@article {pmid40096116,
year = {2025},
author = {González-España, JJ and Sánchez-Rodríguez, L and Pacheco-Ramírez, MA and Feng, J and Nedley, K and Chang, SH and Francisco, GE and Contreras-Vidal, JL},
title = {At-Home Stroke Neurorehabilitation: Early Findings with the NeuroExo BCI System.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
doi = {10.3390/s25051322},
pmid = {40096116},
issn = {1424-8220},
support = {1827769//National Science Foundation/ ; 2137255//NSF Industry-University Cooperative Research Center for Building Reliable Advances and Innovations in Neurotechnology (IUCRC BRAIN)/ ; },
mesh = {Humans ; *Brain-Computer Interfaces ; *Stroke Rehabilitation/methods/instrumentation ; Male ; Female ; Middle Aged ; *Neurological Rehabilitation/methods/instrumentation ; *Exoskeleton Device ; Aged ; Stroke/physiopathology ; Adult ; Robotics/methods ; Electroencephalography/methods ; },
abstract = {BACKGROUND: Democratized access to safe and effective robotic neurorehabilitation for stroke survivors requires innovative, affordable solutions that can be used not only in clinics but also at home. This requires the high usability of the devices involved to minimize costs associated with support from physical therapists or technicians.

METHODS: This paper describes the early findings of the NeuroExo brain-machine interface (BMI) with an upper-limb robotic exoskeleton for stroke neurorehabilitation. This early feasibility study consisted of a six-week protocol, with an initial training and BMI calibration phase at the clinic followed by 60 sessions of neuromotor therapy at the homes of the participants. Pre- and post-assessments were used to assess users' compliance and system performance.

RESULTS: Participants achieved a compliance rate between 21% and 100%, with an average of 69%, while maintaining adequate signal quality and a positive perceived BMI performance during home usage with an average Likert scale score of four out of five. Moreover, adequate signal quality was maintained for four out of five participants throughout the protocol. These findings provide valuable insights into essential components for comprehensive rehabilitation therapy for stroke survivors. Furthermore, linear mixed-effects statistical models showed a significant reduction in trial duration (p-value < 0.02) and concomitant changes in brain patterns (p-value < 0.02).

CONCLUSIONS: the analysis of these findings suggests that a low-cost, safe, simple-to-use BMI system for at-home stroke rehabilitation is feasible.},
}

Humans
*Brain-Computer Interfaces
*Stroke Rehabilitation/methods/instrumentation
Male
Female
Middle Aged
*Neurological Rehabilitation/methods/instrumentation
*Exoskeleton Device
Aged
Stroke/physiopathology
Adult
Robotics/methods
Electroencephalography/methods

@article {pmid40096108,
year = {2025},
author = {Alexopoulou, A and Pergantis, P and Koutsojannis, C and Triantafillou, V and Drigas, A},
title = {Non-Invasive BCI-VR Applied Protocols as Intervention Paradigms on School-Aged Subjects with ASD: A Systematic Review.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
doi = {10.3390/s25051342},
pmid = {40096108},
issn = {1424-8220},
mesh = {Humans ; *Brain-Computer Interfaces ; Child ; *Autism Spectrum Disorder/therapy ; Adolescent ; Electroencephalography/methods ; Virtual Reality ; User-Computer Interface ; Cognition/physiology ; },
abstract = {This paper aims to highlight non-invasive BCI-VR applied protocols as intervention paradigms on school-aged subjects with ASD. Computer-based interventions are considered appropriate for users with ASD as concentration on a screen reduces other stimuli from the environment that are likely to be distracting or disruptive. Since there are no social conditions for engagement in such processes and the responses of computing systems do not hold surprises for users, as the outputs are fully controlled, they are ideal for ASD subjects. Children and adolescents with ASD, when supported by BCI interventions through virtual reality applications, especially appear to show significant improvements in core symptoms, such as cognitive and social deficits, regardless of their age or IQ. We examined nine protocols applied from 2016 to 2023, focusing on the BCI paradigms, the procedure, and the outcomes. Our study is non-exhaustive but representative of the state of the art in the field. As concluded by the research, BCI-VR applied protocols have no side effects and are rather easy to handle and maintain, and despite the fact that there are research limitations, they hold promise as a tool for improving social and cognitive skills in school-aged individuals with ASD.},
}

Humans
*Brain-Computer Interfaces
Child
*Autism Spectrum Disorder/therapy
Adolescent
Electroencephalography/methods
Virtual Reality
User-Computer Interface
Cognition/physiology

@article {pmid40096020,
year = {2025},
author = {Vafaei, E and Hosseini, M},
title = {Transformers in EEG Analysis: A Review of Architectures and Applications in Motor Imagery, Seizure, and Emotion Classification.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
doi = {10.3390/s25051293},
pmid = {40096020},
issn = {1424-8220},
mesh = {*Electroencephalography/methods ; Humans ; *Emotions/physiology ; *Seizures/physiopathology/diagnosis ; Machine Learning ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {Transformers have rapidly influenced research across various domains. With their superior capability to encode long sequences, they have demonstrated exceptional performance, outperforming existing machine learning methods. There has been a rapid increase in the development of transformer-based models for EEG analysis. The high volumes of recently published papers highlight the need for further studies exploring transformer architectures, key components, and models employed particularly in EEG studies. This paper aims to explore four major transformer architectures: Time Series Transformer, Vision Transformer, Graph Attention Transformer, and hybrid models, along with their variants in recent EEG analysis. We categorize transformer-based EEG studies according to the most frequent applications in motor imagery classification, emotion recognition, and seizure detection. This paper also highlights the challenges of applying transformers to EEG datasets and reviews data augmentation and transfer learning as potential solutions explored in recent years. Finally, we provide a summarized comparison of the most recent reported results. We hope this paper serves as a roadmap for researchers interested in employing transformer architectures in EEG analysis.},
}

*Electroencephalography/methods
Humans
*Emotions/physiology
*Seizures/physiopathology/diagnosis
Machine Learning
Algorithms
Signal Processing, Computer-Assisted

@article {pmid40096019,
year = {2025},
author = {Koo, BH and Siu, HC and Newman, DJ and Roche, ET and Petersen, LG},
title = {Utilization of Classification Learning Algorithms for Upper-Body Non-Cyclic Motion Prediction.},
journal = {Sensors (Basel, Switzerland)},
volume = {25},
number = {5},
pages = {},
doi = {10.3390/s25051297},
pmid = {40096019},
issn = {1424-8220},
mesh = {Humans ; *Electromyography/methods ; *Algorithms ; Male ; Adult ; Movement/physiology ; Neural Networks, Computer ; Muscle, Skeletal/physiology ; Female ; Young Adult ; Deep Learning ; Motion ; Signal Processing, Computer-Assisted ; Machine Learning ; },
abstract = {This study explores two methods of predicting non-cyclic upper-body motions using classification algorithms. Exoskeletons currently face challenges with low fluency, hypothesized to be in part caused by the lag in active control innate in many leader-follower paradigms seen in today's systems, leading to energetic inefficiencies and discomfort. To address this, we employ k-nearest neighbor (KNN) and deep learning models to predict motion characteristics, such as magnitude and category, from surface electromyography (sEMG) signals. Data were collected from six muscles located around the elbow. The sEMG signals were processed to identify significant activation changes. Two classification approaches were utilized: a KNN algorithm that categorizes motion based on the slopes of processed sEMG signals at change points and a deep neural network employing continuous categorization. Both methods demonstrated the capability to predict future voluntary non-cyclic motions up to and beyond commonly acknowledged electromechanical delay times, with the deep learning model able to predict, with certainty at or beyond 90%, motion characteristics even prior to myoelectric activation of the muscles involved. Our findings indicate that these classification algorithms can be used to predict upper-body non-cyclic motions to potentially increase machine interfacing fluency. Further exploration into regression-based prediction models could enhance the precision of these predictions, and further work could explore their effects on fluency when utilized in a tandem or wearable robotic application.},
}

Humans
*Electromyography/methods
*Algorithms
Male
Adult
Movement/physiology
Neural Networks, Computer
Muscle, Skeletal/physiology
Female
Young Adult
Deep Learning
Motion
Signal Processing, Computer-Assisted
Machine Learning

@article {pmid40095346,
year = {2025},
author = {Li, J and Shao, N and Zhang, Y and Liu, X and Zhang, H and Tian, L and Piatkevich, KD and Zhang, D and Lee, HJ},
title = {Screening of Vibrational Spectroscopic Voltage Indicator by Stimulated Raman Scattering Microscopy.},
journal = {Small methods},
volume = {},
number = {},
pages = {e2402124},
doi = {10.1002/smtd.202402124},
pmid = {40095346},
issn = {2366-9608},
support = {82372011//National Natural Science Foundation of China/ ; 12074339//National Natural Science Foundation of China/ ; 32050410298//National Natural Science Foundation of China/ ; 32171093//National Natural Science Foundation of China/ ; LZ25H180001//Natural Science Foundation of Zhejiang Province/ ; 2025ZFJH01-01//Fundamental Research Funds for the Central Universities/ ; 2024YFA1408900//National Key Research and Development Program of China/ ; 28961//Brain and Behavior Research Foundation/ ; },
abstract = {Genetically encoded voltage indicators (GEVIs) have significantly advanced voltage imaging, offering spatial details at cellular and subcellular levels not easily accessible with electrophysiology. In addition to fluorescence imaging, certain chemical bond vibrations are sensitive to membrane potential changes, presenting an alternative imaging strategy; however, challenges in signal sensitivity and membrane specificity highlight the need to develop vibrational spectroscopic GEVIs (vGEVIs) in mammalian cells. To address this need, a vGEVI screening approach is developed that employs hyperspectral stimulated Raman scattering (hSRS) imaging synchronized with an induced transmembrane voltage (ITV) stimulation, revealing unique spectroscopic signatures of sensors expressed on membranes. Specifically, by screening various rhodopsin-based voltage sensors in live mammalian cells, a characteristic peak associated with retinal bound to the sensor is identified in one of the GEVIs, Archon, which exhibited a 70 cm[-1] red shift relative to the membrane-bound retinal. Notably, this peak is responsive to changes in membrane potential. Overall, hSRS-ITV presents a promising platform for screening vGEVIs, paving the way for advancements in vibrational spectroscopic voltage imaging.},
}

@article {pmid40093990,
year = {2025},
author = {Yang, KC and Xu, Y and Lin, Q and Tang, LL and Zhong, JW and An, HN and Zeng, YQ and Jia, K and Jin, Y and Yu, G and Gao, F and Zhao, L and Tong, LS},
title = {Explainable deep learning algorithm for identifying cerebral venous sinus thrombosis-related hemorrhage (CVST-ICH) from spontaneous intracerebral hemorrhage using computed tomography.},
journal = {EClinicalMedicine},
volume = {81},
number = {},
pages = {103128},
pmid = {40093990},
issn = {2589-5370},
abstract = {BACKGROUND: Misdiagnosis of hemorrhage secondary to cerebral venous sinus thrombosis (CVST-ICH) as arterial-origin spontaneous intracerebral hemorrhage (sICH) can lead to inappropriate treatment and the potential for severe adverse outcomes. The current practice for identifying CVST-ICH involves venography, which, despite being increasingly utilized in many centers, is not typically used as the initial imaging modality for ICH patients. The study aimed to develop an explainable deep learning model to quickly identify ICH caused by CVST based on non-contrast computed tomography (NCCT).

