gender identity, physiology, hormonal status, and gender-affirming medical procedures utilized by transgender and other gender diverse patients. Transgender and cisgender people present with many of the same medical concerns, but research and treatment of these medical needs must reflect an appreciation of how differences in sex, gender, gender-affirming medical procedures, and minoritized status impact pain.

CONCLUSIONS: While significant advances have occurred in our appreciation of pain, the review indicates the need to support more targeted research on treatment and prevention of pain in transgender individuals. This is particularly relevant both for gender-affirming medical interventions and related medical care. Of particular importance is the need for large long-term follow-up studies to ascertain best practices for such procedures. A multi-disciplinary approach with personalized interventions is of particular importance to move forward.},
}

@article {pmid38601801,
year = {2024},
author = {Li, H and Li, H and Ma, L and Polina, D},
title = {Revealing brain's cognitive process deeply: a study of the consistent EEG patterns of audio-visual perceptual holistic.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1377233},
pmid = {38601801},
issn = {1662-5161},
abstract = {INTRODUCTION: To investigate the brain's cognitive process and perceptual holistic, we have developed a novel method that focuses on the informational attributes of stimuli.

METHODS: We recorded EEG signals during visual and auditory perceptual cognition experiments and conducted ERP analyses to observe specific positive and negative components occurring after 400ms during both visual and auditory perceptual processes. These ERP components represent the brain's perceptual holistic processing activities, which we have named Information-Related Potentials (IRPs). We combined IRPs with machine learning methods to decode cognitive processes in the brain.

RESULTS: Our experimental results indicate that IRPs can better characterize information processing, particularly perceptual holism. Additionally, we conducted a brain network analysis and found that visual and auditory perceptual holistic processing share consistent neural pathways.

DISCUSSION: Our efforts not only demonstrate the specificity, significance, and reliability of IRPs but also reveal their great potential for future brain mechanism research and BCI applications.},
}

@article {pmid38601800,
year = {2024},
author = {Chen, Y and Wang, F and Li, T and Zhao, L and Gong, A and Nan, W and Ding, P and Fu, Y},
title = {Several inaccurate or erroneous conceptions and misleading propaganda about brain-computer interfaces.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1391550},
pmid = {38601800},
issn = {1662-5161},
abstract = {Brain-computer interface (BCI) is a revolutionizing human-computer interaction, which has potential applications for specific individuals or groups in specific scenarios. Extensive research has been conducted on the principles and implementation methods of BCI, and efforts are currently being made to bridge the gap from research to real-world applications. However, there are inaccurate or erroneous conceptions about BCI among some members of the public, and certain media outlets, as well as some BCI researchers, developers, manufacturers, and regulators, propagate misleading or overhyped claims about BCI technology. Therefore, this article summarizes the several misconceptions and misleading propaganda about BCI, including BCI being capable of "mind-controlled," "controlling brain," "mind reading," and the ability to "download" or "upload" information from or to the brain using BCI, among others. Finally, the limitations (shortcomings) and limits (boundaries) of BCI, as well as the necessity of conducting research aimed at countering BCI systems are discussed, and several suggestions are offered to reduce misconceptions and misleading claims about BCI.},
}

@article {pmid38598676,
year = {2024},
author = {Gancio, J and Masoller, C and Tirabassi, G},
title = {Permutation entropy analysis of EEG signals for distinguishing eyes-open and eyes-closed brain states: Comparison of different approaches.},
journal = {Chaos (Woodbury, N.Y.)},
volume = {34},
number = {4},
pages = {},
doi = {10.1063/5.0200029},
pmid = {38598676},
issn = {1089-7682},
mesh = {Humans ; Entropy ; *Brain ; *Electroencephalography/methods ; Brain Mapping/methods ; Signal Processing, Computer-Assisted ; },
abstract = {Developing reliable methodologies to decode brain state information from electroencephalogram (EEG) signals is an open challenge, crucial to implementing EEG-based brain-computer interfaces (BCIs). For example, signal processing methods that identify brain states could allow motor-impaired patients to communicate via non-invasive, EEG-based BCIs. In this work, we focus on the problem of distinguishing between the states of eyes closed (EC) and eyes open (EO), employing quantities based on permutation entropy (PE). An advantage of PE analysis is that it uses symbols (ordinal patterns) defined by the ordering of the data points (disregarding the actual values), hence providing robustness to noise and outliers due to motion artifacts. However, we show that for the analysis of multichannel EEG recordings, the performance of PE in discriminating the EO and EC states depends on the symbols' definition and how their probabilities are estimated. Here, we study the performance of PE-based features for EC/EO state classification in a dataset of N=107 subjects with one-minute 64-channel EEG recordings in each state. We analyze features obtained from patterns encoding temporal or spatial information, and we compare different approaches to estimate their probabilities (by averaging over time, over channels, or by "pooling"). We find that some PE-based features provide about 75% classification accuracy, comparable to the performance of features extracted with other statistical analysis techniques. Our work highlights the limitations of PE methods in distinguishing the eyes' state, but, at the same time, it points to the possibility that subject-specific training could overcome these limitations.},
}

Humans
Entropy
*Brain
*Electroencephalography/methods
Brain Mapping/methods
Signal Processing, Computer-Assisted

@article {pmid38598403,
year = {2024},
author = {Li, D and Wang, X and Dou, M and Zhao, Y and Cui, X and Xiang, J and Wang, B},
title = {Multi-stimulus Least-squares Transformation with Online Adaptation Scheme to Reduce Calibration Effort for SSVEP-based BCIs.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3387283},
pmid = {38598403},
issn = {1558-0210},
abstract = {UNLABELLED: Steady-state visual evoked potential (SSVEP), one of the most popular electroencephalography (EEG)-based brain-computer interface (BCI) paradigms, can achieve high performance using calibration-based recognition algorithms. As calibration-based recognition algorithms are time-consuming to collect calibration data, the least-squares transformation (LST) has been used to reduce the calibration effort for SSVEP-based BCI. However, the transformation matrices constructed by current LST methods are not precise enough, resulting in large differences between the transformed data and the real data of the target subject. This ultimately leads to the constructed spatial filters and reference templates not being effective enough. To address these issues, this paper proposes multi-stimulus LST with online adaptation scheme (ms-LST-OA).

METHODS: The proposed ms-LST-OA consists of two parts. Firstly, to improve the precision of the transformation matrices, we propose the multi-stimulus LST (ms-LST) using cross-stimulus learning scheme as the cross-subject data transformation method. The ms-LST uses the data from neighboring stimuli to construct a higher precision transformation matrix for each stimulus to reduce the differences between transformed data and real data. Secondly, to further optimize the constructed spatial filters and reference templates, we use an online adaptation scheme to learn more features of the EEG signals of the target subject through an iterative process trial-by-trial.

RESULTS: ms-LST-OA performance was measured for three datasets (Benchmark Dataset, BETA Dataset, and UCSD Dataset). Using few calibration data, the ITR of ms-LST-OA achieved 210.01±10.10 bits/min, 172.31±7.26 bits/min, and 139.04±14.90 bits/min for all three datasets, respectively.

CONCLUSION: Using ms-LST-OA can reduce calibration effort for SSVEP-based BCIs.},
}

@article {pmid38598402,
year = {2024},
author = {Qin, K and Xu, R and Li, S and Wang, X and Cichocki, A and Jin, J},
title = {A Time Local Weighted Transformation Recognition Framework for Steady State Visual Evoked Potentials based Brain Computer Interfaces.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3386763},
pmid = {38598402},
issn = {1558-0210},
abstract = {Canonical correlation analysis (CCA), Multivariate synchronization index (MSI), and their extended methods have been widely used for target recognition in Brain-computer interfaces (BCIs) based on Steady State Visual Evoked Potentials (SSVEP), and covariance calculation is an important process for these algorithms. Some studies have proved that embedding time-local information into the covariance can optimize the recognition effect of the above algorithms. However, the optimization effect can only be observed from the recognition results and the improvement principle of time-local information cannot be explained. Therefore, we propose a time-local weighted transformation (TT) recognition framework that directly embeds the time-local information into the electroencephalography signal through weighted transformation. The influence mechanism of time-local information on the SSVEP signal can then be observed in the frequency domain. Low-frequency noise is suppressed on the premise of sacrificing part of the SSVEP fundamental frequency energy, the harmonic energy of SSVEP is enhanced at the cost of introducing a small amount of high-frequency noise. The experimental results show that the TT recognition framework can significantly improve the recognition ability of the algorithms and the separability of extracted features. Its enhancement effect is significantly better than the traditional time-local covariance extraction method, which has enormous application potential.},
}

@article {pmid38593021,
year = {2024},
author = {Niu, X and Lu, N and Yan, R and Luo, H},
title = {Model and Data Dual-Driven Double-Point Observation Network for Ultra-Short MI EEG Classification.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2024.3386565},
pmid = {38593021},
issn = {2168-2208},
abstract = {Although deep networks have succeeded in various signal classification tasks, the time sequence samples used to train the deep models are usually required to reach a certain length. Especially, in brain computer interface (BCI) research, around 3.5s-long motor imagery (MI) Electroencephalography (EEG) samples are needed to obtain satisfactory classification performance. This time-span requirement of the training samples makes real-time MI BCI systems impossible to implement based on deep networks, which restricts the related researches within laboratory and makes practical application hard to accomplish. To address this issue, a double-point observation deep network (DoNet) is developed to classify ultra-short samples buried in noise. First, an analytical solution is developed theoretically to perform ultra-short signal classification based on double-point couples. Then, a signal-noise model is constructed to study the interference of noise on classification based on double-point couples. Based on which, an independent identical distribution condition is utilized to improve the classification accuracy in a data-driven manner. Combining the theoretical model and data-driven mechanism, DoNet can construct a steady data-distribution for the double-point couples of the samples with the same label. Therefore, the conditional probability of each double-point couple of a test sample can be obtained. With a voting strategy, the samples can be accurately classified by fusing these conditional probabilities. Meanwhile, the noise interference can be suppressed. DoNet has been evaluated on two public EEG datasets. Compared to most state-of-the-art methods, the 1s-long EEG signal classification accuracy has been improved by more than 3%.},
}

@article {pmid38592090,
year = {2024},
author = {Ille, N},
title = {Orthogonal extended infomax algorithm.},
journal = {Journal of neural engineering},
volume = {21},
number = {2},
pages = {},
doi = {10.1088/1741-2552/ad38db},
pmid = {38592090},
issn = {1741-2552},
mesh = {*Algorithms ; *Brain-Computer Interfaces ; Electroencephalography ; Learning ; Normal Distribution ; },
abstract = {Objective.The extended infomax algorithm for independent component analysis (ICA) can separate sub- and super-Gaussian signals but converges slowly as it uses stochastic gradient optimization. In this paper, an improved extended infomax algorithm is presented that converges much faster.Approach.Accelerated convergence is achieved by replacing the natural gradient learning rule of extended infomax by a fully-multiplicative orthogonal-group based update scheme of the ICA unmixing matrix, leading to an orthogonal extended infomax algorithm (OgExtInf). The computational performance of OgExtInf was compared with original extended infomax and with two fast ICA algorithms: the popular FastICA and Picard, a preconditioned limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm belonging to the family of quasi-Newton methods.Main results.OgExtInf converges much faster than original extended infomax. For small-size electroencephalogram (EEG) data segments, as used for example in online EEG processing, OgExtInf is also faster than FastICA and Picard.Significance.OgExtInf may be useful for fast and reliable ICA, e.g. in online systems for epileptic spike and seizure detection or brain-computer interfaces.},
}

*Algorithms
*Brain-Computer Interfaces
Electroencephalography
Learning
Normal Distribution

@article {pmid38590363,
year = {2024},
author = {Osuna-Orozco, R and Zhao, Y and Stealey, HM and Lu, HY and Contreras-Hernandez, E and Santacruz, SR},
title = {Adaptation and learning as strategies to maximize reward in neurofeedback tasks.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1368115},
pmid = {38590363},
issn = {1662-5161},
abstract = {INTRODUCTION: Adaptation and learning have been observed to contribute to the acquisition of new motor skills and are used as strategies to cope with changing environments. However, it is hard to determine the relative contribution of each when executing goal directed motor tasks. This study explores the dynamics of neural activity during a center-out reaching task with continuous visual feedback under the influence of rotational perturbations.

METHODS: Results for a brain-computer interface (BCI) task performed by two non-human primate (NHP) subjects are compared to simulations from a reinforcement learning agent performing an analogous task. We characterized baseline activity and compared it to the activity after rotational perturbations of different magnitudes were introduced. We employed principal component analysis (PCA) to analyze the spiking activity driving the cursor in the NHP BCI task as well as the activation of the neural network of the reinforcement learning agent.

RESULTS AND DISCUSSION: Our analyses reveal that both for the NHPs and the reinforcement learning agent, the task-relevant neural manifold is isomorphic with the task. However, for the NHPs the manifold is largely preserved for all rotational perturbations explored and adaptation of neural activity occurs within this manifold as rotations are compensated by reassignment of regions of the neural space in an angular pattern that cancels said rotations. In contrast, retraining the reinforcement learning agent to reach the targets after rotation results in substantial modifications of the underlying neural manifold. Our findings demonstrate that NHPs adapt their existing neural dynamic repertoire in a quantitatively precise manner to account for perturbations of different magnitudes and they do so in a way that obviates the need for extensive learning.},
}

@article {pmid38589229,
year = {2024},
author = {Falaki, A and Quessy, S and Dancause, N},
title = {Differential modulation of local field potentials in the primary and premotor cortices during ipsilateral and contralateral reach to grasp in macaque monkeys.},
journal = {The Journal of neuroscience : the official journal of the Society for Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1523/JNEUROSCI.1161-23.2024},
pmid = {38589229},
issn = {1529-2401},
abstract = {Hand movements are associated with modulations of neuronal activity across several interconnected cortical areas, including the primary motor cortex (M1), and the dorsal and ventral premotor cortices (PMd and PMv). Local field potentials (LFPs) provide a link between neuronal discharges and synaptic inputs. Our current understanding of how LFPs vary in M1, PMd, and PMv during contralateral and ipsilateral movements is incomplete. To help reveal unique features in the pattern of modulations, we simultaneously recorded LFPs in these areas in two macaque monkeys performing reach and grasp movements with either the right or left hand. The greatest effector-dependent differences were seen in M1, at low (≤ 13 Hz) and gamma frequencies. In premotor areas, differences related to hand use were only present in low frequencies. PMv exhibited the greatest increase in low frequencies during instruction cues and the smallest effector-dependent modulation during movement execution. In PMd, delta oscillations were greater during contralateral reach and grasp, and beta activity increased during contralateral grasp. In contrast, beta oscillations decreased in M1 and PMv. These results suggest that while M1 primarily exhibits effector-specific LFP activity, premotor areas compute more effector-independent aspects of the task requirements, particularly during movement preparation for PMv and production for PMd. The generation of precise hand movements likely relies on the combination of complementary information contained in the unique pattern of neural modulations contained in each cortical area. Accordingly, integrating LFPs from premotor areas and M1 could enhance the performance and robustness of brain-machine interfaces.Significance Statement We compared local field potentials (LFPs) from the primary motor cortex (M1), the dorsal and ventral premotor cortices (PMd and PMv) while monkeys performed reach and grasp with the contralateral or ipsilateral hand. In general, hand-related differences were greater in M1 than in premotor areas. During both contralateral and ipsilateral trials, LFPs were more similar when comparing the two premotor areas than comparing M1 to PMd or PMv. However, the pattern of modulations in each area had unique features. The combination of these signals is likely essential to support the flexibility and complexity of unilateral hand movements. Our results help to understand the neural substrate that allows cortical areas to concurrently contribute to different aspects of movement planning and production.},
}

@article {pmid38587944,
year = {2024},
author = {Carrara, I and Papadopoulo, T},
title = {Classification of BCI-EEG Based on the Augmented Covariance Matrix.},
journal = {IEEE transactions on bio-medical engineering},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TBME.2024.3386219},
pmid = {38587944},
issn = {1558-2531},
abstract = {OBJECTIVE: Electroencephalography signals are recorded as multidimensional datasets. We propose a new framework based on the augmented covariance that stems from an autoregressive model to improve motor imagery classification.

METHODS: From the autoregressive model can be derived the Yule-Walker equations, which show the emergence of a symmetric positive definite matrix: the augmented covariance matrix. The state-of the art for classifying covariance matrices is based on Riemannian Geometry. A fairly natural idea is therefore to apply this Riemannian Geometry based approach to these augmented covariance matrices. The methodology for creating the augmented covariance matrix shows a natural connection with the delay embedding theorem proposed by Takens for dynamical systems. Such an embedding method is based on the knowledge of two parameters: the delay and the embedding dimension, respectively related to the lag and the order of the autoregressive model. This approach provides new methods to compute the hyper-parameters in addition to standard grid search.

RESULTS: The augmented covariance matrix performed ACMs better than any state-of-the-art methods. We will test our approach on several datasets and several subjects using the MOABB framework, using both within-session and cross-session evaluation.

CONCLUSION: The improvement in results is due to the fact that the augmented covariance matrix incorporates not only spatial but also temporal information. As such, it contains information on the nonlinear components of the signal through the embedding procedure, which allows the leveraging of dynamical systems algorithms.

SIGNIFICANCE: These results extend the concepts and the results of the Riemannian distance based classification algorithm.},
}

@article {pmid38586334,
year = {2024},
author = {Curà, F and Sesana, R and Corsaro, L and Dugand, MM},
title = {An Active Thermography approach for materials characterisation of thermal management systems for Lithium-ion batteries.},
journal = {Heliyon},
volume = {10},
number = {7},
pages = {e28587},
pmid = {38586334},
issn = {2405-8440},
abstract = {The aim of this work is an alternative non destructive technique for estimating the thermal properties of four different Thermal Management System (TMS) materials. More in detail, a thermographic setup realized with the Active Thermography approach (AT) is utilized for the purpose and the data elaboration follows the ISO 18755 Standard. As well known, Phase Changes Materials (PCMs) represent an innovative solution in the Thermal Management System (TMS) of Lithium-Ion batteries and, during the years, many solutions were developed to improve its thermal properties. As a matter of fact, parameters such as the internal temperature or heat exchanges impact on both efficiency and safety of the whole battery system. Consequently, the thermal conductivity was often chosen as a performance indicator of Thermal Management System (TMS) materials. In this work, both thermal diffusivity and thermal conductivity were estimated in two different testing conditions, respectively at room temperature and higher temperature conditions. The Active Thermography (AT) technique proposed in this activity has satisfactory estimated both thermal diffusivity and thermal conductivity of Thermal Management System (TMS) materials. An analytical model was also developed to reproduce the temperature experimental profiles. Finally, results obtained with AT approach were compared to those available from commercial datasheet and literature.},
}

@article {pmid38586195,
year = {2024},
author = {Ma, P and Dong, C and Lin, R and Liu, H and Lei, D and Chen, X and Liu, H},
title = {A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1306283},
pmid = {38586195},
issn = {1662-4548},
abstract = {BACKGROUND: The development of Brain-Computer Interface (BCI) technology has brought tremendous potential to various fields. In recent years, prominent research has focused on enhancing the accuracy of BCI decoding algorithms by effectively utilizing meaningful features extracted from electroencephalographic (EEG) signals.

OBJECTIVE: This paper proposes a method for extracting brain functional network features based on directed transfer function (DTF) and graph theory. The method incorporates the extracted brain network features with common spatial pattern (CSP) to enhance the performance of motor imagery (MI) classification task.

METHODS: The signals from each electrode of the EEG, utilizing a total of 32 channels, are used as input signals for the network nodes. In this study, 26 healthy participants were recruited to provide EEG data. The brain functional network is constructed in Alpha and Beta bands using the DTF method. The node degree (ND), clustering coefficient (CC), and global efficiency (GE) of the brain functional network are obtained using graph theory. The DTF network features and graph theory are combined with the traditional signal processing method, the CSP algorithm. The redundant network features are filtered out using the Lasso method, and finally, the fused features are classified using a support vector machine (SVM), culminating in a novel approach we have termed CDGL.

RESULTS: For Beta frequency band, with 8 electrodes, the proposed CDGL method achieved an accuracy of 89.13%, a sensitivity of 90.15%, and a specificity of 88.10%, which are 14.10, 16.69, and 11.50% percentage higher than the traditional CSP method (75.03, 73.46, and 76.60%), respectively. Furthermore, the results obtained with 8 channels were superior to those with 4 channels (82.31, 83.35, and 81.74%), and the result for the Beta frequency band were better than those for the Alpha frequency band (87.42, 87.48, and 87.36%). Similar results were also obtained on two public datasets, where the CDGL algorithm's performance was found to be optimal.

CONCLUSION: The feature fusion of DTF network and graph theory features enhanced CSP algorithm's performance in MI task classification. Increasing the number of channels allows for more EEG signal feature information, enhancing the model's sensitivity and discriminative ability toward specific activities in brain regions. It should be noted that the functional brain network features in the Beta band exhibit superior performance improvement for the algorithm compared to those in the Alpha band.},
}

@article {pmid38586146,
year = {2024},
author = {Shuqfa, Z and Lakas, A and Belkacem, AN},
title = {Increasing accessibility to a large brain-computer interface dataset: Curation of physionet EEG motor movement/imagery dataset for decoding and classification.},
journal = {Data in brief},
volume = {54},
number = {},
pages = {110181},
pmid = {38586146},
issn = {2352-3409},
abstract = {A reliable motor imagery (MI) brain-computer interface (BCI) requires accurate decoding, which in turn requires model calibration using electroencephalography (EEG) signals from subjects executing or imagining the execution of movements. Although the PhysioNet EEG Motor Movement/Imagery Dataset is currently the largest EEG dataset in the literature, relatively few studies have used it to decode MI trials. In the present study, we curated and cleaned this dataset to store it in an accessible format that is convenient for quick exploitation, decoding, and classification using recent integrated development environments. We dropped six subjects owing to anomalies in EEG recordings and pre-possessed the rest, resulting in 103 subjects spanning four MI and four motor execution tasks. The annotations were coded to correspond to different tasks using numerical values. The resulting dataset is stored in both MATLAB structure and CSV files to ensure ease of access and organization. We believe that improving the accessibility of this dataset will help EEG-based MI-BCI decoding and classification, enabling more reliable real-life applications. The convenience and ease of access of this dataset may therefore lead to improvements in cross-subject classification and transfer learning.},
}