METHODS: The study population included patients diagnosed with CVST-ICH and other spontaneous ICH from January 2016 to March 2023 at the Second Affiliated Hospital of Zhejiang University, Taizhou First People's Hospital, Taizhou Hospital, Quzhou Second People's Hospital, and Longyan First People's Hospital. A transfer learning-based 3D U-Net with segmentation and classification was proposed and developed only on admission plain CT. Model performance was assessed using the area under the curve (AUC), sensitivity, and specificity metrics. For further evaluation, the average diagnostic performance of nine doctors on plain CT was compared with model assistance. Interpretability methods, including Grad-CAM++, SHAP, IG, and occlusion, were employed to understand the model's attention.

FINDINGS: An internal dataset was constructed using propensity score matching based on age, initially including 102 CVST-ICH patients (median age: 44 [29, 61] years) and 683 sICH patients (median age: 65 [52, 73] years). After matching, 102 CVST-ICH patients and 306 sICH patients (median age: 50 [40, 62] years) were selected. An external dataset consisted of 38 CVST-ICH and 119 sICH patients from four other hospitals. Validation showed AUC 0·94, sensitivity 0·96, and specificity 0·8 for the internal testing subset; AUC 0·85, sensitivity 0·87, and specificity 0·82 for the external dataset, respectively. The discrimination performance of nine doctors interpreting CT images significantly improved with the assistance of the proposed model (accuracy 0·79 vs 0·71, sensitivity 0·88 vs 0·81, specificity 0·75 vs 0·68, p < 0·05). Interpretability methods highlighted the attention of model to the features of hemorrhage edge appearance.

INTERPRETATION: The present model demonstrated high-performing and robust results on discrimination between CVST-ICH and spontaneous ICH, and aided doctors' diagnosis in clinical practice as well. Prospective validation with larger-sample size is required.

FUNDING: The work was funded by the National Key R&D Program of China (2023YFE0118900), National Natural Science Foundation of China (No.81971155 and No.81471168), the Science and Technology Department of Zhejiang Province (LGJ22H180004), Medical and Health Science and Technology Project of Zhejiang Province (No.2022KY174), the 'Pioneer' R&D Program of Zhejiang (No. 2024C03006 and No. 2023C03026) and the MOE Frontier Science Center for Brain Science & Brain-Machine Integration, Zhejiang University.},
}

@article {pmid40092993,
year = {2025},
author = {Wen, J and Li, Y and Deng, W and Li, Z},
title = {Central nervous system and immune cells interactions in cancer: unveiling new therapeutic avenues.},
journal = {Frontiers in immunology},
volume = {16},
number = {},
pages = {1528363},
pmid = {40092993},
issn = {1664-3224},
mesh = {Humans ; *Neoplasms/immunology/therapy/drug therapy ; *Central Nervous System/immunology ; *Tumor Microenvironment/immunology ; Animals ; Cell Communication/immunology ; Signal Transduction ; Neuroimmunomodulation ; Immunotherapy/methods ; Immune Checkpoint Inhibitors/therapeutic use/pharmacology ; },
abstract = {Cancer remains a leading cause of mortality worldwide. Despite significant advancements in cancer research, our understanding of its complex developmental pathways remains inadequate. Recent research has clarified the intricate relationship between the central nervous system (CNS) and cancer, particularly how the CNS influences tumor growth and metastasis via regulating immune cell activity. The interactions between the central nervous system and immune cells regulate the tumor microenvironment via various signaling pathways, cytokines, neuropeptides, and neurotransmitters, while also incorporating processes that alter the tumor immunological landscape. Furthermore, therapeutic strategies targeting neuro-immune cell interactions, such as immune checkpoint inhibitors, alongside advanced technologies like brain-computer interfaces and nanodelivery systems, exhibit promise in improving treatment efficacy. This complex bidirectional regulatory network significantly affects tumor development, metastasis, patient immune status, and therapy responses. Therefore, understanding the mechanisms regulating CNS-immune cell interactions is crucial for developing innovative therapeutic strategies. This work consolidates advancements in CNS-immune cell interactions, evaluates their potential in cancer treatment strategies, and provides innovative insights for future research and therapeutic approaches.},
}

Humans
*Neoplasms/immunology/therapy/drug therapy
*Central Nervous System/immunology
*Tumor Microenvironment/immunology
Animals
Cell Communication/immunology
Signal Transduction
Neuroimmunomodulation
Immunotherapy/methods
Immune Checkpoint Inhibitors/therapeutic use/pharmacology

@article {pmid40092069,
year = {2025},
author = {Sayal, A and Direito, B and Sousa, T and Singer, N and Castelo-Branco, M},
title = {Music in the loop: a systematic review of current neurofeedback methodologies using music.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1515377},
pmid = {40092069},
issn = {1662-4548},
abstract = {Music, a universal element in human societies, possesses a profound ability to evoke emotions and influence mood. This systematic review explores the utilization of music to allow self-control of brain activity and its implications in clinical neuroscience. Focusing on music-based neurofeedback studies, it explores methodological aspects and findings to propose future directions. Three key questions are addressed: the rationale behind using music as a stimulus, its integration into the feedback loop, and the outcomes of such interventions. While studies emphasize the emotional link between music and brain activity, mechanistic explanations are lacking. Additionally, there is no consensus on the imaging or behavioral measures of neurofeedback success. The review suggests considering whole-brain neural correlates of music stimuli and their interaction with target brain networks and reward mechanisms when designing music-neurofeedback studies. Ultimately, this review aims to serve as a valuable resource for researchers, facilitating a deeper understanding of music's role in neurofeedback and guiding future investigations.},
}

@article {pmid40091139,
year = {2025},
author = {Thamaraimanalan, T and Gopal, D and Vignesh, S and Kishore Kumar, K},
title = {Exploiting adaptive neuro-fuzzy inference systems for cognitive patterns in multimodal brain signal analysis.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {9029},
pmid = {40091139},
issn = {2045-2322},
mesh = {Humans ; *Fuzzy Logic ; *Electroencephalography/methods ; *Brain/physiology ; *Cognition/physiology ; *Principal Component Analysis ; Neural Networks, Computer ; Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {The analysis of cognitive patterns through brain signals offers critical insights into human cognition, including perception, attention, memory, and decision-making. However, accurately classifying these signals remains a challenge due to their inherent complexity and non-linearity. This study introduces a novel method, PCA-ANFIS, which integrates Principal Component Analysis (PCA) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), to enhance cognitive pattern recognition in multimodal brain signal analysis. PCA reduces the dimensionality of EEG data while retaining salient features, enabling computational efficiency. ANFIS combines the adaptability of neural networks with the interpretability of fuzzy logic, making it well-suited to model the non-linear relationships within brain signals. Performance metrics of our proposed method, such as accuracy, sensitivity, and computational efficiency. These additions highlight the effectiveness of the method and provide a concise summary of the findings. The proposed method achieves superior classification performance, with an unprecedented accuracy of 99.5%, significantly outperforming existing approaches. Comprehensive experiments were conducted using a diverse multimodal EEG dataset, demonstrating the method's robustness and sensitivity. The integration of PCA and ANFIS addresses key challenges in multimodal brain signal analysis, such as EEG artifact contamination and non-stationarity, ensuring reliable feature extraction and classification. This research has significant implications for both cognitive neuroscience and clinical practice. By advancing the understanding of cognitive processes, the PCA-ANFIS method facilitates accurate diagnosis and treatment of cognitive disorders and neurological conditions. Future work will focus on testing the approach with larger and more diverse datasets and exploring its applicability in domains such as neurofeedback, neuromarketing, and brain-computer interfaces. This study establishes PCA-ANFIS as a capable tool for the precise and efficient classification of cognitive patterns in brain signal processing.},
}

Humans
*Fuzzy Logic
*Electroencephalography/methods
*Brain/physiology
*Cognition/physiology
*Principal Component Analysis
Neural Networks, Computer
Signal Processing, Computer-Assisted
Algorithms

@article {pmid40089693,
year = {2025},
author = {Khamisa, N and Madala, S and Fonka, CB},
title = {Burnout among South African nurses during the peak of COVID-19 pandemic: a holistic investigation.},
journal = {BMC nursing},
volume = {24},
number = {1},
pages = {290},
pmid = {40089693},
issn = {1472-6955},
abstract = {BACKGROUND: The wellbeing of health care workers (HCWs) has been an ongoing challenge, especially within low and middle-income countries (LMICs) such as South Africa. Evidence suggesting that HCWs are increasingly stressed and burned out is cause for concern. Nurses in particular have been impacted physically, mentally and psychosocially during the recent COVID-19 pandemic. This may leave a disproportionate consequence, affecting various aspects of their wellbeing, thereby justifying a need for a more holistic investigation of the wellbeing of South African nurses and their coping mechanisms during the peak of the pandemic.

METHODS: This was a cross-sectional study design. Online self-reported questionnaires were administered in six hospitals, sampled purposively and conveniently from three South African provinces. Using STATA 18.0, the Wilcoxon Ranksum test at 5% alpha compared the wellbeing and coping mechanisms of nursing staff and nursing management during COVID-19's peak. Univariable and multivariable linear regression analyses were performed to determine factors associated with burnout in nurses, at a 95% confidence interval (CI). Validated scales measuring burnout, coping, resilience, as well as mental and physical health were utilised.