@article {pmid38586082,
year = {2024},
author = {Mueller, NN and Kim, Y and Ocoko, MYM and Dernelle, P and Kale, I and Patwa, S and Hermoso, AC and Chirra, D and Capadona, JR and Hess-Dunning, A},
title = {Effects of Micromachining on Anti-oxidant Elution from a Mechanically-Adaptive Polymer.},
journal = {Journal of micromechanics and microengineering : structures, devices, and systems},
volume = {34},
number = {3},
pages = {},
pmid = {38586082},
issn = {0960-1317},
support = {I01 RX003083/RX/RRD VA/United States ; },
abstract = {Intracortical microelectrodes (IMEs) can be used to restore motor and sensory function as a part of brain-computer interfaces in individuals with neuromusculoskeletal disorders. However, the neuroinflammatory response to IMEs can result in their premature failure, leading to reduced therapeutic efficacy. Mechanically-adaptive, resveratrol-eluting (MARE) neural probes target two mechanisms believed to contribute to the neuroinflammatory response by reducing the mechanical mismatch between the brain tissue and device, as well as locally delivering an antioxidant therapeutic. To create the mechanically-adaptive substrate, a dispersion, casting, and evaporation method is used, followed by a microfabrication process to integrate functional recording electrodes on the material. Resveratrol release experiments were completed to generate a resveratrol release profile and demonstrated that the MARE probes are capable of long-term controlled release. Additionally, our results showed that resveratrol can be degraded by laser-micromachining, an important consideration for future device fabrication. Finally, the electrodes were shown to have a suitable impedance for single-unit neural recording and could record single units in vivo.},
}

@article {pmid38585364,
year = {2024},
author = {Ben Pazi, H and Jahashan, S and Har Nof, S and Zibman, S and Yanai-Kohelet, O and Prigan, L and Intrator, N and Bornstein, NM and Ribo, M},
title = {Pre-hospital stroke monitoring past, present, and future: a perspective.},
journal = {Frontiers in neurology},
volume = {15},
number = {},
pages = {1341170},
pmid = {38585364},
issn = {1664-2295},
abstract = {Integrated brain-machine interface signifies a transformative advancement in neurological monitoring and intervention modalities for events such as stroke, the leading cause of disability. Historically, stroke management relied on clinical evaluation and imaging. While today's stroke landscape integrates artificial intelligence for proactive clinical decision-making, mainly in imaging and stroke detection, it depends on clinical observation for early detection. Cardiovascular monitoring and detection systems, which have become standard throughout healthcare and wellness settings, provide a model for future cerebrovascular monitoring and detection. This commentary reviews the progression of continuous stroke monitoring, spotlighting contemporary innovations and prospective avenues, and emphasizes the influential roles of cutting-edge technologies in shaping stroke care.},
}

@article {pmid38585226,
year = {2023},
author = {Yuvaraj, M and Raja, P and David, A and Burdet, E and Skm, V and Balasubramanian, S},
title = {A systematic investigation of detectors for low signal-to-noise ratio EMG signals.},
journal = {F1000Research},
volume = {12},
number = {},
pages = {429},
pmid = {38585226},
issn = {2046-1402},
mesh = {Humans ; Electromyography ; Signal-To-Noise Ratio ; *Signal Processing, Computer-Assisted ; Algorithms ; *Stroke ; },
abstract = {BACKGROUND: Active participation of stroke survivors during robot-assisted movement therapy is essential for sensorimotor recovery. Robot-assisted therapy contingent on movement intention is an effective way to encourage patients' active engagement. For severely impaired stroke patients with no residual movements, a surface electromyogram (EMG) has been shown to be a viable option for detecting movement intention. Although numerous algorithms for EMG detection exist, the detector with the highest accuracy and lowest latency for low signal-to-noise ratio (SNR) remains unknown.

METHODS: This study, therefore, investigates the performance of 13 existing EMG detection algorithms on simulated low SNR (0dB and -3dB) EMG signals generated using three different EMG signal models: Gaussian, Laplacian, and biophysical model. The detector performance was quantified using the false positive rate (FPR), false negative rate (FNR), and detection latency. Any detector that consistently showed FPR and FNR of no more than 20%, and latency of no more than 50ms, was considered an appropriate detector for use in robot-assisted therapy.

RESULTS: The results indicate that the Modified Hodges detector - a simplified version of the threshold-based Hodges detector introduced in the current study - was the most consistent detector across the different signal models and SNRs. It consistently performed for ~90% and ~40% of the tested trials for 0dB and -3dB SNR, respectively. The two statistical detectors (Gaussian and Laplacian Approximate Generalized Likelihood Ratio) and the Fuzzy Entropy detectors have a slightly lower performance than Modified Hodges.

CONCLUSIONS: Overall, the Modified Hodges, Gaussian and Laplacian Approximate Generalized Likelihood Ratio, and the Fuzzy Entropy detectors were identified as the potential candidates that warrant further investigation with real surface EMG data since they had consistent detection performance on low SNR EMG data.},
}

Humans
Electromyography
Signal-To-Noise Ratio
*Signal Processing, Computer-Assisted
Algorithms
*Stroke

@article {pmid38584867,
year = {2024},
author = {Khan, AYY and Anjum, A and Qadri, HM},
title = {Ethical tightrope: Navigating neuro-ethics in brain computer interface (BCI) technology.},
journal = {Brain & spine},
volume = {4},
number = {},
pages = {102800},
pmid = {38584867},
issn = {2772-5294},
}

@article {pmid38581031,
year = {2024},
author = {Ferrero, L and Soriano-Segura, P and Navarro, J and Jones, O and Ortiz, M and Iáñez, E and Azorín, JM and Contreras-Vidal, JL},
title = {Brain-machine interface based on deep learning to control asynchronously a lower-limb robotic exoskeleton: a case-of-study.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {21},
number = {1},
pages = {48},
pmid = {38581031},
issn = {1743-0003},
support = {FPU19/03165//Ministry of Science, Innovation and Universities through the Aid for the Training of University Teachers/ ; PID2021-124111OB-C31//MCIN/AEI/10.13039/501100011033 and by ERDF A way of making Europe/ ; },
mesh = {Humans ; *Deep Learning ; *Exoskeleton Device ; *Brain-Computer Interfaces ; Algorithms ; Lower Extremity ; Electroencephalography/methods ; },
abstract = {BACKGROUND: This research focused on the development of a motor imagery (MI) based brain-machine interface (BMI) using deep learning algorithms to control a lower-limb robotic exoskeleton. The study aimed to overcome the limitations of traditional BMI approaches by leveraging the advantages of deep learning, such as automated feature extraction and transfer learning. The experimental protocol to evaluate the BMI was designed as asynchronous, allowing subjects to perform mental tasks at their own will.

METHODS: A total of five healthy able-bodied subjects were enrolled in this study to participate in a series of experimental sessions. The brain signals from two of these sessions were used to develop a generic deep learning model through transfer learning. Subsequently, this model was fine-tuned during the remaining sessions and subjected to evaluation. Three distinct deep learning approaches were compared: one that did not undergo fine-tuning, another that fine-tuned all layers of the model, and a third one that fine-tuned only the last three layers. The evaluation phase involved the exclusive closed-loop control of the exoskeleton device by the participants' neural activity using the second deep learning approach for the decoding.

RESULTS: The three deep learning approaches were assessed in comparison to an approach based on spatial features that was trained for each subject and experimental session, demonstrating their superior performance. Interestingly, the deep learning approach without fine-tuning achieved comparable performance to the features-based approach, indicating that a generic model trained on data from different individuals and previous sessions can yield similar efficacy. Among the three deep learning approaches compared, fine-tuning all layer weights demonstrated the highest performance.

CONCLUSION: This research represents an initial stride toward future calibration-free methods. Despite the efforts to diminish calibration time by leveraging data from other subjects, complete elimination proved unattainable. The study's discoveries hold notable significance for advancing calibration-free approaches, offering the promise of minimizing the need for training trials. Furthermore, the experimental evaluation protocol employed in this study aimed to replicate real-life scenarios, granting participants a higher degree of autonomy in decision-making regarding actions such as walking or stopping gait.},
}

Humans
*Deep Learning
*Exoskeleton Device
*Brain-Computer Interfaces
Algorithms
Lower Extremity
Electroencephalography/methods

@article {pmid38580923,
year = {2024},
author = {Allonen, S and Aittoniemi, J and Vuorialho, M and Närhi, L and Panula, K and Vuento, R and Honkaniemi, J},
title = {Streptococcus intermedius causing primary bacterial ventriculitis in a patient with severe periodontitis - a case report.},
journal = {BMC neurology},
volume = {24},
number = {1},
pages = {112},
pmid = {38580923},
issn = {1471-2377},
support = {EVO; 1326/2010//the State Research Funding of Vaasa Hospital District/ ; },
mesh = {Male ; Humans ; Middle Aged ; Streptococcus intermedius ; *Cerebral Ventriculitis/complications/diagnostic imaging/drug therapy ; Anti-Bacterial Agents/therapeutic use ; *Meningitis/diagnosis ; *Central Nervous System Bacterial Infections ; *Periodontitis/complications/drug therapy ; },
abstract = {BACKGROUND: Streptococcus intermedius is a member of the S. anginosus group and is part of the normal oral microbiota. It can cause pyogenic infections in various organs, primarily in the head and neck area, including brain abscesses and meningitis. However, ventriculitis due to periodontitis has not been reported previously.

CASE PRESENTATION: A 64-year-old male was admitted to the hospital with a headache, fever and later imbalance, blurred vision, and general slowness. Neurological examination revealed nuchal rigidity and general clumsiness. Meningitis was suspected, and the patient was treated with dexamethasone, ceftriaxone and acyclovir. A brain computer tomography (CT) scan was normal, and cerebrospinal fluid (CSF) Gram staining and bacterial cultures remained negative, so the antibacterial treatment was discontinued. Nine days after admission, the patient's condition deteriorated. The antibacterial treatment was restarted, and a brain magnetic resonance imaging revealed ventriculitis. A subsequent CT scan showed hydrocephalus, so a ventriculostomy was performed. In CSF Gram staining, chains of gram-positive cocci were observed. Bacterial cultures remained negative, but a bacterial PCR detected Streptococcus intermedius. An orthopantomography revealed advanced periodontal destruction in several teeth and periapical abscesses, which were subsequently operated on. The patient was discharged in good condition after one month.