RESULTS: Of 139 participants, 112(97.4%) were females, with 91(82%) and 20(18%) being nursing staff and management respectively. The median age of the participants was 43.3 years (n = 112), with a practising duration of 12 years (n = 111). There was a significant difference in the burnout score between nursing staff and nursing management (p = 0.028). In the univariable linear regression model, burnout was significantly (p < 0.05) associated with the Brief COPE Inventory (BCI), Conor-Davidson Resilience Scale (CDRS), Global Mental and Health Scale (GMHS), Global Physical and Health Scale (GPHS) and Hospital Anxiety and Depression Scale (HADS), as well as occupation. In the multivariable linear regression model, burnout was significantly associated with the CDRS [Coeff.=0.7, 95%CI 0.4; 0.9], GMHS [Coeff.=-2.4, 95%CI -3.2; -1.6], GPHS [Coeff.2.1, 95%CI 1.3; 2.9], and HADS [Coeff.=0.7, 95%CI 0.2; 1.2].

CONCLUSION: Investigating multiple aspects of wellbeing in this study, it's shown that coping and resilience may not be key factors in promoting the wellbeing of South African nurses. However, effective mental health interventions are crucial and should be prioritised to mitigate burnout during future health emergencies. Future studies examining the associations between general health, coping and resilience may help generate further evidence towards holistic interventions aimed at promoting nurses' wellbeing.

CLINICAL TRIAL NUMBER: Not applicable.},
}

@article {pmid40089573,
year = {2025},
author = {Sivasakthivel, R and Rajagopal, M and Anitha, G and Loganathan, K and Abbas, M and Ksibi, A and Rao, KS},
title = {Simulating online and offline tasks using hybrid cheetah optimization algorithm for patients affected by neurodegenerative diseases.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {8951},
pmid = {40089573},
issn = {2045-2322},
mesh = {Humans ; *Algorithms ; Female ; *Neurodegenerative Diseases/physiopathology ; Male ; *Brain-Computer Interfaces ; Adult ; Neural Networks, Computer ; Electroencephalography/methods ; Middle Aged ; },
abstract = {Brain-Computer Interface (BCI) is a versatile technique to offer better communication system for people affected by the locked-in syndrome (LIS).In the current decade, there has been a growing demand for improved care and services for individuals with neurodegenerative diseases. To address this barrier, the current work is designed with four states of BCI for paralyzed persons using Welch Power Spectral Density (W-PSD). The features extracted from the signals were trained with a hybrid Feed Forward Neural Network Cheetah Optimization Algorithm (FFNNCOA) in both offline and online modes. Totally, eighteen subjects were involved in this study. The study proved that the offline analysis phase outperformed than the online phase in the real-time. The experiment was achieved the accuracies of 95.56% and 93.88% for men and female respectively. Furthermore, the study confirms that the subject's performance in the offline can manage the task more easily than in online mode.},
}

Humans
*Algorithms
Female
*Neurodegenerative Diseases/physiopathology
Male
*Brain-Computer Interfaces
Adult
Neural Networks, Computer
Electroencephalography/methods
Middle Aged

@article {pmid40087270,
year = {2025},
author = {Eby, J and Beutel, M and Koivisto, D and Achituve, I and Fetaya, E and Zariffa, J},
title = {Electromyographic typing gesture classification dataset for neurotechnological human-machine interfaces.},
journal = {Scientific data},
volume = {12},
number = {1},
pages = {440},
pmid = {40087270},
issn = {2052-4463},
mesh = {Humans ; *Electromyography ; *Gestures ; Brain-Computer Interfaces ; },
abstract = {Neurotechnological interfaces have the potential to create new forms of human-machine interactions, by allowing devices to interact directly with neurological signals instead of via intermediates such as keystrokes. Surface electromyography (sEMG) has been used extensively in myoelectric control systems, which use bioelectric activity recorded from muscles during contractions to classify actions. This technology has been used primarily for rehabilitation applications. In order to support the development of myoelectric interfaces for a broader range of human-machine interactions, we present an sEMG dataset obtained during key presses in a typing task. This fine-grained classification dataset consists of 16-channel bilateral sEMG recordings and key logs, collected from 19 individuals in two sessions on different days. We report baseline results on intra-session, inter-session and inter-subject evaluations. Our baseline results show that within-session accuracy is relatively high, even with simple learning models. However, the results on between-session and between-participant are much lower, showing that generalizing between sessions and individuals is an open challenge.},
}

Humans
*Electromyography
*Gestures
Brain-Computer Interfaces

@article {pmid40086264,
year = {2025},
author = {Jiang, Y and Zhou, C and Zhao, J and Ren, X and Wang, Q and Ni, P and Li, T},
title = {Derivation of human-derived iPSC line from a male adolescent with first-episode of sporadic schizophrenia.},
journal = {Stem cell research},
volume = {85},
number = {},
pages = {103694},
doi = {10.1016/j.scr.2025.103694},
pmid = {40086264},
issn = {1876-7753},
abstract = {Schizophrenia is considered to be a neurodevelopmental disorder with high heritability. In this study, peripheral blood mononuclear cells (PBMCs) were collected from a male adolescent diagnosed with first-episode of sporadic schizophrenia. Induced pluripotent stem cells (iPSCs) were generated by reprogramming using the factors OCT4, SOX2, NANOG, LIN28, c-MYC, KLF4, and SV40LT. The generated iPSC line was validated by karyotype analysis and expression of pluripotency markers. These iPSCs were capable of differentiating into derivatives of all three germ layers in vivo.},
}

@article {pmid40085468,
year = {2025},
author = {Chen, J and Yang, H and Xia, Y and Gong, T and Thomas, A and Liu, J and Chen, W and Carlson, T and Zhao, H},
title = {Simultaneous Mental Fatigue and Mental Workload Assessment with Wearable High-Density Diffuse Optical Tomography.},
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.2025.3551676},
pmid = {40085468},
issn = {1558-0210},
abstract = {Accurately assessing mental states-such as mental workload and fatigue- is crucial for ensuring the reliability and effectiveness of brain-computer interface (BCI)-based applications. Relying on signals from a limited brain region with low spatial resolution may fail to capture the full scope of relevant information. To address this, high-density diffuse optical tomography (HD-DOT), an emerging form of functional near-infrared spectroscopy (fNIRS) was employed in this study, which provides higher spatial resolution for hemodynamic measurements and enables the reconstruction of 3D brain images. An experiment protocol was designed to investigate both mental workload and fatigue, two critical components of cognitive state that often fluctuate concurrently in real-world scenarios. Machine learning methods were applied for subject-specific classification, achieving 95.14% mean accuracy for fatigue/non-fatigue and 97.93% for four n-back tasks using Random Forest, outperforming Support Vector Machines. These results highlight the transformative potential of HD-DOT in advancing multifaceted cognitive state assessment, paving the way for more precise, adaptable, and powerful BCI applications.},
}

@article {pmid40084138,
year = {2025},
author = {Kaiju, T and Inoue, M and Hirata, M and Suzuki, T},
title = {Compact and low-power wireless headstage for electrocorticography recording of freely moving primates in a home cage.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1491844},
pmid = {40084138},
issn = {1662-4548},
abstract = {OBJECTIVE: Wireless electrocorticography (ECoG) recording from unrestrained nonhuman primates during behavioral tasks is a potent method for investigating higher-order brain functions over extended periods. However, conventional wireless neural recording devices have not been optimized for ECoG recording, and few devices have been tested on freely moving primates engaged in behavioral tasks within their home cages.

METHODS: We developed a compact, low-power, 32-channel wireless ECoG headstage specifically designed for neuroscience research. To evaluate its efficacy, we established a behavioral task setup within a home cage environment.

RESULTS: The developed headstage weighed merely 1.8 g and had compact dimensions of 25 mm × 16 mm × 4 mm. It was efficiently powered by a 100-mAh battery (weighing 3 g), enabling continuous recording for 8.5 h. The device successfully recorded data from an unrestrained monkey performing a center-out joystick task within its home cage.

CONCLUSION: The device demonstrated excellent capability for recording ECoG data from freely moving primates in a home cage environment. This versatile device enhances task design freedom, decrease researchers' workload, and enhances data collection efficiency.},
}

@article {pmid40083893,
year = {2025},
author = {Gordienko, Y and Gordienko, N and Taran, V and Rojbi, A and Telenyk, S and Stirenko, S},
title = {Effect of natural and synthetic noise data augmentation on physical action classification by brain-computer interface and deep learning.},
journal = {Frontiers in neuroinformatics},
volume = {19},
number = {},
pages = {1521805},
pmid = {40083893},
issn = {1662-5196},
abstract = {Analysis of electroencephalography (EEG) signals gathered by brain-computer interface (BCI) recently demonstrated that deep neural networks (DNNs) can be effectively used for investigation of time sequences for physical actions (PA) classification. In this study, the relatively simple DNN with fully connected network (FCN) components and convolutional neural network (CNN) components was considered to classify finger-palm-hand manipulations each from the grasp-and-lift (GAL) dataset. The main aim of this study was to imitate and investigate environmental influence by the proposed noise data augmentation (NDA) of two kinds: (i) natural NDA by inclusion of noise EEG data from neighboring regions by increasing the sampling size N and the different offset values for sample labeling and (ii) synthetic NDA by adding the generated Gaussian noise. The natural NDA by increasing N leads to the higher micro and macro area under the curve (AUC) for receiver operating curve values for the bigger N values than usage of synthetic NDA. The detrended fluctuation analysis (DFA) was applied to investigate the fluctuation properties and calculate the correspondent Hurst exponents H for the quantitative characterization of the fluctuation variability. H values for the low time window scales (< 2 s) are higher in comparison with ones for the bigger time window scales. For example, H more than 2-3 times higher for some PAs, i.e., it means that the shorter EEG fragments (< 2 s) demonstrate the scaling behavior of the higher complexity than the longer fragments. As far as these results were obtained by the relatively small DNN with the low resource requirements, this approach can be promising for porting such models to Edge Computing infrastructures on devices with the very limited computational resources.},
}

@article {pmid40083152,
year = {2025},
author = {Liu, H and Bai, Y and Zheng, Q and Zhao, R and Guo, M and Zhu, J and Ni, G},
title = {Effects of spatial separation and background noise on brain functional connectivity during auditory selective spatial attention.},
journal = {Cerebral cortex (New York, N.Y. : 1991)},
volume = {35},
number = {3},
pages = {},
doi = {10.1093/cercor/bhaf054},
pmid = {40083152},
issn = {1460-2199},
support = {2022BKY056//Tianjin Research Innovation Project for Postgraduate Students/ ; 2023YFF1203500//National Key Research and Development Program of China/ ; },
mesh = {Humans ; Male ; *Attention/physiology ; *Brain/physiology ; Female ; Young Adult ; *Noise ; *Auditory Perception/physiology ; *Magnetic Resonance Imaging ; *Space Perception/physiology ; *Acoustic Stimulation/methods ; Adult ; Neural Pathways/physiology ; Brain Mapping/methods ; Signal-To-Noise Ratio ; },
abstract = {Auditory selective spatial attention (ASSA) plays an important role in "cocktail party" scenes, but the effects of spatial separation between target and distractor sources and background noise on the associated brain responses have not been thoroughly investigated. This study utilized the multilayer time-varying brain network to reveal the effect patterns of different separation degrees and signal-to-noise ratio (SNR) levels on brain functional connectivity during ASSA. Specifically, a multilayer time-varying brain network with six time-windows equally divided by each epoch was constructed to investigate the segregation and integration of brain functional connectivity. The results showed that the inter-layer connectivity strength was consistently lower than the intra-layer connectivity strength for various separation degrees and SNR levels. Moreover, the connectivity strength of the multilayer time-varying brain network increased with decreasing separation degrees and initially increased and subsequently decreased with decreasing SNR levels. The second time-window of the network showed the most significant variation under some conditions and was determined as the core layer. The topology within the core layer was mainly reflected in the connectivity between the frontal and parietal-occipital cortices. In conclusion, these results suggest that spatial separation and background noise significantly modulate brain functional connectivity during ASSA.},
}

Humans
Male
*Attention/physiology
*Brain/physiology
Female
Young Adult
*Noise
*Auditory Perception/physiology
*Magnetic Resonance Imaging
*Space Perception/physiology
*Acoustic Stimulation/methods
Adult
Neural Pathways/physiology
Brain Mapping/methods
Signal-To-Noise Ratio

@article {pmid40083123,
year = {2025},
author = {Meng, M and Chen, G and Chen, S and Ma, Y and Gao, Y and Luo, Z},
title = {DGPDR: discriminative geometric perception dimensionality reduction of SPD matrices on Riemannian manifold for EEG classification.},
journal = {Computer methods in biomechanics and biomedical engineering},
volume = {},
number = {},
pages = {1-11},
doi = {10.1080/10255842.2025.2476184},
pmid = {40083123},
issn = {1476-8259},
abstract = {Manifold learning with Symmetric Positive Definite (SPD) matrices has demonstrated potential for classifying Electroencephalography (EEG) in Brain-Computer Interface (BCI) applications. However, SPD matrices may lead to crucial information loss of EEG signals. This paper proposes a dimensionality reduction method based on discriminative geometric perception on the Riemannian manifold to enhance SPD matrix discriminability. Experiments on BCI Competition IV Dataset 1 and Dataset 2a show the proposed method improves accuracy by 5.0% and 19.38% respectively, demonstrating that applying discriminative geometric perception can effectively maintain robust performance associated with the dimensionality-reduced SPD matrix.},
}

@article {pmid40082907,
year = {2025},
author = {Bonato, P and Reinkensmeyer, D and Manto, M},
title = {Two decades of breakthroughs: charting the future of NeuroEngineering and Rehabilitation.},
journal = {Journal of neuroengineering and rehabilitation},
volume = {22},
number = {1},
pages = {59},
pmid = {40082907},
issn = {1743-0003},
mesh = {Humans ; *Neurological Rehabilitation/instrumentation/trends/methods ; *Biomedical Engineering/trends/methods/instrumentation ; Periodicals as Topic/trends ; Brain-Computer Interfaces/trends ; Forecasting ; Robotics/trends/instrumentation/methods ; },
abstract = {The Journal of NeuroEngineering and Rehabilitation (JNER) has become a major actor for the dissemination of knowledge in the scientific community, bridging the gaps between innovative neuroengineering and rehabilitation. Major fields of innovations have emerged these last 25 years, such as machine learning and the ongoing AI revolution, wearable technologies, human machine interfaces, robotics, advanced prosthetics, functional electrical stimulation and various neuromodulation techniques. With the major burden of disorders impacting on the central/peripheral nervous system and the musculoskeletal system both in adults and in children, successful tailored neurorehabilitation has become a major objective for the research and clinical community at a world scale. JNER contributes to this challenging goal, publishing groundbreaking cutting-edge research using the open access model. The multidisciplinary approaches at the crossroads of biomedical engineering, neuroscience, physical medicine and rehabilitation make of the journal a unique growing platform welcoming breakthrough discoveries to reshape the field and restore function.},
}

Humans
*Neurological Rehabilitation/instrumentation/trends/methods
*Biomedical Engineering/trends/methods/instrumentation
Periodicals as Topic/trends
Brain-Computer Interfaces/trends
Forecasting
Robotics/trends/instrumentation/methods

@article {pmid40082683,
year = {2025},
author = {Verwoert, M and Amigó-Vega, J and Gao, Y and Ottenhoff, MC and Kubben, PL and Herff, C},
title = {Whole-brain dynamics of articulatory, acoustic and semantic speech representations.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {432},
pmid = {40082683},
issn = {2399-3642},
support = {17619//Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Netherlands Organisation for Scientific Research)/ ; },
mesh = {Humans ; *Speech/physiology ; Male ; *Brain/physiology ; *Semantics ; Female ; Adult ; Young Adult ; Speech Acoustics ; Brain Mapping ; },
abstract = {Speech production is a complex process that traverses several representations, from the meaning of spoken words (semantic), through the movement of articulatory muscles (articulatory) and, ultimately, to the produced audio waveform (acoustic). In this study, we identify how these different representations of speech are spatially and temporally distributed throughout the depth of the brain. Intracranial neural data is recorded from 15 participants, across 1647 electrode contacts, while overtly speaking 100 unique words. We find a bilateral spatial distribution for all three representations, with a more widespread and temporally dynamic distribution in the left compared to the right hemisphere. The articulatory and acoustic representations share a similar spatial distribution surrounding the Sylvian fissure, while the semantic representation is more widely distributed across the brain in a mostly distinct network. These results highlight the distributed nature of the speech production neural process and the potential of non-motor representations for speech brain-computer interfaces.},
}

Humans
*Speech/physiology
Male
*Brain/physiology
*Semantics
Female
Adult
Young Adult
Speech Acoustics
Brain Mapping

@article {pmid40082601,
year = {2025},
author = {Wang, Y and Yang, Z and Shi, X and Han, H and Li, AN and Zhang, B and Yuan, W and Sun, YH and Li, XM and Lian, H and Li, MD},
title = {Investigating the effect of Arvcf reveals an essential role on regulating the mesolimbic dopamine signaling-mediated nicotine reward.},
journal = {Communications biology},
volume = {8},
number = {1},
pages = {429},
pmid = {40082601},
issn = {2399-3642},
mesh = {Animals ; *Dopamine/metabolism ; *Reward ; *Nicotine/pharmacology ; Mice ; Male ; *Ventral Tegmental Area/metabolism/drug effects ; *Dopaminergic Neurons/metabolism/drug effects ; *Mice, Knockout ; *Nucleus Accumbens/metabolism/drug effects ; Signal Transduction ; Mice, Inbred C57BL ; },
abstract = {The mesolimbic dopamine system is crucial for drug reinforcement and reward learning, leading to addiction. We previously demonstrated that Arvcf was associated significantly with nicotine and alcohol addiction through genome-wide association studies. However, the role and mechanisms of Arvcf in dopamine-mediated drug reward processes were largely unknown. In this study, we first showed that Arvcf mediates nicotine-induced reward behavior by using conditioned place preference (CPP) model on Arvcf-knockout (Arvcf-KO) animal model. Then, we revealed that Arvcf was mainly expressed in VTA dopaminergic neurons whose expression could be upregulated by nicotine treatment. Subsequently, our SnRNA-seq analysis revealed that Arvcf was directly involved in dopamine biosynthesis in VTA dopaminergic neurons. Furthermore, we found that Arvcf-KO led to a significant reduction in both the dopamine synthesis and release in the nucleus accumbens (NAc) on nicotine stimulation. Specifically, we demonstrated that inhibition of Arvcf in VTA dopaminergic neurons decreased dopamine release within VTA-NAc circuit and suppressed nicotine reward-related behavior, while overexpression of Arvcf led to the opposite results. Taken together, these findings highlight the role of Arvcf in regulating dopamine signaling and reward learning, and its enhancement of dopamine release in the VTA-NAc circuit as a novel mechanism for nicotine reward.},
}

Animals
*Dopamine/metabolism
*Reward
*Nicotine/pharmacology
Mice
Male
*Ventral Tegmental Area/metabolism/drug effects
*Dopaminergic Neurons/metabolism/drug effects
*Mice, Knockout
*Nucleus Accumbens/metabolism/drug effects
Signal Transduction
Mice, Inbred C57BL

@article {pmid40082534,
year = {2025},
author = {Muniyandi, AP and Padmanandam, K and Subbaraj, K and Khadidos, AO and Khadidos, AO and Deepa, N and Selvarajan, S},
title = {An intelligent emotion prediction system using improved sand cat optimization technique based on EEG signals.},
journal = {Scientific reports},
volume = {15},
number = {1},
pages = {8782},
pmid = {40082534},
issn = {2045-2322},
mesh = {*Electroencephalography/methods ; *Emotions/physiology ; Humans ; *Algorithms ; Signal Processing, Computer-Assisted ; Brain/physiology ; Animals ; },
abstract = {Emotion recognition and prediction plays a vital role in human-computer interaction (HCI), offering more potential for efficient intuitive and adaptive systems. This presents an innovative and efficient approach for emotion prediction from electroencephalogram (EEG) signals by using an Improved Sand Cat Optimization (ISCO) technique to enhance prediction accuracy and efficiency. EEG signals directly indicates the brain activity and these signals are rich and reliable source of data for capturing emotional states. The proposed method is improved by adapting the Cat movement which uses convex lens opposition based learning technique and this will enhance the Cat movement towards target. The proposed method converges to target identification quickly for achieving efficient emotion prediction by extending the exiting Sand Cat Optimization algorithm. The algorithm has been evaluated by using openly available EEG signals dataset, which contains 2132 labelled records of three categories of emotional classes. The performance of the proposed method is compared with other nature inspired optimization algorithms such as Practical Swam Optimization (PSO), Artificial Rabbit Optimization (ARO), Artificial Bee Colony Optimization (ABCO), and Cat Optimization (CO) algorithm. The experimental evaluation shows that the proposed technique outperforms and showcases significant improvements in emotion prediction with accuracy of 97.5% compared to the other bioinspired optimization techniques. This research article has a scope to contribute to the advancement of emotion prediction system in the field of mental health care monitoring, HCI systems, gaming systems, and affective computing.},
}