CONCLUSIONS: Poor dental health can lead to life-threatening infections in the central nervous system, even in a completely healthy individual. Primary bacterial ventriculitis is a diagnostic challenge, which may result in delayed treatment and increased mortality.},
}

Male
Humans
Middle Aged
Streptococcus intermedius
*Cerebral Ventriculitis/complications/diagnostic imaging/drug therapy
Anti-Bacterial Agents/therapeutic use
*Meningitis/diagnosis
*Central Nervous System Bacterial Infections
*Periodontitis/complications/drug therapy

@article {pmid38580626,
year = {2024},
author = {Chen, F and Zheng, J and Wang, L and Krajbich, I},
title = {Attribute latencies causally shape intertemporal decisions.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {2948},
pmid = {38580626},
issn = {2041-1723},
support = {2148982//National Science Foundation (NSF)/ ; 72002195//National Natural Science Foundation of China (National Science Foundation of China)/ ; 71871199//National Natural Science Foundation of China (National Science Foundation of China)/ ; 72371226//National Natural Science Foundation of China (National Science Foundation of China)/ ; STI 2030-Major Projects 2021ZD0200409//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; },
mesh = {Humans ; Animals ; Mice ; Time Factors ; *Delay Discounting ; Reward ; Reaction Time ; Choice Behavior/physiology ; },
abstract = {Intertemporal choices - decisions that play out over time - pervade our life. Thus, how people make intertemporal choices is a fundamental question. Here, we investigate the role of attribute latency (the time between when people start to process different attributes) in shaping intertemporal preferences using five experiments with choices between smaller-sooner and larger-later rewards. In the first experiment, we identify attribute latencies using mouse-trajectories and find that they predict individual differences in choices, response times, and changes across time constraints. In the other four experiments we test the causal link from attribute latencies to choice, staggering the display of the attributes. This changes attribute latencies and intertemporal preferences. Displaying the amount information first makes people more patient, while displaying time information first does the opposite. These findings highlight the importance of intra-choice dynamics in shaping intertemporal choices and suggest that manipulating attribute latency may be a useful technique for nudging.},
}

Humans
Animals
Mice
Time Factors
*Delay Discounting
Reward
Reaction Time
Choice Behavior/physiology

@article {pmid38580047,
year = {2024},
author = {Wang, C and Sun, Y and Xing, Y and Liu, K and Xu, K},
title = {Role of electrophysiological activity and interactions of lateral habenula in the development of depression-like behavior in a chronic restraint stress model.},
journal = {Brain research},
volume = {},
number = {},
pages = {148914},
doi = {10.1016/j.brainres.2024.148914},
pmid = {38580047},
issn = {1872-6240},
abstract = {Closed-loop deep brain stimulation (DBS) system offers a promising approach for treatment-resistant depression, but identifying universally accepted electrophysiological biomarkers for closed-loop DBS systems targeting depression is challenging. There is growing evidence suggesting a strong association between the lateral habenula (LHb) and depression. Here, we took LHb as a key target, utilizing multi-site local field potentials (LFPs) to study the acute and chronic changes in electrophysiology, functional connectivity, and brain network characteristics during the formation of a chronic restraint stress (CRS) model. Furthermore, our model combining the electrophysiological changes of LHb and interactions between LHb and other potential targets of depression can effectively distinguish depressive states, offering a new way for developing effective closed-loop DBS strategies.},
}

@article {pmid38579958,
year = {2024},
author = {Lo, YT and Lim, MJR and Kok, CY and Wang, S and Blok, SZ and Ang, TY and Ng, VYP and Rao, JP and Chua, KSG},
title = {Neural interface-based motor neuroprosthesis in post-stroke upper limb neurorehabilitation: An individual patient data meta-analysis.},
journal = {Archives of physical medicine and rehabilitation},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.apmr.2024.04.001},
pmid = {38579958},
issn = {1532-821X},
abstract = {OBJECTIVE: To determine the efficacy of neural interface-, including brain-computer interface (BCI), based neurorehabilitation through conventional and individual patient data (IPD) meta-analysis, and to assess clinical parameters associated with positive response to neural interface-based neurorehabilitation.

DATA SOURCES: PubMed, EMBASE, and Cochrane Library databases up to February 2022 were reviewed.

STUDY SELECTION: Studies using neural interface-controlled physical effectors (FES and/or powered exoskeletons) and reported Fugl-Meyer Assessment-upper extremity (FMA-UE) scores were identified. This meta-analysis was prospectively registered on PROSPERO (#CRD42022312428). PRISMA guidelines were followed.

DATA EXTRACTION: Change in FMA-UE scores were pooled to estimate the mean effect size. Subgroup analyses were performed on clinical parameters and neural interface parameters with both study-level variables and IPD.

DATA SYNTHESIS: Forty-six studies containing 617 patients were included. Twenty-nine studies involving 214 patients reported IPD. FMA-UE score increased by a mean of 5.23 (95% CI: 3.85 to 6.61). Systems that used motor attempt resulted in greater FMA-UE gain than motor imagery, as did training lasting >4 versus ≤4 weeks. On IPD analysis, the mean time-to-improvement above MCID was 12 weeks (95% CI: 7 to not reached). At 6 months, 58% improved above MCID (95% CI: 41 to 70%). Patients with severe impairment (p=0.042) and age >50 years (p=0.0022) correlated with the failure to improve above the MCID on univariate log-rank tests. However, these factors were only borderline significant on multivariate Cox analysis (HR 0.15, p = 0.08 and HR 0.47, p = 0.06, respectively).

CONCLUSION: Neural interface-based motor rehabilitation resulted in significant though modest reductions in post-stroke impairment and should be considered for wider applications in stroke neurorehabilitation.},
}

@article {pmid38579696,
year = {2024},
author = {Ali, YH and Bodkin, KL and Rigotti-Thompson, M and Patel, K and Card, NS and Bhaduri, B and Nason-Tomaszewski, SR and Mifsud, DM and Hou, X and Nicolas, C and Allcroft, S and Hochberg, L and Au Yong, N and Stavisky, SD and Miller, LE and Brandman, D and Pandarinath, C},
title = {BRAND: A platform for closed-loop experiments with deep network models.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad3b3a},
pmid = {38579696},
issn = {1741-2552},
abstract = {OBJECTIVE: Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g., Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g., C and C++).

APPROACH: To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termed nodes, which communicate with each other in a graph via streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis, an in-memory database, to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes.

MAIN RESULTS: In our tests, BRAND achieved <600 microsecond latency between processes when sending large quantities of data (1024 channels of 30 kHz neural data in 1-millisecond chunks). BRAND runs a brain-computer interface with a recurrent neural network (RNN) decoder with less than 8 milliseconds of latency from neural data input to decoder prediction. In a real-world demonstration of the system, participant T11 in the BrainGate2 clinical trial (ClinicalTrials.gov Identifier: NCT00912041) performed a standard cursor control task, in which 30 kHz signal processing, RNN decoding, task control, and graphics were all executed in BRAND. This system also supports real-time inference with complex latent variable models like Latent Factor Analysis via Dynamical Systems.

SIGNIFICANCE: By providing a framework that is fast, modular, and language-agnostic, BRAND lowers the barriers to integrating the latest tools in neuroscience and machine learning into closed-loop experiments. .},
}

@article {pmid38578854,
year = {2024},
author = {Lin, PJ and Li, W and Zhai, X and Li, Z and Sun, J and Xu, Q and Pan, Y and Ji, L and Li, C},
title = {Explainable deep-learning prediction for brain-computer interfaces supported lower extremity motor gains based on multi-state fusion.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2024.3384498},
pmid = {38578854},
issn = {1558-0210},
abstract = {Predicting the potential for recovery of motor function in stroke patients who undergo specific rehabilitation treatments is an important and major challenge. Recently, electroencephalography (EEG) has shown potential in helping to determine the relationship between cortical neural activity and motor recovery. EEG recorded in different states could more accurately predict motor recovery than single-state recordings. Here, we design a multi-state (combining eyes closed, EC, and eyes open, EO) fusion neural network for predicting the motor recovery of patients with stroke after EEG-brain-computer-interface (BCI) rehabilitation training and use an explainable deep learning method to identify the most important features of EEG power spectral density and functional connectivity contributing to prediction. The prediction accuracy of the multi-states fusion network was 82%, significantly improved compared with a single-state model. The neural network explanation result demonstrated the important region and frequency oscillation bands. Specifically, in those two states, power spectral density and functional connectivity were shown as the regions and bands related to motor recovery in frontal, central, and occipital. Moreover, the motor recovery relation in bands, the power spectrum density shows the bands at delta and alpha bands. The functional connectivity shows the delta, theta, and alpha bands in the EC state; delta, theta, and beta mid at the EO state are related to motor recovery. Multi-state fusion neural networks, which combine multiple states of EEG signals into a single network, can increase the accuracy of predicting motor recovery after BCI training, and reveal the underlying mechanisms of motor recovery in brain activity.},
}

@article {pmid38577666,
year = {2024},
author = {Liu, F and Zheng, H and Ma, S and Zhang, W and Liu, X and Chua, Y and Shi, L and Zhao, R},
title = {Advancing brain-inspired computing with hybrid neural networks.},
journal = {National science review},
volume = {11},
number = {5},
pages = {nwae066},
pmid = {38577666},
issn = {2053-714X},
abstract = {Brain-inspired computing, drawing inspiration from the fundamental structure and information-processing mechanisms of the human brain, has gained significant momentum in recent years. It has emerged as a research paradigm centered on brain-computer dual-driven and multi-network integration. One noteworthy instance of this paradigm is the hybrid neural network (HNN), which integrates computer-science-oriented artificial neural networks (ANNs) with neuroscience-oriented spiking neural networks (SNNs). HNNs exhibit distinct advantages in various intelligent tasks, including perception, cognition and learning. This paper presents a comprehensive review of HNNs with an emphasis on their origin, concepts, biological perspective, construction framework and supporting systems. Furthermore, insights and suggestions for potential research directions are provided aiming to propel the advancement of the HNN paradigm.},
}

@article {pmid38576451,
year = {2024},
author = {Ning, M and Duwadi, S and Yücel, MA and von Lühmann, A and Boas, DA and Sen, K},
title = {fNIRS dataset during complex scene analysis.},
journal = {Frontiers in human neuroscience},
volume = {18},
number = {},
pages = {1329086},
pmid = {38576451},
issn = {1662-5161},
support = {T32 DC013017/DC/NIDCD NIH HHS/United States ; },
}

@article {pmid38572293,
year = {2024},
author = {},
title = {Correction to: Transfer learning promotes acquisition of individual BCI skills.},
journal = {PNAS nexus},
volume = {3},
number = {4},
pages = {pgae137},
doi = {10.1093/pnasnexus/pgae137},
pmid = {38572293},
issn = {2752-6542},
abstract = {[This corrects the article DOI: 10.1093/pnasnexus/pgae076.].},
}

@article {pmid38572110,
year = {2024},
author = {Wang, L and Hong, W and Zhu, H and He, Q and Yang, B and Wang, J and Weng, Q},
title = {Macrophage senescence in health and diseases.},
journal = {Acta pharmaceutica Sinica. B},
volume = {14},
number = {4},
pages = {1508-1524},
pmid = {38572110},
issn = {2211-3835},
abstract = {Macrophage senescence, manifested by the special form of durable cell cycle arrest and chronic low-grade inflammation like senescence-associated secretory phenotype, has long been considered harmful. Persistent senescence of macrophages may lead to maladaptation, immune dysfunction, and finally the development of age-related diseases, infections, autoimmune diseases, and malignancies. However, it is a ubiquitous, multi-factorial, and dynamic complex phenomenon that also plays roles in remodeled processes, including wound repair and embryogenesis. In this review, we summarize some general molecular changes and several specific biomarkers during macrophage senescence, which may bring new sight to recognize senescent macrophages in different conditions. Also, we take an in-depth look at the functional changes in senescent macrophages, including metabolism, autophagy, polarization, phagocytosis, antigen presentation, and infiltration or recruitment. Furthermore, some degenerations and diseases associated with senescent macrophages as well as the mechanisms or relevant genetic regulations of senescent macrophages are integrated, not only emphasizing the possibility of regulating macrophage senescence to benefit age-associated diseases but also has an implication on the finding of potential targets or drugs clinically.},
}

@article {pmid38570593,
year = {2024},
author = {van Stuijvenberg, OC and Broekman, MLD and Wolff, SEC and Bredenoord, AL and Jongsma, KR},
title = {Developer perspectives on the ethics of AI-driven neural implants: a qualitative study.},
journal = {Scientific reports},
volume = {14},
number = {1},
pages = {7880},
pmid = {38570593},
issn = {2045-2322},
support = {17619//Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ ; },
mesh = {*Artificial Intelligence ; Reproducibility of Results ; Qualitative Research ; Focus Groups ; *Cochlear Implants ; },
abstract = {Convergence of neural implants with artificial intelligence (AI) presents opportunities for the development of novel neural implants and improvement of existing neurotechnologies. While such technological innovation carries great promise for the restoration of neurological functions, they also raise ethical challenges. Developers of AI-driven neural implants possess valuable knowledge on the possibilities, limitations and challenges raised by these innovations; yet their perspectives are underrepresented in academic literature. This study aims to explore perspectives of developers of neurotechnology to outline ethical implications of three AI-driven neural implants: a cochlear implant, a visual neural implant, and a motor intention decoding speech-brain-computer-interface. We conducted semi-structured focus groups with developers (n = 19) of AI-driven neural implants. Respondents shared ethically relevant considerations about AI-driven neural implants that we clustered into three themes: (1) design aspects; (2) challenges in clinical trials; (3) impact on users and society. Developers considered accuracy and reliability of AI-driven neural implants conditional for users' safety, authenticity, and mental privacy. These needs were magnified by the convergence with AI. Yet, the need for accuracy and reliability may also conflict with potential benefits of AI in terms of efficiency and complex data interpretation. We discuss strategies to mitigate these challenges.},
}

*Artificial Intelligence
Reproducibility of Results
Qualitative Research
Focus Groups
*Cochlear Implants

@article {pmid38570113,
year = {2024},
author = {Sun, WB and Fu, JX and Chen, YL and Li, HF and Wu, ZY and Chen, DF},
title = {Both gain- and loss-of-function variants of KCNA1 are associated with paroxysmal kinesignic dyskinesia.},
journal = {Journal of genetics and genomics = Yi chuan xue bao},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jgg.2024.03.013},
pmid = {38570113},
issn = {1673-8527},
abstract = {KCNA1 is the coding gene for Kv1.1 voltage-gated potassium channel α subunit. Three variants of KCNA1 have been reported to manifest as paroxysmal kinesignic dyskinesia (PKD), but the correlation between them remains unclear due to the phenotypic complexity of KCNA1 variants as well as the rarity of PKD cases. Using the whole exome sequencing followed by Sanger sequencing, we screen potential pathogenic KCNA1 variants in patients clinically diagnosed with paroxysmal movement disorders and identify three previously unreported missense variants of KCNA1 in three unrelated Chinese families. The proband of one family (c.496G>A, p.A166T) manifests as episodic ataxia type 1, and the other two (c.877G>A, p.V293I; and c.1112C>A, p.T371A) manifest as PKD. The pathogenicity of these variants is confirmed by functional studies, suggesting that p.A166T and p.T371A cause a loss-of-function of the channel, while p.V293I leads to a gain-of-function with the property of voltage-dependent gating and activation kinetic affected. By reviewing the locations of PKD-manifested KCNA1 variants in Kv1.1 protein, we find that these variants tend to cluster around the pore domain, which is similar to epilepsy. Thus, our study strengthens the correlation between KCNA1 variants and PKD and provides more information on genotype-phenotype correlations of KCNA1 channelopathy.},
}

@article {pmid38565846,
year = {2024},
author = {Li, H and Li, Z and Yuan, X and Tian, Y and Ye, W and Zeng, P and Li, XM and Guo, F},
title = {Dynamic encoding of temperature in the central circadian circuit coordinates physiological activities.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {2834},
pmid = {38565846},
issn = {2041-1723},
mesh = {Animals ; Circadian Rhythm/physiology ; Temperature ; Sleep/physiology ; Drosophila ; *Circadian Clocks/physiology ; *Drosophila Proteins/genetics ; Drosophila melanogaster/physiology ; },
abstract = {The circadian clock regulates animal physiological activities. How temperature reorganizes circadian-dependent physiological activities remains elusive. Here, using in-vivo two-photon imaging with the temperature control device, we investigated the response of the Drosophila central circadian circuit to temperature variation and identified that DN1as serves as the most sensitive temperature-sensing neurons. The circadian clock gate DN1a's diurnal temperature response. Trans-synaptic tracing, connectome analysis, and functional imaging data reveal that DN1as bidirectionally targets two circadian neuronal subsets: activity-related E cells and sleep-promoting DN3s. Specifically, behavioral data demonstrate that the DN1a-E cell circuit modulates the evening locomotion peak in response to cold temperature, while the DN1a-DN3 circuit controls the warm temperature-induced nocturnal sleep reduction. Our findings systematically and comprehensively illustrate how the central circadian circuit dynamically integrates temperature and light signals to effectively coordinate wakefulness and sleep at different times of the day, shedding light on the conserved neural mechanisms underlying temperature-regulated circadian physiology in animals.},
}

Animals
Circadian Rhythm/physiology
Temperature
Sleep/physiology
Drosophila
*Circadian Clocks/physiology
*Drosophila Proteins/genetics
Drosophila melanogaster/physiology

@article {pmid38565100,
year = {2024},
author = {Li, W and Li, H and Sun, X and Kang, H and An, S and Wang, G and Gao, Z},
title = {Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/ad3986},
pmid = {38565100},
issn = {1741-2552},
abstract = {OBJECTIVE: The extensive application of electroencephalography (EEG) in brain-computer interfaces (BCIs) can be attributed to its non-invasive nature and capability to offer high-resolution data. The acquisition of EEG signals is a straightforward process, but the datasets associated with these signals frequently exhibit data scarcity and require substantial resources for proper labeling. Furthermore, there is a significant limitation in the generalization performance of EEG models due to the substantial inter-individual variability observed in EEG signals.

APPROACH: To address these issues, we propose a novel self-supervised contrastive learning framework for decoding motor imagery (MI) signals in cross-subject scenarios. Specifically, we design an encoder combining convolutional neural network and attention mechanism. In the contrastive learning training stage, the network undergoes training with the pretext task of data augmentation to minimize the distance between pairs of homologous transformations while simultaneously maximizing the distance between pairs of heterologous transformations. It enhances the amount of data utilized for training and improves the network's ability to extract deep features from original signals without relying on the true labels of the data.

MAIN RESULTS: To evaluate our framework's efficacy, we conduct extensive experiments on three public MI datasets: BCI IV IIa, BCI IV IIb, and HGD datasets. The proposed method achieves cross-subject classification accuracies of 67.32%, 82.34%, and 81.13% on the three datasets, demonstrating superior performance compared to existing methods.

SIGNIFICANCE: Therefore, this method has great promise for improving the performance of cross-subject transfer learning in MI-based BCI systems.},
}

@article {pmid38563704,
year = {2024},
author = {Yang, Q and Wu, B and Castagnola, E and Pwint, MY and Williams, N and Vazquez, AL and Cui, XT},
title = {Integrated Microprism and Microelectrode Array for Simultaneous Electrophysiology and Two-Photon Imaging Across all Cortical Layers.},
journal = {Advanced healthcare materials},
volume = {},
number = {},
pages = {e2302362},
doi = {10.1002/adhm.202302362},
pmid = {38563704},
issn = {2192-2659},
abstract = {Cerebral neural electronics play a crucial role in neuroscience research with increasing translational applications such as brain-computer interface for sensory input and motor output restoration. While widely utilized for decades, our understandings of the cellular mechanisms underlying this technology remains limited. Although two-photon microscopy (TPM) has shown great promise in imaging superficial neural electrodes, its application to deep-penetrating electrodes is unclear. Here, we introduce a novel device integrating transparent microelectrode arrays (MEAs) with glass microprisms, enabling electrophysiology recording and stimulation alongside TPM imaging across all cortical layers in a vertical plane. Tested in Thy1-GCaMP6 mice for over 4 months, our integrated device demonstrated the capability for multisite electrophysiological recording and simultaneous TPM calcium imaging. As a proof of concept, we investigated the impact of microstimulation amplitude, frequency, and depth on neural activation patterns throughout cortical layers using our setup. With future improvements in material stability and single unit yield, our multimodal tool can greatly expand integrated electrophysiology and optical imaging from the superficial brain to the entire cortical column, opening new avenues for neuroscience research and neurotechnology development. This article is protected by copyright. All rights reserved.},
}

@article {pmid38562772,
year = {2024},
author = {Rajeswaran, P and Payeur, A and Lajoie, G and Orsborn, AL},
title = {Assistive sensory-motor perturbations influence learned neural representations.},
journal = {bioRxiv : the preprint server for biology},
volume = {},
number = {},
pages = {},
doi = {10.1101/2024.03.20.585972},
pmid = {38562772},
abstract = {Task errors are used to learn and refine motor skills. We investigated how task assistance influences learned neural representations using Brain-Computer Interfaces (BCIs), which map neural activity into movement via a decoder. We analyzed motor cortex activity as monkeys practiced BCI with a decoder that adapted to improve or maintain performance over days. Population dimensionality remained constant or increased with learning, counter to trends with non-adaptive BCIs. Yet, over time, task information was contained in a smaller subset of neurons or population modes. Moreover, task information was ultimately stored in neural modes that occupied a small fraction of the population variance. An artificial neural network model suggests the adaptive decoders contribute to forming these compact neural representations. Our findings show that assistive decoders manipulate error information used for long-term learning computations, like credit assignment, which informs our understanding of motor learning and has implications for designing real-world BCIs.},
}

@article {pmid37935217,
year = {2024},
author = {Venu, K and Natesan, P},
title = {Hybrid optimization assisted channel selection of EEG for deep learning model-based classification of motor imagery task.},
journal = {Biomedizinische Technik. Biomedical engineering},
volume = {69},
number = {2},
pages = {125-140},
pmid = {37935217},
issn = {1862-278X},
mesh = {*Deep Learning ; Electroencephalography/methods ; *Brain-Computer Interfaces ; Imagination ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {OBJECTIVES: To design and develop an approach named HC + SMA-SSA scheme for classifying motor imagery task.

METHODS: The offered model employs a new method for classifying motor imagery task. Initially, down sampling is deployed to pre-process the incoming signal. Subsequently, "Modified Stockwell Transform (ST) and common spatial pattern (CSP) based features are extracted". Then, optimal channel selection is made by a novel hybrid optimization model named as Spider Monkey Assisted SSA (SMA-SSA). Here, "Long Short Term Memory (LSTM) and Bidirectional Gated Recurrent Unit (BI-GRU)" models are used for final classification, whose outcomes are averaged at the end. At last, the improvement of SMA-SSA based model is proven over different metrics.

RESULTS: A superior sensitivity of 0.939 is noted for HC + SMA-SSA that was higher over HC with no optimization and proposed with traditional ST.