*Electroencephalography/methods
*Emotions/physiology
Humans
*Algorithms
Signal Processing, Computer-Assisted
Brain/physiology
Animals

@article {pmid40081769,
year = {2025},
author = {Yin, Y and Cao, Y and Zhou, Y and Xu, Z and Luo, P and Yang, B and He, Q and Yan, H and Yang, X},
title = {Downregulation of DDIT4 levels with borneol attenuates hepatotoxicity induced by gilteritinib.},
journal = {Biochemical pharmacology},
volume = {236},
number = {},
pages = {116869},
doi = {10.1016/j.bcp.2025.116869},
pmid = {40081769},
issn = {1873-2968},
abstract = {Gilteritinib, a multi-target kinase inhibitor, is currently used as standard therapy for acute myeloid leukemia. However, approximately half of the patients encounter liver-related adverse effects during the treatment with gilteritinib, which limiting its clinical applications. The underlying mechanisms of gilteritinib-induced hepatotoxicity and the development of strategies to prevent this toxicity are not well-reported. In our study, we utilized JC-1 dye, and MitoSOX to demonstrate that gilteritinib treatment leads to hepatocytes undergoing p53-mediated mitochondrial apoptosis. Furthermore, qRT-PCR analysis revealed that DNA damage-inducible transcript 4 (DDIT4), a downstream target of p53, was upregulated following gilteritinib administration and was identified as a key factor in gilteritinib-induced hepatotoxicity. After drug screening and western blot analysis, borneol, a bicyclic monoterpenoid, was found to decrease the protein level of DDIT4. This is the first compound found to downregulate DDIT4 levels and ameliorate hepatic injury caused by gilteritinib. Our findings suggest that high levels of DDIT4 are the primary driver behind gilteritinib-induced liver injury, and that borneol could potentially be a clinically safe and feasible therapeutic strategy by inhibiting DDIT4 levels.},
}

@article {pmid40081503,
year = {2025},
author = {Xu, F and Lou, Y and Deng, Y and Lun, Z and Zhao, P and Yan, D and Han, Z and Wu, Z and Feng, C and Chen, L and Leng, J},
title = {Motor Imagery EEG Decoding Based on TS-former for Spinal Cord Injury Patients.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111298},
doi = {10.1016/j.brainresbull.2025.111298},
pmid = {40081503},
issn = {1873-2747},
abstract = {Traditional machine learning methods struggle with efficiency when processing large-scale data, while deep learning approaches, such as convolutional neural networks (CNN) and long short-term memory networks (LSTM), exhibit certain limitations when handling long-duration sequences. The choice of convolutional kernel size needs to be determined after several experiments, and LSTM has difficulty capturing effective information from long-time sequences. In this paper, we propose a transfer learning (TL) method based on Transformer, which constructs a new network architecture for feature extraction and classification of electroencephalogram (EEG) signals in the time-space domain, named TS-former. The frequency and spatial domain information of EEG signals is extracted using the Filter Bank Common Spatial Pattern (FBCSP), and the resulting features are subsequently processed by the Transformer to capture temporal patterns. The input features are processed by the Transformer using a multi-head attention mechanism, and the final classification outputs are generated through a fully connected layer. A classification model is pre-trained using fine-tuning techniques. When performing a new classification task, only some layers of the model are modified to adapt it to the new data and achieve good classification results. The experiments are conducted on a motor imagery (MI) EEG dataset from 16 spinal cord injury (SCI) patients. After training the model using a ten-time ten-fold cross-validation method, the average classification accuracy reached 95.09%. Our experimental results confirm a new approach to build a brain-computer interface (BCI) system for rehabilitation training of SCI patients.},
}

@article {pmid40079091,
year = {2025},
author = {Zhang, Y and Coid, J},
title = {Testing syndemic models along pathways to psychotic spectrum disorder: implications for population-level preventive interventions.},
journal = {Psychological medicine},
volume = {55},
number = {},
pages = {e85},
doi = {10.1017/S0033291725000455},
pmid = {40079091},
issn = {1469-8978},
mesh = {Humans ; Male ; *Psychotic Disorders/prevention & control ; Adult ; *Syndemic ; Cross-Sectional Studies ; Young Adult ; *Violence/prevention & control/psychology ; Substance-Related Disorders/prevention & control ; United Kingdom ; Middle Aged ; Adolescent ; Sexual Behavior ; Crime/prevention & control/statistics & numerical data/psychology ; Adverse Childhood Experiences/statistics & numerical data ; },
abstract = {BACKGROUND: Population-level preventive interventions are urgently needed and may be effective for psychosis due to social determinants. We tested three syndemic models along pathways from childhood adversity (CA) to psychotic spectrum disorder (PSD) and their implications for prevention.

METHODS: Cross-sectional data from 7461 British men surveyed in 5 population subgroups. We tested interactions on both additive and multiplicative scales for a syndemic of violence/criminality (VC), sexual behavior (SH), and substance misuse (SM) according to the presence of CA and adult traumatic life events; mediation analysis of path models; and partial least squares path modeling, with PSD as outcome.

RESULTS: Multiplicative synergistic interactions were found between VC, SH, and SM among men, who experienced CA and traumatic adult life events. However, when disaggregated, only SM mediated the pathway from CA to PSD. Path modeling showed traumatic life events acted on PSD through the syndemic and had no direct effect on PSD. Higher syndemic scores and living in areas of deprivation characterized men with PSD and CA.

CONCLUSIONS: Our findings support a broad division of PSD into cases due to (i) biological/inherent causes, and (ii) social determinants, the latter including a syndemic pathway determined by CA. Preventive strategies should focus primarily on preventing adverse effects of CA on developmental pathways which result in PSD. Single component prevention strategies may prevent triggering effects of SM on PSD during adolescence/early adulthood among vulnerable individuals due to CA. Future research should determine applicability and transferability of interventions based on these findings to different populations, specifically those experiencing syndemics.},
}

Humans
Male
*Psychotic Disorders/prevention & control
Adult
*Syndemic
Cross-Sectional Studies
Young Adult
*Violence/prevention & control/psychology
Substance-Related Disorders/prevention & control
United Kingdom
Middle Aged
Adolescent
Sexual Behavior
Crime/prevention & control/statistics & numerical data/psychology
Adverse Childhood Experiences/statistics & numerical data

@article {pmid40078487,
year = {2025},
author = {Li, M and Yu, P and Shen, Y},
title = {A spatial and temporal transformer-based EEG emotion recognition in VR environment.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1517273},
pmid = {40078487},
issn = {1662-5161},
abstract = {With the rapid development of deep learning, Electroencephalograph(EEG) emotion recognition has played a significant role in affective brain-computer interfaces. Many advanced emotion recognition models have achieved excellent results. However, current research is mostly conducted in laboratory settings for emotion induction, which lacks sufficient ecological validity and differs significantly from real-world scenarios. Moreover, emotion recognition models are typically trained and tested on datasets collected in laboratory environments, with little validation of their effectiveness in real-world situations. VR, providing a highly immersive and realistic experience, is an ideal tool for emotional research. In this paper, we collect EEG data from participants while they watched VR videos. We propose a purely Transformer-based method, EmoSTT. We use two separate Transformer modules to comprehensively model the temporal and spatial information of EEG signals. We validate the effectiveness of EmoSTT on a passive paradigm collected in a laboratory environment and an active paradigm emotion dataset collected in a VR environment. Compared with state-of-the-art methods, our method achieves robust emotion classification performance and can be well transferred between different emotion elicitation paradigms.},
}

@article {pmid40074408,
year = {2025},
author = {Zhang, C and Pu, Y and Kong, XZ},
title = {Latent dimensions of brain asymmetry.},
journal = {Handbook of clinical neurology},
volume = {208},
number = {},
pages = {37-45},
doi = {10.1016/B978-0-443-15646-5.00027-0},
pmid = {40074408},
issn = {0072-9752},
mesh = {Humans ; *Functional Laterality/physiology ; *Brain/physiology/anatomy & histology ; },
abstract = {Functional lateralization represents a fundamental aspect of brain organization, where certain cognitive functions are specialized in one hemisphere over the other. Deviations from typical patterns of lateralization often manifest in various brain disorders, such as autism spectrum disorder, schizophrenia, and dyslexia. However, despite its importance, uncovering the intrinsic properties of brain lateralization and its underlying structural basis remains challenging. On the one hand, functional lateralization has long been oversimplified, often reduced to a unidimensional perspective. For instance, individuals are sometimes labeled as left-brained or right-brained based on specific behavioral measures like handedness and language lateralization. Such a perspective disregards the nuanced subtypes of lateralization, each potentially attributed to distinct factors and associated with unique functional correlates. On the other hand, traditional studies of brain structural asymmetry have typically focused on localized analyses of homologous regions in the two hemispheres. This perspective fails to capture the inherent interplay between brain regions, resulting in an overly complex depiction of structural asymmetry. Such conceptual and methodological discrepancies between studies of functional lateralization and structural asymmetry pose significant obstacles to establishing meaningful links between them. To address this gap, a shift toward uncovering the dimensional structure of brain asymmetry has been proposed. This chapter introduces the concept of latent dimensions of brain asymmetry and provides an up-to-date overview of studies regarding dimensions of functional lateralization and structural asymmetry in the human brain. By transcending the traditional analysis and employing multivariate pattern techniques, these studies offer valuable insights into our understanding of the intricate organizational principles governing the human brain's lateralized functions.},
}