CONCLUSIONS: The proposed method achieved effective classification performance in terms of performance measures.},
}

*Deep Learning
Electroencephalography/methods
*Brain-Computer Interfaces
Imagination
Algorithms
Signal Processing, Computer-Assisted

@article {pmid38566861,
year = {2019},
author = {Fontaine, AK and Segil, JL and Caldwell, JH and Weir, RFF},
title = {Real-Time Prosthetic Digit Actuation by Optical Read-out of Activity-Dependent Calcium Signals in an Ex Vivo Peripheral Nerve.},
journal = {International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering},
volume = {2019},
number = {},
pages = {143-146},
pmid = {38566861},
issn = {1948-3546},
support = {OT2 OD023852/OD/NIH HHS/United States ; },
abstract = {Improved neural interfacing strategies are needed for the full articulation of advanced prostheses. To address limitations of existing control interface designs, the work of our laboratory has presented an optical approach to reading activity from individual nerve fibers using activity-dependent calcium transients. Here, we demonstrate the feasibility of such signals to control prosthesis actuation by using the axonal fluorescence signal in an ex vivo mouse nerve to drive a prosthetic digit in real-time. Additionally, signals of varying action potential frequency are streamed post hoc to the prosthesis, showing graded motor output and the potential for proportional neural control. This proof-of-concept work is a novel demonstration of the functional use of activity-dependent optical read-out in the nerve.},
}

@article {pmid38562360,
year = {2024},
author = {Ilchev, B and Chervenkov, V and Valchev, N and Nakov, V and Minchev, T and Vassilev, G and Tsvetanov, T and Laleva, L and Milev, M and Spiriev, T},
title = {Interdisciplinary Successful Revascularization of Traumatic Occlusion of the Right Common Carotid Artery.},
journal = {Cureus},
volume = {16},
number = {3},
pages = {e55395},
pmid = {38562360},
issn = {2168-8184},
abstract = {Blunt carotid artery injury (BCI) poses a rare yet severe threat following vascular trauma, often leading to significant morbidity and mortality. We present a case of a 33-year-old male who suffered complete thrombotic occlusion of the right common carotid artery (CCA) following a workplace accident. Clinical evaluation revealed profound neurological deficits, prompting multidisciplinary surgical intervention guided by the Denver criteria (Grade I - disruption inside the vessel that results in a narrowing of the lumen by less than 25%; Grade II - dissection or intramural hematoma causing greater than 25% stenosis; Grade III - comprises pseudoaneurysm formation; Grade IV - causes total vessel occlusion; Grade V - describes vessel transection with extravasation). Surgical exploration unveiled extensive arterial damage, necessitating thrombectomy, primary repair, and double-layered patch angioplasty using an autologous saphenous vein. Postoperative recovery was uneventful, with the restoration of pulsatile blood flow confirmed by Doppler ultrasound. Three-month follow-up demonstrated patent arterial reconstruction and improved cerebral perfusion, despite the persistent neurological deficits. Our case underscores the challenges in diagnosing and managing BCI, advocating for a tailored approach based on injury severity and neurological status. While conservative management remains standard, surgical intervention offers a viable option in select cases, particularly those with complete vessel occlusion and neurological compromise. Long-term surveillance is imperative to assess the durability of arterial reconstruction and monitor for recurrent thromboembolic events. Further research is warranted to refine management algorithms and elucidate optimal treatment strategies in this rare but critical vascular pathology.},
}

@article {pmid38560190,
year = {2024},
author = {Akuthota, S and K, R and Ravichander, J},
title = {Artifact removal and motor imagery classification in EEG using advanced algorithms and modified DNN.},
journal = {Heliyon},
volume = {10},
number = {7},
pages = {e27198},
pmid = {38560190},
issn = {2405-8440},
abstract = {This paper presents an advanced approach for EEG artifact removal and motor imagery classification using a combination of Four Class Iterative Filtering and Filter Bank Common Spatial Pattern Algorithm with a Modified Deep Neural Network (DNN) classifier. The research aims to enhance the accuracy and reliability of BCI systems by addressing the challenges posed by EEG artifacts and complex motor imagery tasks. The methodology begins by introducing FCIF, a novel technique for ocular artifact removal, utilizing iterative filtering and filter banks. FCIF's mathematical formulation allows for effective artifact mitigation, thereby improving the quality of EEG data. In tandem, the FC-FBCSP algorithm is introduced, extending the Filter Bank Common Spatial Pattern approach to handle four-class motor imagery classification. The Modified DNN classifier enhances the discriminatory power of the FC-FBCSP features, optimizing the classification process. The paper showcases a comprehensive experimental setup, featuring the utilization of BCI Competition IV Dataset 2a & 2b. Detailed preprocessing steps, including filtering and feature extraction, are presented with mathematical rigor. Results demonstrate the remarkable artifact removal capabilities of FCIF and the classification prowess of FC-FBCSP combined with the Modified DNN classifier. Comparative analysis highlights the superiority of the proposed approach over baseline methods and the method achieves the mean accuracy of 98.575%.},
}

@article {pmid38560116,
year = {2024},
author = {Chen, D},
title = {Improved empirical mode decomposition bagging RCSP combined with Fisher discriminant method for EEG feature extraction and classification.},
journal = {Heliyon},
volume = {10},
number = {7},
pages = {e28235},
pmid = {38560116},
issn = {2405-8440},
abstract = {BACKGROUND: Traditional Common Spatial Pattern (CSP) algorithms for Electroencephalogram (EEG) signal classification are sensitive to noise and can produce low accuracy in small sample datasets.

NEW METHOD: To solve the problem, an improved Empirical Mode Decomposition (EMD) Bagging Regularized CSP (RCSP) algorithm is proposed. It filters EEG signals through improved EMD, inhibits high-frequency noise, retains effective information in the characteristic frequency band, and uses Bagging algorithm for data reconstruction. Feature extraction is performed with regularization of spatial patterns and Fisher linear discriminant analysis for feature classification. T-test is used for classification.

RESULTS: The improved EMD Bagging RCSP algorithm has improved accuracy and robustness compared to CSP and its derivatives. The average classification rate is increased by about 6%, demonstrating the effectiveness and correctness of the proposed algorithm.Comparison with existing methods: The proposed algorithm outperforms CSP and its derivatives by retaining effective information and inhibiting high-frequency noise in small sample EEG datasets.

CONCLUSIONS: The proposed EMD Bagging RCSP algorithm provides a reliable and effective method for EEG signal classification and can be used in various applications, including brain-computer interfaces and clinical EEG diagnosis.},
}

@article {pmid38558011,
year = {2024},
author = {Chen, D and Zhao, Z and Zhang, S and Chen, S and Wu, X and Shi, J and Liu, N and Pan, C and Tang, Y and Meng, C and Zhao, X and Tao, B and Liu, W and Chen, D and Ding, H and Zhang, P and Tang, Z},
title = {Evolving Therapeutic Landscape of Intracerebral Hemorrhage: Emerging Cutting-Edge Advancements in Surgical Robots, Regenerative Medicine, and Neurorehabilitation Techniques.},
journal = {Translational stroke research},
volume = {},
number = {},
pages = {},
pmid = {38558011},
issn = {1868-601X},
support = {2022B37//Research Fund of Tongji Hospital/ ; 92148206//National Natural Science Foundation of China/ ; },
abstract = {Intracerebral hemorrhage (ICH) is the most serious form of stroke and has limited available therapeutic options. As knowledge on ICH rapidly develops, cutting-edge techniques in the fields of surgical robots, regenerative medicine, and neurorehabilitation may revolutionize ICH treatment. However, these new advances still must be translated into clinical practice. In this review, we examined several emerging therapeutic strategies and their major challenges in managing ICH, with a particular focus on innovative therapies involving robot-assisted minimally invasive surgery, stem cell transplantation, in situ neuronal reprogramming, and brain-computer interfaces. Despite the limited expansion of the drug armamentarium for ICH over the past few decades, the judicious selection of more efficacious therapeutic modalities and the exploration of multimodal combination therapies represent opportunities to improve patient prognoses after ICH.},
}

@article {pmid38557253,
year = {2024},
author = {Van Horn, AL and Burgess, JR},
title = {From Blunt Cardiac Injury to Heart Transplant Following Motorcycle Collision.},
journal = {The American surgeon},
volume = {},
number = {},
pages = {31348241241699},
doi = {10.1177/00031348241241699},
pmid = {38557253},
issn = {1555-9823},
abstract = {Traumatic coronary artery occlusion and dissection is an exceedingly rare complication of blunt cardiac injury (BCI), though it has been previously noted in a number of case reports. However, it can also lead to heart transplant, which to our knowledge has not been previously described in the literature. We present a case of a healthy 24-year-old man without significant past medical history who was in a motorcycle accident, resulting in sternal fracture and BCI. He was ultimately found to have thrombotic occlusion and dissection of his left anterior descending artery (LAD), requiring mechanical thrombectomy and drug-eluting stent, as well as subsequent hospitalizations and operations due to various complications. It was suspected that he went into ventricular fibrillation and had a second motorcycle collision, resulting in cardiogenic shock. Ultimately, his progression of ischemic cardiomyopathy and mitral regurgitation led to the need for heart transplant. Blunt cardiac injury with myocardial contusion has such a broad range of pathologies. It is essential that patients with these injury patterns raise a high level of suspicion for BCI and are followed closely with appropriate diagnostic testing and rapid intervention for best possible outcomes.},
}

@article {pmid38557034,
year = {2024},
author = {Ling, W and Shang, X and Yu, C and Li, C and Xu, K and Feng, L and Wei, Y and Tang, T and Huang, X},
title = {Miniaturized Implantable Fluorescence Probes Integrated with Metal-Organic Frameworks for Deep Brain Dopamine Sensing.},
journal = {ACS nano},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsnano.4c00632},
pmid = {38557034},
issn = {1936-086X},
abstract = {Continuously monitoring neurotransmitter dynamics can offer profound insights into neural mechanisms and the etiology of neurological diseases. Here, we present a miniaturized implantable fluorescence probe integrated with metal-organic frameworks (MOFs) for deep brain dopamine sensing. The probe is assembled from physically thinned light-emitting diodes (LEDs) and phototransistors, along with functional surface coatings, resulting in a total thickness of 120 μm. A fluorescent MOF that specifically binds dopamine is introduced, enabling a highly sensitive dopamine measurement with a detection limit of 79.9 nM. A compact wireless circuit weighing only 0.85 g is also developed and interfaced with the probe, which was later applied to continuously monitor real-time dopamine levels during deep brain stimulation in rats, providing critical information on neurotransmitter dynamics. Cytotoxicity tests and immunofluorescence analysis further suggest a favorable biocompatibility of the probe for implantable applications. This work presents fundamental principles and techniques for integrating fluorescent MOFs and flexible electronics for brain-computer interfaces and may provide more customized platforms for applications in neuroscience, disease tracing, and smart diagnostics.},
}

@article {pmid38555287,
year = {2024},
author = {Wei, M and Xu, K and Tang, B and Li, J and Yun, Y and Zhang, P and Wu, Y and Bao, K and Lei, K and Chen, Z and Ma, H and Sun, C and Liu, R and Li, M and Li, L and Lin, H},
title = {Monolithic back-end-of-line integration of phase change materials into foundry-manufactured silicon photonics.},
journal = {Nature communications},
volume = {15},
number = {1},
pages = {2786},
pmid = {38555287},
issn = {2041-1723},
support = {91950204, 62105287, 61975179, and 92150302//National Natural Science Foundation of China (National Science Foundation of China)/ ; },
abstract = {Monolithic integration of novel materials without modifying the existing photonic component library is crucial to advancing heterogeneous silicon photonic integrated circuits. Here we show the introduction of a silicon nitride etch stop layer at select areas, coupled with low-loss oxide trench, enabling incorporation of functional materials without compromising foundry-verified device reliability. As an illustration, two distinct chalcogenide phase change materials (PCMs) with remarkable nonvolatile modulation capabilities, namely Sb2Se3 and Ge2Sb2Se4Te1, were monolithic back-end-of-line integrated, offering compact phase and intensity tuning units with zero-static power consumption. By employing these building blocks, the phase error of a push-pull Mach-Zehnder interferometer optical switch could be reduced with a 48% peak power consumption reduction. Mirco-ring filters with >5-bit wavelength selective intensity modulation and waveguide-based >7-bit intensity-modulation broadband attenuators could also be achieved. This foundry-compatible platform could open up the possibility of integrating other excellent optoelectronic materials into future silicon photonic process design kits.},
}