Humans
*Functional Laterality/physiology
*Brain/physiology/anatomy & histology

@article {pmid40073454,
year = {2025},
author = {Pei, Y and Zhao, S and Xie, L and Ji, B and Luo, Z and Ma, C and Gao, K and Wang, X and Sheng, T and Yan, Y and Yin, E},
title = {Toward the enhancement of affective brain-computer interfaces using dependence within EEG series.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adbfc0},
pmid = {40073454},
issn = {1741-2552},
abstract = {In recent years, electroencephalogram (EEG)-based emotion recognition technology has made remarkable advances. However, a subtle but crucial problem caused by the sliding window method has long been overlooked, which is the serious quantity mismatch between stimuli and short-term EEG frames. This may be an important factor limiting the performance of the emotion recognition systems. We name this mismatch as quantity-independence imbalance (Q/I imbalance) and propose the weak independence hypothesis to explain it. To validate this hypothesis and explore the effects of the Q/I imbalance on short-term EEG frames, we design four experiments from four perspectives, which are visualization, cross-validation, randomness test, and redundancy test. Furthermore, inspired by the redundancy of the short-term EEG samples, we propose an inference correction (IC), which uses the majority of the classifier's outputs to correct the prediction. The proposed IC is tested in the two datasets, including 60 subjects, using the intra- and inter-subject validations. Our IC achieves a significant improvement of 14.97% in classification accuracy. This study promotes the understanding of the time-dependent nature of EEG signals.},
}

@article {pmid40073451,
year = {2025},
author = {Dong, Y and Zheng, L and Pei, W and Gao, X and Wang, Y},
title = {A 240-target VEP-based BCI system employing narrow-band random sequences.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adbfc1},
pmid = {40073451},
issn = {1741-2552},
abstract = {OBJECTIVE: In the field of brain-computer interface (BCI), achieving high information transfer rates (ITR) with a large number of targets remains a challenge. This study aims to address this issue by developing a novel code-modulated visual evoked potential (c-VEP) BCI system capable of handling an extensive instruction set while maintaining high performance.

METHOD: We propose a c-VEP BCI system that employs narrow-band random sequences as visual stimuli and utilizes a convolutional neural network (CNN)-based EEG2Code decoding algorithm. This algorithm predicts corresponding stimulus sequences from EEG data and achieves efficient and accurate classification.

MAIN RESULTS: Offline experiments which conducted in a sequential paradigm, resulted in an average accuracy of 87.66% and a simulated ITR of 260.14 bits/min. In online experiments, the system demonstrated an accuracy of 76.27% and an ITR of 213.80 bits/min in a cued spelling task.

SIGNIFICANCE: This work represents an advancement in c-VEP BCI systems, offering one of the largest known instruction set in VEP-based BCIs and demonstrating robust performance metrics. The proposed system is potential for more practical and efficient BCI applications.},
}

@article {pmid40072857,
year = {2025},
author = {Ji, D and Huang, Y and Chen, Z and Zhou, X and Wang, J and Xiao, X and Xu, M and Ming, D},
title = {Enhanced spatial division multiple access BCI performance via incorporating MEG with EEG.},
journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNSRE.2025.3550653},
pmid = {40072857},
issn = {1558-0210},
abstract = {Spatial division multiple access (SDMA) is a way of encoding BCI systems based on spatial distribution of brain signal characteristics. However, SDMA-BCI based on EEG had poor system performance limited by spatial resolution. MEG-EEG fusion modality analysis can help solve this problem. According retina-cortical relationship, this study used stimulus out of the central visual field and tiny fixation points to construct a 16-command SDMA coded MEG-EEG fusion modality BCI system. We achieved this by synchronously acquiring MEG and EEG signals from 10 subjects. We compared the spatiotemporal features between MEG and EEG by analyzing signals in the occipital region. We fused MEG and EEG modalities without any signal processing and used the multi-class discriminative canonical pattern matching (Multi-DCPM) algorithm to evaluate and compare the system performance of EEG, MEG, and MEG-EEG fusion modalities. The result showed that MEG and EEG had obvious differences in spatial distribution characteristics. MEG improves offline classification accuracy of the 16 fixation points by 27.81% over EEG at 4s data length. Specially, the MEG-EEG fusion modality achieves an impressive average offline accuracy of 91.71%, which was a significant improvement over MEG (p<0.01, ANOVA). The MEG-EEG fusion modality achieved average information transfer rate (ITR) of 60.74 bits/min with a data length of 1 s, which was a 14% improvement over MEG. The MEG-EEG fusion modality significantly enhanced the spatial features and performance of SDMA-encoded BCIs. These results highlight the potential and feasibility of MEG-EEG fusion modality BCI, and provide theoretical insights and practical value for promoting the further development and application of SDMA in BCI.},
}

@article {pmid40072172,
year = {2025},
author = {Zhang, C and Zhang, C and Liu, Y},
title = {Progress in the Development of Flexible Devices Utilizing Protein Nanomaterials.},
journal = {Nanomaterials (Basel, Switzerland)},
volume = {15},
number = {5},
pages = {},
doi = {10.3390/nano15050367},
pmid = {40072172},
issn = {2079-4991},
support = {52473014//National Natural Science Foundation of China/ ; },
abstract = {Flexible devices are soft, lightweight, and portable, making them suitable for large-area applications. These features significantly expand the scope of electronic devices and demonstrate their unique value in various fields, including smart wearable devices, medical and health monitoring, human-computer interaction, and brain-computer interfaces. Protein materials, due to their unique molecular structure, biological properties, sustainability, self-assembly ability, and good biocompatibility, can be applied in electronic devices to significantly enhance the sensitivity, stability, mechanical strength, energy density, and conductivity of the devices. Protein-based flexible devices have become an important research direction in the fields of bioelectronics and smart wearables, providing new material support for the development of more environmentally friendly and reliable flexible electronics. Currently, many proteins, such as silk fibroin, collagen, ferritin, and so on, have been used in biosensors, memristors, energy storage devices, and power generation devices. Therefore, in this paper, we provide an overview of related research in the field of protein-based flexible devices, including the concept and characteristics of protein-based flexible devices, fabrication materials, fabrication processes, characterization, and evaluation, and we point out the future development direction of protein-based flexible devices.},
}

@article {pmid40071135,
year = {2025},
author = {Kobayashi, N and Ino, M},
title = {Parameter optimization of 3D convolutional neural network for dry-EEG motor imagery brain-machine interface.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1469244},
doi = {10.3389/fnins.2025.1469244},
pmid = {40071135},
issn = {1662-4548},
abstract = {Easing the behavioral restrictions of those in need of care not only improves their own quality of life (QoL) but also reduces the burden on care workers and may help reduce the number of care workers in countries with declining birthrates. The brain-machine interface (BMI), in which appliances and machines are controlled only by brain activity, can be used in nursing care settings to alleviate behavioral restrictions and reduce stress for those in need of care. It is also expected to reduce the workload of care workers. In this study, we focused on motor imagery (MI) classification by deep-learning to construct a system that can identify MI obtained by electroencephalography (EEG) measurements with high accuracy and a low latency response. By completing the system on the edge, the privacy of personal MI data can be ensured, and the system is ubiquitous, which improves user convenience. On the other hand, however, the edge is limited by hardware resources, and the implementation of models with a huge number of parameters and high computational cost, such as deep-learning, on the edge is challenging. Therefore, by optimizing the MI measurement conditions and various parameters of the deep-learning model, we attempted to reduce the power consumption and improve the response latency of the system by minimizing the computational cost while maintaining high classification accuracy. In addition, we investigated the use of a 3-dimension convolutional neural network (3D CNN), which can retain spatial locality as a feature to further improve the classification accuracy. We propose a method to maintain a high classification accuracy while enabling processing on the edge by optimizing the size and number of kernels and the layer structure. Furthermore, to develop a practical BMI system, we introduced dry electrodes, which are more comfortable for daily use, and optimized the number of parameters and memory consumption size of the proposed model to maintain classification accuracy even with fewer electrodes, less recall time, and a lower sampling rate. Compared to EEGNet, the proposed 3D CNN reduces the number of parameters, the number of multiply-accumulates, and memory footprint by approximately 75.9%, 16.3%, and 12.5%, respectively, while maintaining the same level of classification accuracy with the conditions of eight electrodes, 3.5 seconds sample window size, and 125 Hz sampling rate in 4-class dry-EEG MI.},
}

@article {pmid40070670,
year = {2025},
author = {Pan, H and Tang, C and Song, C and Li, J},
title = {Analysis of clinical efficacy of sacral magnetic stimulation for the treatment of detrusor underactivity.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1499310},
doi = {10.3389/fneur.2025.1499310},
pmid = {40070670},
issn = {1664-2295},
abstract = {OBJECTIVE: The objective of this study was to investigate the effectiveness and safety of sacral magnetic stimulation (SMS) in the management of detrusor underactivity (DU).

METHODS: We retrospectively analyzed 66 patients with detrusor underactivity treated at Hangzhou Third People's Hospital from January 2020 to October 2024, divided into two groups (33 cases each). Both groups had confirmed detrusor underactivity via urodynamic studies. The control group received conventional treatment (medication, catheterization, bladder training), while the observation group received SMS therapy. Urination diaries, urodynamic parameters and self-rating anxiety scale (SAS) were collected before and after the 4-week treatment to evaluate SMS efficacy and safety.

RESULTS: All patients in the observation group completed the course of sacral magnetic stimulation without experiencing any serious complications. After treatment, the observation group showed a significant reduction in the number of daily urinations, nocturnal urinations, SAS score and residual urine volume (RUV) (p < 0.05) compared with the control group. There was no statistically significant difference in maximum cystometric capacity (MCC) (p > 0.05). However, improvements were observed in SAS score, Detrusor Pressure at Maximum Flow (Pdet), Bladder Contractility Index (BCI), Maximum urinary Flow Rate (Qmax) and Average Urinary Flow Rate (Qavg) (p < 0.05). The effective rate in the observation group was 78.78%, significantly higher than that in the control group (p < 0.05). Although there was a slight decrease in the effective rate during the 6-month follow-up, the difference was not statistically significant (p > 0.05).