@article {pmid38554856,
year = {2024},
author = {Bader, ER and Boro, AD and Killian, NJ and Eskandar, EN},
title = {A method for precisely timed, on-demand intracranial stimulation using the RNS device.},
journal = {Brain stimulation},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.brs.2024.03.020},
pmid = {38554856},
issn = {1876-4754},
}

@article {pmid38554787,
year = {2024},
author = {Chunduri, V and Aoudni, Y and Khan, S and Aziz, A and Rizwan, A and Deb, N and Keshta, I and Soni, M},
title = {Multi-Scale Spatiotemporal Attention Network for Neuron based Motor Imagery EEG Classification.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110128},
doi = {10.1016/j.jneumeth.2024.110128},
pmid = {38554787},
issn = {1872-678X},
abstract = {BACKGROUND: In recent times, the expeditious expansion of Brain-Computer Interface (BCI) technology in neuroscience, which relies on electroencephalogram (EEG) signals associated with motor imagery, has yielded outcomes that rival conventional approaches, notably due to the triumph of deep learning. Nevertheless, the task of developing and training a comprehensive network to extract the underlying characteristics of motor imagining EEG data continues to pose challenges.

NEW METHOD: This paper presents a multi-scale spatiotemporal self-attention (SA) network model that relies on an attention mechanism. This model aims to classify motor imagination EEG signals into four classes (left hand, right hand, foot, tongue/rest) by considering the temporal and spatial properties of EEG. It is employed to autonomously allocate greater weights to channels linked to motor activity and lesser weights to channels not related to movement, thus choosing the most suitable channels. Neuron utilises parallel multi-scale Temporal Convolutional Network (TCN) layers to extract feature information in the temporal domain at various scales, effectively eliminating temporal domain noise.

RESULTS: The suggested model achieves accuracies of 79.26%, 85.90%, and 96.96% on the BCI competition datasets IV-2a, IV-2b, and HGD, respectively.

In terms of single-subject classification accuracy, this strategy demonstrates superior performance compared to existing methods.

CONCLUSION: The results indicate that the proposed strategy exhibits favourable performance, resilience, and transfer learning capabilities.},
}

@article {pmid38552161,
year = {2024},
author = {Wang, W and Zhou, H and Xu, Z and Li, Z and Zhang, L and Wan, P},
title = {Flexible Conformally Bioadhesive MXene Hydrogel Electronics for Machine Learning-Facilitated Human-Interactive Sensing.},
journal = {Advanced materials (Deerfield Beach, Fla.)},
volume = {},
number = {},
pages = {e2401035},
doi = {10.1002/adma.202401035},
pmid = {38552161},
issn = {1521-4095},
abstract = {Wearable epidermic electronics assembled from conductive hydrogels are attracting various research attention for their seamless integration with human body for conformally real-time health monitoring, clinical diagnostics and medical treatment, and human-interactive sensing. Nevertheless, it remains a tremendous challenge to simultaneously achieve conformally bioadhesive epidermic electronics with remarkable self-adhesiveness, reliable ultraviolet (UV)-protection ability, and admirable sensing performance for high-fidelity epidermal electrophysiological signals monitoring, along with timely photothermal therapeutic performances after medical diagnostic sensing, as well as efficient antibacterial activity and reliable hemostatic effect for potential medical therapy. Herein, a conformally bioadhesive hydrogel-based epidermic sensor, featuring superior self-adhesiveness and excellent UV-protection performance, is developed by dexterously assembling conducting MXene nanosheets network with biological hydrogel polymer network for conformally stably attaching onto human skin for high-quality recording of various epidermal electrophysiological signals with high signal-to-noise ratios (SNR) and low interfacial impedance for intelligent medical diagnosis and smart human-machine interface. Moreover, a smart sign language gesture recognition platform based on collected EMG signals are designed for hassle-free communication with hearing-impaired people with the help of advanced machine learning algorithms. Meanwhile, the bioadhesive MXene hydrogel possesses reliable antibacterial capability, excellent biocompatibility and effective hemostasis properties for promising bacterial-infected wound bleeding. This article is protected by copyright. All rights reserved.},
}

@article {pmid38550646,
year = {2024},
author = {Bossi, F and Ciardo, F and Mostafaoui, G},
title = {Editorial: Neurocognitive features of human-robot and human-machine interaction.},
journal = {Frontiers in psychology},
volume = {15},
number = {},
pages = {1394970},
pmid = {38550646},
issn = {1664-1078},
}

@article {pmid38550567,
year = {2024},
author = {Racz, FS and Kumar, S and Kaposzta, Z and Alawieh, H and Liu, DH and Liu, R and Czoch, A and Mukli, P and Millán, JDR},
title = {Combining detrended cross-correlation analysis with Riemannian geometry-based classification for improved brain-computer interface performance.},
journal = {Frontiers in neuroscience},
volume = {18},
number = {},
pages = {1271831},
pmid = {38550567},
issn = {1662-4548},
abstract = {Riemannian geometry-based classification (RGBC) gained popularity in the field of brain-computer interfaces (BCIs) lately, due to its ability to deal with non-stationarities arising in electroencephalography (EEG) data. Domain adaptation, however, is most often performed on sample covariance matrices (SCMs) obtained from EEG data, and thus might not fully account for components affecting covariance estimation itself, such as regional trends. Detrended cross-correlation analysis (DCCA) can be utilized to estimate the covariance structure of such signals, yet it is computationally expensive in its original form. A recently proposed online implementation of DCCA, however, allows for its fast computation and thus makes it possible to employ DCCA in real-time applications. In this study we propose to replace the SCM with the DCCA matrix as input to RGBC and assess its effect on offline and online BCI performance. First we evaluated the proposed decoding pipeline offline on previously recorded EEG data from 18 individuals performing left and right hand motor imagery (MI), and benchmarked it against vanilla RGBC and popular MI-detection approaches. Subsequently, we recruited eight participants (with previous BCI experience) who operated an MI-based BCI (MI-BCI) online using the DCCA-enhanced Riemannian decoder. Finally, we tested the proposed method on a public, multi-class MI-BCI dataset. During offline evaluations the DCCA-based decoder consistently and significantly outperformed the other approaches. Online evaluation confirmed that the DCCA matrix could be computed in real-time even for 22-channel EEG, as well as subjects could control the MI-BCI with high command delivery (normalized Cohen's κ: 0.7409 ± 0.1515) and sample-wise MI detection (normalized Cohen's κ: 0.5200 ± 0.1610). Post-hoc analysis indicated characteristic connectivity patterns under both MI conditions, with stronger connectivity in the hemisphere contralateral to the MI task. Additionally, fractal scaling exponent of neural activity was found increased in the contralateral compared to the ipsilateral motor cortices (C4 and C3 for left and right MI, respectively) in both classes. Combining DCCA with Riemannian geometry-based decoding yields a robust and effective decoder, that not only improves upon the SCM-based approach but can also provide relevant information on the neurophysiological processes behind MI.},
}

@article {pmid38547834,
year = {2024},
author = {Abbott, JR and Jeakle, EN and Haghighi, P and Usoro, JO and Sturgill, BS and Wu, Y and Geramifard, N and Radhakrishna, R and Patnaik, S and Nakajima, S and Hess, J and Mehmood, Y and Devata, V and Vijayakumar, G and Sood, A and Doan Thai, TT and Dogra, K and Hernandez-Reynoso, AG and Pancrazio, JJ and Cogan, SF},
title = {Planar amorphous silicon carbide microelectrode arrays for chronic recording in rat motor cortex.},
journal = {Biomaterials},
volume = {308},
number = {},
pages = {122543},
doi = {10.1016/j.biomaterials.2024.122543},
pmid = {38547834},
issn = {1878-5905},
abstract = {Chronic implantation of intracortical microelectrode arrays (MEAs) capable of recording from individual neurons can be used for the development of brain-machine interfaces. However, these devices show reduced recording capabilities under chronic conditions due, at least in part, to the brain's foreign body response (FBR). This creates a need for MEAs that can minimize the FBR to possibly enable long-term recording. A potential approach to reduce the FBR is the use of MEAs with reduced cross-sectional geometries. Here, we fabricated 4-shank amorphous silicon carbide (a-SiC) MEAs and implanted them into the motor cortex of seven female Sprague-Dawley rats. Each a-SiC MEA shank was 8 μm thick by 20 μm wide and had sixteen sputtered iridium oxide film (SIROF) electrodes (4 per shank). A-SiC was chosen as the fabrication base for its high chemical stability, good electrical insulation properties, and amenability to thin film fabrication. Electrochemical analysis and neural recordings were performed weekly for 4 months. MEAs were characterized pre-implantation in buffered saline and in vivo using electrochemical impedance spectroscopy and cyclic voltammetry at 50 mV/s and 50,000 mV/s. Neural recordings were analyzed for single unit activity. At the end of the study, animals were sacrificed for immunohistochemical analysis. We observed statistically significant, but small, increases in 1 and 30 kHz impedance values and 50,000 mV/s charge storage capacity over the 16-week implantation period. Slow sweep 50 mV/s CV and 1 Hz impedance did not significantly change over time. Impedance values increased from 11.6 MΩ to 13.5 MΩ at 1 Hz, 1.2 MΩ-2.9 MΩ at 1 kHz, and 0.11 MΩ-0.13 MΩ at 30 kHz over 16 weeks. The median charge storage capacity of the implanted electrodes at 50 mV/s was 58.1 mC/cm[2] on week 1 and 55.9 mC/cm[2] on week 16, and at 50,000 mV/s, 4.27 mC/cm[2] on week 1 and 5.93 mC/cm[2] on week 16. Devices were able to record neural activity from 92% of all active channels at the beginning of the study, At the study endpoint, a-SiC devices were still recording single-unit activity on 51% of electrochemically active electrode channels. In addition, we observed that the signal-to-noise ratio experienced a small decline of -0.19 per week. We also classified observed units as fast and slow repolarizing based on the trough-to-peak time. Although the overall presence of single units declined, fast and slow repolarizing units declined at a similar rate. At recording electrode depth, immunohistochemistry showed minimal tissue response to the a-SiC devices, as indicated by statistically insignificant differences in activated glial cell response between implanted brains slices and contralateral sham slices at 150 μm away from the implant location, as evidenced by GFAP staining. NeuN staining revealed the presence of neuronal cell bodies close to the implantation site, again statistically not different from a contralateral sham slice. These results warrant further investigation of a-SiC MEAs for future long-term implantation neural recording studies.},
}