CONCLUSION: In conclusion, sacral magnetic stimulation therapy has demonstrated effectiveness in improving urinary function in patients with detrusor underactivity while maintaining a high level of safety.},
}

@article {pmid40069360,
year = {2025},
author = {Cheng, M and Lu, D and Li, K and Wang, Y and Tong, X and Qi, X and Yan, C and Ji, K and Wang, J and Wang, W and Lv, H and Zhang, X and Kong, W and Zhang, J and Ma, J and Li, K and Wang, Y and Feng, J and Wei, P and Li, Q and Shen, C and Fu, XD and Ma, Y and Zhang, X},
title = {Mitochondrial respiratory complex IV deficiency recapitulates amyotrophic lateral sclerosis.},
journal = {Nature neuroscience},
volume = {},
number = {},
pages = {},
pmid = {40069360},
issn = {1546-1726},
support = {2019YFA0508701//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; 2022YFA1303300//Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology)/ ; },
abstract = {Amyotrophic lateral sclerosis (ALS) is categorized into ~10% familial and ~90% sporadic cases. While familial ALS is caused by mutations in many genes of diverse functions, the underlying pathogenic mechanisms of ALS, especially in sporadic ALS (sALS), are largely unknown. Notably, about half of the cases with sALS showed defects in mitochondrial respiratory complex IV (CIV). To determine the causal role of this defect in ALS, we used transcription activator-like effector-based mitochondrial genome editing to introduce mutations in CIV subunits in rat neurons. Our results demonstrate that neuronal CIV deficiency is sufficient to cause a number of ALS-like phenotypes, including cytosolic TAR DNA-binding protein 43 redistribution, selective motor neuron loss and paralysis. These results highlight CIV deficiency as a potential cause of sALS and shed light on the specific vulnerability of motor neurons, marking an important advance in understanding and therapeutic development of sALS.},
}

@article {pmid40068721,
year = {2025},
author = {Kopalli, SR and Shukla, M and Jayaprakash, B and Kundlas, M and Srivastava, A and Jagtap, J and Gulati, M and Chigurupati, S and Ibrahim, E and Khandige, P and Garcia, DS and Koppula, S and Gasmi, A},
title = {Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery.},
journal = {Neuroscience},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.neuroscience.2025.03.017},
pmid = {40068721},
issn = {1873-7544},
abstract = {Stroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation strategies. The integration of Artificial Intelligence (AI) into stroke rehabilitation presents significant advancements across the continuum of care, from acute diagnosis to long-term recovery. This review explores AI's role in stroke rehabilitation, highlighting its impact on early diagnosis, motor recovery, and cognitive rehabilitation. AI-driven imaging techniques, such as deep learning applied to CT and MRI scans, improve early diagnosis and identify ischemic penumbra, enabling timely, personalized interventions. AI-assisted decision support systems optimize acute stroke treatment, including thrombolysis and endovascular therapy. In motor rehabilitation, AI-powered robotics and exoskeletons provide precise, adaptive assistance, while AI-augmented Virtual and Augmented Reality environments offer immersive, tailored recovery experiences. Brain-Computer Interfaces utilize AI for neurorehabilitation through neural signal processing, supporting motor recovery. Machine learning models predict functional recovery outcomes and dynamically adjust therapy intensities. Wearable technologies equipped with AI enable continuous monitoring and real-time feedback, facilitating home-based rehabilitation. AI-driven tele-rehabilitation platforms overcome geographic barriers by enabling remote assessment and intervention. The review also addresses the ethical, legal, and regulatory challenges associated with AI implementation, including data privacy and technical integration. Future research directions emphasize the transformative potential of AI in stroke rehabilitation, with case studies and clinical trials illustrating the practical benefits and efficacy of AI technologies in improving patient recovery.},
}

@article {pmid40068108,
year = {2025},
author = {Li, Y and Li, H and Wang, H and Wang, X},
title = {Utilizing Caenorhabditis Elegans as a Rapid and Precise Model for Assessing Amphetamine-Type Stimulants: A Novel Approach to Evaluating New Psychoactive Substances Activity and Mechanisms.},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
volume = {},
number = {},
pages = {e2500808},
doi = {10.1002/advs.202500808},
pmid = {40068108},
issn = {2198-3844},
support = {T2341003//National Natural Science Foundation of China/ ; T2394480(T2394483)//National Natural Science Foundation of China/ ; 2021ZD0203000(2021ZD0203003)//STI2030-Major Projects/ ; 20240304115SF//Science and Technology Development Plan Project of Jilin Province/ ; XDB0450102//Strategic Priority Research Program of the Chinese Academy of Sciences/ ; BMI2400014//Open Research Fund of the State Key Laboratory of Brain-Machine Intelligence, Zhejiang University/ ; },
abstract = {The surge of new psychoactive substances (NPS) poses significant public health challenges due to their unregulated status and diverse effects. However, existing in vivo models for evaluating their activities are limited. To address this gap, this study utilizes the model organism Caenorhabditis elegans (C. elegans) to evaluate the activity of amphetamine-type stimulants (ATS) and their analogs. The swimming-induced paralysis (SWIP) assay is employed to measure the acute responses of C. elegans to various ATS, including amphetamine (AMPH), methamphetamine (METH), 3,4-methylenedioxymethamphetamine (MDMA) and their enantiomers. The findings reveal distinct responses in wild-type and mutant C. elegans, highlighting the roles of dopaminergic and serotonergic pathways, particularly DOP-3 and SER-4 receptors. The assay also revealed that C. elegans can distinguish between the chiral forms of ATS. Additionally, structural activity relationships (SAR) are observed, with meta-R amphetamines showing more pronounced effects than ortho-R and para-R analogs. This study demonstrates the utility of C. elegans in rapidly assessing ATS activity and toxicity, providing a cost-effective and precise method for high-throughput testing of NPS. These results contribute to a better understanding of ATS pharmacology and offer a valuable framework for future research and potential regulatory applications.},
}

@article {pmid40067734,
year = {2025},
author = {Wang, X and Qi, W and Yang, W and Wang, W},
title = {Cholesky Space for Brain-Computer Interfaces.},
journal = {IEEE transactions on neural networks and learning systems},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/TNNLS.2025.3542801},
pmid = {40067734},
issn = {2162-2388},
abstract = {Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) enable direct interactions between the brain and external environments, with applications in medical rehabilitation, motor substitution, gaming, and entertainment. Traditional methods that model the non-Euclidean characteristics of EEG signals demonstrate robustness and high performance, but they suffer from significant computational costs and are typically restricted to a single BCI paradigm. This article addresses these limitations by utilizing a diffeomorphism from Riemannian manifolds to the Cholesky space, which simplifies the solution process and enables application across multiple BCI paradigms. Our proposed Cholesky space-based model, CSNet, achieves state-of-the-art (SOTA) performance in motor imagery (MI) decoding and emotion recognition and demonstrates competitive performance in error-related negativity (ERN) decoding, all without the need for data preprocessing. Furthermore, our runtime comparison shows that the Cholesky space method is more efficient than the method based on the Riemannian manifold as the matrix dimension increases. To enhance the interpretability of CSNet, we perform t-distributed stochastic neighbor embedding (t-SNE) visualization for MI, frequency band energy visualization for emotion recognition, and temporal importance visualization for ERN. The results indicate that CSNet effectively learns discriminative features, identifies important frequency bands, and focuses on important temporal features. The CSNet effectively captures the non-Euclidean characteristics of EEG signals across various BCI paradigms, while mitigating high computational costs, making it a promising candidate for future BCI algorithms. The code for this study is publicly available at: https://github.com/XingfuWang/CSNet.},
}

@article {pmid40067717,
year = {2025},
author = {Du, Y and Chen, J and Liu, Z and Wong, N and Zhang, C and Ding, Z and Liu, J and Ngai, ECH},
title = {Valence-Arousal Disentangled Representation Learning for Emotion Recognition in SSVEP-Based BCIs.},
journal = {IEEE journal of biomedical and health informatics},
volume = {PP},
number = {},
pages = {},
doi = {10.1109/JBHI.2025.3549727},
pmid = {40067717},
issn = {2168-2208},
abstract = {Steady state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs), which are widely used in rehabilitation and disability assistance, can benefit from real-time emotion recognition to enhance human-machine interaction. However, the learned discriminative latent representations in SSVEP-BCIs may generalize in an unintended direction, which can lead to reduced accuracy in detecting emotional states. In this paper, we introduce a Valence-Arousal Disentangled Representation Learning (VADL) method, drawing inspiration from the classical two-dimensional emotional model, to enhance the performance and generalization of emotion recognition within SSVEP-BCIs. VADL distinctly disentangles the latent variables of valence and arousal information to improve accuracy. It utilizes the structured state space duality model to thoroughly extract global emotional features. Additionally, we propose a Multisubject Gradient Blending training strategy that individually tailors the learning pace of reconstruction and discrimination tasks within VADL on-the-fly. To verify the feasibility of our method, we have developed a comprehensive database comprising 23 subjects, in which both the emotional states and SSVEPs were effectively elicited. Experimental results indicate that VADL surpasses existing state-of-the-art benchmark algorithms.},
}

@article {pmid40064104,
year = {2025},
author = {Rodríguez-García, ME and Carino-Escobar, RI and Carrillo-Mora, P and Hernandez-Arenas, C and Ramirez-Nava, AG and Pacheco-Gallegos, MDR and Valdes-Cristerna, R and Cantillo-Negrete, J},
title = {Neuroplasticity changes in cortical activity, grey matter, and white matter of stroke patients after upper extremity motor rehabilitation via a brain-computer interface therapy program.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adbebf},
pmid = {40064104},
issn = {1741-2552},
abstract = {OBJECTIVE: Upper extremity (UE) motor function loss is one of the most impactful consequences of stroke. Recently, brain-computer interface (BCI) systems have been utilized in therapy programs to enhance UE motor recovery after stroke, widely attributed to neuroplasticity mechanisms. However, the effect that the BCI's closed-loop feedback can have in these programs is unclear. The aim of this study was to quantitatively assess and compare the neuroplasticity effects elicited in stroke patients by a UE motor rehabilitation BCI therapy and by its sham-BCI counterpart.

APPROACH: Twenty patients were randomly assigned to either the experimental group (EG), who controlled the BCI system via UE motor intention, or the control group (CG), who received random feedback. The elicited neuroplasticity effects were quantified using asymmetry metrics derived from electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI) data acquired before, at the middle, and at the end of the intervention, alongside UE sensorimotor function evaluations. These asymmetry indexes compare the affected and unaffected hemispheres and are robust to lesion location variability.