@article {pmid38545737,
year = {2024},
author = {Ergün, E and Aydemir, Ö and Korkmaz, OE},
title = {Investigating the informative brain region in multiclass electroencephalography and near infrared spectroscopy based BCI system using band power based features.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-16},
doi = {10.1080/10255842.2024.2333924},
pmid = {38545737},
issn = {1476-8259},
abstract = {In recent years, various brain imaging techniques have been used as input signals for brain-computer interface (BCI) systems. Electroencephalography (EEG) and near-infrared spectroscopy (NIRS) are two prominent techniques in this field, each with its own advantages and limitations. As a result, there is a growing tendency to integrate these methods in a hybrid within BCI systems. The primary aim of this study is to identify highly functional brain regions within an EEG + NIRS-based BCI system. To achieve this, the research focused on identifying EEG electrodes positioned in different brain lobes and then investigating the functionality of each lobe. The methodology involved segmenting the EEG + NIRS dataset into 2.4 s time windows, and then extracting band-power based features from these segmented signals. A classification algorithm, specifically the k-nearest neighbor algorithm, was then used to classify the features. The result was a remarkable classification accuracy (CA) of 95.54%±1.31 when using the active brain region within the hybrid model. These results underline the effectiveness of the proposed approach, as it outperformed both standalone EEG and NIRS modalities in terms of CA by 5.19% and 40.90%, respectively. Furthermore, the results confirm the considerable potential of the method in classifying EEG + NIRS signals recorded during tasks such as reading text while scrolling in different directions, including right, left, up and down. This research heralds a promising step towards enhancing the capabilities of BCI systems by harnessing the synergistic power of EEG and NIRS technologies.},
}

@article {pmid38544185,
year = {2024},
author = {Albán-Escobar, M and Navarrete-Arroyo, P and De la Cruz-Guevara, DR and Tobar-Quevedo, J},
title = {Assistance Device Based on SSVEP-BCI Online to Control a 6-DOF Robotic Arm.},
journal = {Sensors (Basel, Switzerland)},
volume = {24},
number = {6},
pages = {},
pmid = {38544185},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; *Robotic Surgical Procedures ; Electroencephalography/methods ; *Self-Help Devices ; Evoked Potentials, Visual ; Photic Stimulation ; },
abstract = {This paper explores the potential benefits of integrating a brain-computer interface (BCI) utilizing the visual-evoked potential paradigm (SSVEP) with a six-degrees-of-freedom (6-DOF) robotic arm to enhance rehabilitation tools. The SSVEP-BCI employs electroencephalography (EEG) as a method of measuring neural responses inside the occipital lobe in reaction to pre-established visual stimulus frequencies. The BCI offline and online studies yielded accuracy rates of 75% and 83%, respectively, indicating the efficacy of the system in accurately detecting and capturing user intent. The robotic arm achieves planar motion by utilizing a total of five control frequencies. The results of this experiment exhibited a high level of precision and consistency, as indicated by the recorded values of ±0.85 and ±1.49 cm for accuracy and repeatability, respectively. Moreover, during the performance tests conducted with the task of constructing a square within each plane, the system demonstrated accuracy of 79% and 83%. The use of SSVEP-BCI and a robotic arm together shows promise and sets a solid foundation for the development of assistive technologies that aim to improve the health of people with amyotrophic lateral sclerosis, spina bifida, and other related diseases.},
}

Humans
*Brain-Computer Interfaces
*Robotic Surgical Procedures
Electroencephalography/methods
*Self-Help Devices
Evoked Potentials, Visual
Photic Stimulation

@article {pmid38541720,
year = {2024},
author = {Pais-Vieira, C and Figueiredo, JG and Perrotta, A and Matos, D and Aguiar, M and Ramos, J and Gato, M and Poleri, T and Pais-Vieira, M},
title = {Activation of a Rhythmic Lower Limb Movement Pattern during the Use of a Multimodal Brain-Computer Interface: A Case Study of a Clinically Complete Spinal Cord Injury.},
journal = {Life (Basel, Switzerland)},
volume = {14},
number = {3},
pages = {},
pmid = {38541720},
issn = {2075-1729},
support = {UIDP/04501/2020, UIDB/04279/2020, FCT/IF/00098/2015, project CISUC -UID/CEC/00326/2020, and via the doctoral scholarship 2023.02051.BD//Fundação para a Ciência e Tecnologia/ ; 95/2016//Bial (Portugal)/ ; (grant agreement No. 779963)//European Union's Horizon 2020 Research and Innovation Programme, via an Open Call issued and executed under Project EUROBENCH (grant agreement No. 779963) Thertact-Fb and Thertact- NEXT/ ; MC-12-2018//SANTA CASA Prémios Neurociências Melo e Castro/ ; },
abstract = {Brain-computer interfaces (BCIs) that integrate virtual reality with tactile feedback are increasingly relevant for neurorehabilitation in spinal cord injury (SCI). In our previous case study employing a BCI-based virtual reality neurorehabilitation protocol, a patient with complete T4 SCI experienced reduced pain and emergence of non-spastic lower limb movements after 10 sessions. However, it is still unclear whether these effects can be sustained, enhanced, and replicated, as well as the neural mechanisms that underlie them. The present report outlines the outcomes of extending the previous protocol with 24 more sessions (14 months, in total). Clinical, behavioral, and neurophysiological data were analyzed. The protocol maintained or reduced pain levels, increased self-reported quality of life, and was frequently associated with the appearance of non-spastic lower limb movements when the patient was engaged and not experiencing stressful events. Neural activity analysis revealed that changes in pain were encoded in the theta frequency band by the left frontal electrode F3. Examination of the lower limbs revealed alternating movements resembling a gait pattern. These results suggest that sustained use of this BCI protocol leads to enhanced quality of life, reduced and stable pain levels, and may result in the emergence of rhythmic patterns of lower limb muscle activity reminiscent of gait.},
}

@article {pmid38539656,
year = {2024},
author = {Yao, X and Li, T and Ding, P and Wang, F and Zhao, L and Gong, A and Nan, W and Fu, Y},
title = {Emotion Classification Based on Transformer and CNN for EEG Spatial-Temporal Feature Learning.},
journal = {Brain sciences},
volume = {14},
number = {3},
pages = {},
pmid = {38539656},
issn = {2076-3425},
support = {62376112, 82172058, 81771926, 61763022, and 62006246.//National Natural Science Foundation of China/ ; },
abstract = {OBJECTIVES: The temporal and spatial information of electroencephalogram (EEG) signals is crucial for recognizing features in emotion classification models, but it excessively relies on manual feature extraction. The transformer model has the capability of performing automatic feature extraction; however, its potential has not been fully explored in the classification of emotion-related EEG signals. To address these challenges, the present study proposes a novel model based on transformer and convolutional neural networks (TCNN) for EEG spatial-temporal (EEG ST) feature learning to automatic emotion classification.

METHODS: The proposed EEG ST-TCNN model utilizes position encoding (PE) and multi-head attention to perceive channel positions and timing information in EEG signals. Two parallel transformer encoders in the model are used to extract spatial and temporal features from emotion-related EEG signals, and a CNN is used to aggregate the EEG's spatial and temporal features, which are subsequently classified using Softmax.

RESULTS: The proposed EEG ST-TCNN model achieved an accuracy of 96.67% on the SEED dataset and accuracies of 95.73%, 96.95%, and 96.34% for the arousal-valence, arousal, and valence dimensions, respectively, for the DEAP dataset.

CONCLUSIONS: The results demonstrate the effectiveness of the proposed ST-TCNN model, with superior performance in emotion classification compared to recent relevant studies.

SIGNIFICANCE: The proposed EEG ST-TCNN model has the potential to be used for EEG-based automatic emotion recognition.},
}

@article {pmid38539622,
year = {2024},
author = {Kuang, M and Zhan, Z and Gao, S},
title = {Natural Image Reconstruction from fMRI Based on Node-Edge Interaction and Multi-Scale Constraint.},
journal = {Brain sciences},
volume = {14},
number = {3},
pages = {},
pmid = {38539622},
issn = {2076-3425},
support = {2019SCUH0007//Sichuan University Innovation Spark Project/ ; },
abstract = {Reconstructing natural stimulus images using functional magnetic resonance imaging (fMRI) is one of the most challenging problems in brain decoding and is also the crucial component of a brain-computer interface. Previous methods cannot fully exploit the information about interactions among brain regions. In this paper, we propose a natural image reconstruction method based on node-edge interaction and a multi-scale constraint. Inspired by the extensive information interactions in the brain, a novel graph neural network block with node-edge interaction (NEI-GNN block) is presented, which can adequately model the information exchange between brain areas via alternatively updating the nodes and edges. Additionally, to enhance the quality of reconstructed images in terms of both global structure and local detail, we employ a multi-stage reconstruction network that restricts the reconstructed images in a coarse-to-fine manner across multiple scales. Qualitative experiments on the generic object decoding (GOD) dataset demonstrate that the reconstructed images contain accurate structural information and rich texture details. Furthermore, the proposed method surpasses the existing state-of-the-art methods in terms of accuracy in the commonly used n-way evaluation. Our approach achieves 82.00%, 59.40%, 45.20% in n-way mean squared error (MSE) evaluation and 83.50%, 61.80%, 46.00% in n-way structural similarity index measure (SSIM) evaluation, respectively. Our experiments reveal the importance of information interaction among brain areas and also demonstrate the potential for developing visual-decoding brain-computer interfaces.},
}

@article {pmid38539605,
year = {2024},
author = {Gu, X and Jiang, L and Chen, H and Li, M and Liu, C},
title = {Exploring Brain Dynamics via EEG and Steady-State Activation Map Networks in Music Composition.},
journal = {Brain sciences},
volume = {14},
number = {3},
pages = {},
pmid = {38539605},
issn = {2076-3425},
support = {2023YFC3604100//the National Key Research and Development Program of China/ ; YG2021169//the Art Planning Program of Jiangxi Province/ ; YS22247//Jiangxi University Humanities and Social Science Program/ ; },
abstract = {In recent years, the integration of brain-computer interface technology and neural networks in the field of music generation has garnered widespread attention. These studies aimed to extract individual-specific emotional and state information from electroencephalogram (EEG) signals to generate unique musical compositions. While existing research has focused primarily on brain regions associated with emotions, this study extends this research to brain regions related to musical composition. To this end, a novel neural network model incorporating attention mechanisms and steady-state activation mapping (SSAM) was proposed. In this model, the self-attention module enhances task-related information in the current state matrix, while the extended attention module captures the importance of state matrices over different time frames. Additionally, a convolutional neural network layer is used to capture spatial information. Finally, the ECA module integrates the frequency information learned by the model in each of the four frequency bands, mapping these by learning their complementary frequency information into the final attention representation. Evaluations conducted on a dataset specifically constructed for this study revealed that the model surpassed representative models in the emotion recognition field, with recognition rate improvements of 1.47% and 3.83% for two different music states. Analysis of the attention matrix indicates that the left frontal lobe and occipital lobe are the most critical brain regions in distinguishing between 'recall and creation' states, while FP1, FPZ, O1, OZ, and O2 are the electrodes most related to this state. In our study of the correlations and significances between these areas and other electrodes, we found that individuals with musical training exhibit more extensive functional connectivity across multiple brain regions. This discovery not only deepens our understanding of how musical training can enhance the brain's ability to work in coordination but also provides crucial guidance for the advancement of brain-computer music generation technologies, particularly in the selection of key brain areas and electrode configurations. We hope our research can guide the work of EEG-based music generation to create better and more personalized music.},
}