MAIN RESULTS: Most patients from the EG presented brain activity lateralization to one brain hemisphere, as described by EEG (8 patients) and fMRI (6 patients) metrics. Conversely, the CG showed less pronounced lateralizations, presenting primarily bilateral activity patterns. DTI metrics showed increased white matter integrity in half of the EG patients' unaffected hemisphere, and in all but 2 CG patients' affected hemisphere. Individual patient analysis suggested that lesion location was relevant since functional and structural lateralizations occurred towards different hemispheres depending on stroke site.

SIGNIFICANCE: This study shows that a BCI intervention can elicit more pronounced neuroplasticity-related lateralizations than a sham-BCI therapy. These findings could serve as future biomarkers, helping to better select patients and increasing the impact that a BCI intervention can achieve.

CLINICAL TRIAL: NCT04724824. .},
}

@article {pmid40064095,
year = {2025},
author = {Russo, JS and Shiels, TA and Lin, CS and John, SE and Grayden, DB},
title = {Feasibility of source-level motor imagery classification for people with multiple sclerosis.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adbec1},
pmid = {40064095},
issn = {1741-2552},
abstract = {There is limited work investigating Brain-Computer Interface (BCI) technology in people with Multiple Sclerosis (pwMS), a neurodegenerative disorder of the central nervous system. Present work is limited to recordings at the scalp, which may be significantly altered by changes within the cortex due to volume conduction. The recordings obtained from the sensors, therefore, combine disease-related alterations and task-relevant neural signals, as well as signals from other regions of the brain that are not relevant. The current study aims to unmix signals affected by MS progression and BCI task-relevant signals using estimated source activity to improve classification accuracy. Approach. Data was collected from eight participants with a range of MS severity and ten neurotypical participants. This dataset was used to report the classification accuracy of imagined movements of the hands and feet at the sensor-level and the source-level in the current study. K-means clustering of equivalent current dipoles was conducted to unmix temporally independent signals. The location of these dipoles was compared between MS and control groups and used for classification of imagined movement. Linear discriminant analysis classification was performed at each time-frequency point to highlight differences in frequency band delay. Main Results. Source-level signal acquisition significantly improved decoding accuracy of imagined movement vs. rest and movement vs. movement classification in pwMS and controls. There was no significant difference found in alpha (7-13 Hz) and beta (13-30 Hz) band classification delay between the neurotypical control and MS group, including imagery of limbs with weakness or paralysis. Significance. This study is the first to demonstrate the advantages of source-level analysis for BCI applications in pwMS. The results highlight the potential for enhanced clinical outcomes and emphasize the need for longitudinal studies to assess the impact of MS progression on BCI performance, which is crucial for effective clinical translation of BCI technology. .},
}

@article {pmid40063703,
year = {2025},
author = {Ahmed, AAA and Alegret, N and Almeida, B and Alvarez-Puebla, R and Andrews, AM and Ballerini, L and Barrios-Capuchino, JJ and Becker, C and Blick, RH and Bonakdar, S and Chakraborty, I and Chen, X and Cheon, J and Chilla, G and Coelho Conceicao, AL and Delehanty, J and Dulle, M and Efros, AL and Epple, M and Fedyk, M and Feliu, N and Feng, M and Fernández-Chacón, R and Fernandez-Cuesta, I and Fertig, N and Förster, S and Garrido, JA and George, M and Guse, AH and Hampp, N and Harberts, J and Han, J and Heekeren, HR and Hofmann, UG and Holzapfel, M and Hosseinkazemi, H and Huang, Y and Huber, P and Hyeon, T and Ingebrandt, S and Ienca, M and Iske, A and Kang, Y and Kasieczka, G and Kim, DH and Kostarelos, K and Lee, JH and Lin, KW and Liu, S and Liu, X and Liu, Y and Lohr, C and Mailänder, V and Maffongelli, L and Megahed, S and Mews, A and Mutas, M and Nack, L and Nakatsuka, N and Oertner, TG and Offenhäusser, A and Oheim, M and Otange, B and Otto, F and Patrono, E and Peng, B and Picchiotti, A and Pierini, F and Pötter-Nerger, M and Pozzi, M and Pralle, A and Prato, M and Qi, B and Ramos-Cabrer, P and Genger, UR and Ritter, N and Rittner, M and Roy, S and Santoro, F and Schuck, NW and Schulz, F and Şeker, E and Skiba, M and Sosniok, M and Stephan, H and Wang, R and Wang, T and Wegner, KD and Weiss, PS and Xu, M and Yang, C and Zargarian, SS and Zeng, Y and Zhou, Y and Zhu, D and Zierold, R and Parak, WJ},
title = {Interfacing with the Brain: How Nanotechnology Can Contribute.},
journal = {ACS nano},
volume = {},
number = {},
pages = {},
doi = {10.1021/acsnano.4c10525},
pmid = {40063703},
issn = {1936-086X},
abstract = {Interfacing artificial devices with the human brain is the central goal of neurotechnology. Yet, our imaginations are often limited by currently available paradigms and technologies. Suggestions for brain-machine interfaces have changed over time, along with the available technology. Mechanical levers and cable winches were used to move parts of the brain during the mechanical age. Sophisticated electronic wiring and remote control have arisen during the electronic age, ultimately leading to plug-and-play computer interfaces. Nonetheless, our brains are so complex that these visions, until recently, largely remained unreachable dreams. The general problem, thus far, is that most of our technology is mechanically and/or electrically engineered, whereas the brain is a living, dynamic entity. As a result, these worlds are difficult to interface with one another. Nanotechnology, which encompasses engineered solid-state objects and integrated circuits, excels at small length scales of single to a few hundred nanometers and, thus, matches the sizes of biomolecules, biomolecular assemblies, and parts of cells. Consequently, we envision nanomaterials and nanotools as opportunities to interface with the brain in alternative ways. Here, we review the existing literature on the use of nanotechnology in brain-machine interfaces and look forward in discussing perspectives and limitations based on the authors' expertise across a range of complementary disciplines─from neuroscience, engineering, physics, and chemistry to biology and medicine, computer science and mathematics, and social science and jurisprudence. We focus on nanotechnology but also include information from related fields when useful and complementary.},
}

@article {pmid40063028,
year = {2025},
author = {Ben-Zion, Z and Simon, AJ and Rosenblatt, M and Korem, N and Duek, O and Liberzon, I and Shalev, AY and Hendler, T and Levy, I and Harpaz-Rotem, I and Scheinost, D},
title = {Connectome-Based Predictive Modeling of PTSD Development Among Recent Trauma Survivors.},
journal = {JAMA network open},
volume = {8},
number = {3},
pages = {e250331},
doi = {10.1001/jamanetworkopen.2025.0331},
pmid = {40063028},
issn = {2574-3805},
mesh = {Humans ; *Stress Disorders, Post-Traumatic/physiopathology/diagnostic imaging ; Female ; Adult ; Male ; *Connectome ; *Survivors/psychology/statistics & numerical data ; *Magnetic Resonance Imaging ; Machine Learning ; Middle Aged ; Prognosis ; Israel/epidemiology ; Longitudinal Studies ; Wounds and Injuries/psychology/physiopathology/complications ; },
abstract = {IMPORTANCE: The weak link between subjective symptom-based diagnostics for posttraumatic psychopathology and objective neurobiological indices hinders the development of effective personalized treatments.

OBJECTIVE: To identify early neural networks associated with posttraumatic stress disorder (PTSD) development among recent trauma survivors.

This prognostic study used data from the Neurobehavioral Moderators of Posttraumatic Disease Trajectories (NMPTDT) large-scale longitudinal neuroimaging dataset of recent trauma survivors. The NMPTDT study was conducted from January 20, 2015, to March 11, 2020, and included adult civilians who were admitted to a general hospital emergency department in Israel and screened for early PTSD symptoms indicative of chronic PTSD risk. Enrolled participants completed comprehensive clinical assessments and functional magnetic resonance imaging (fMRI) scans at 1, 6, and 14 months post trauma. Data were analyzed from September 2023 to March 2024.

EXPOSURE: Traumatic events included motor vehicle incidents, physical assaults, robberies, hostilities, electric shocks, fires, drownings, work accidents, terror attacks, or large-scale disasters.

MAIN OUTCOMES AND MEASURES: Connectome-based predictive modeling (CPM), a whole-brain machine learning approach, was applied to resting-state and task-based fMRI data collected at 1 month post trauma. The primary outcome measure was PTSD symptom severity across the 3 time points, assessed with the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5). Secondary outcomes included Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) PTSD symptom clusters (intrusion, avoidance, negative alterations in mood and cognition, hyperarousal).

RESULTS: A total of 162 recent trauma survivors (mean [SD] age, 33.9 [11.5] years; 80 women [49.4%] and 82 men [50.6%]) were included at 1 month post trauma. Follow-up assessments were completed by 136 survivors (84.0%) at 6 months and by 133 survivors (82.1%) at 14 months post trauma. Among the 162 recent trauma survivors, CPM significantly predicted PTSD severity at 1 month (ρ = 0.18, P < .001) and 14 months (ρ = 0.24, P < .001) post trauma, but not at 6 months post trauma (ρ = 0.03, P = .39). The most predictive edges at 1 month included connections within and between the anterior default mode, motor sensory, and salience networks. These networks, with the additional contribution of the central executive and visual networks, were predictive of symptoms at 14 months. CPM predicted avoidance and negative alterations in mood and cognition at 1 month, but it predicted intrusion and hyperarousal symptoms at 14 months.

CONCLUSIONS AND RELEVANCE: In this prognostic study of recent trauma survivors, individual differences in large-scale neural networks shortly after trauma were associated with variability in PTSD symptom trajectories over the first year following trauma exposure. These findings suggest that CPM may identify potential targets for interventions.},
}

Humans
*Stress Disorders, Post-Traumatic/physiopathology/diagnostic imaging
Female
Adult
Male
*Connectome
*Survivors/psychology/statistics & numerical data
*Magnetic Resonance Imaging
Machine Learning
Middle Aged
Prognosis
Israel/epidemiology
Longitudinal Studies
Wounds and Injuries/psychology/physiopathology/complications

@article {pmid40062568,
year = {2025},
author = {Pilipović, K and Parpura, V},
title = {The potential of single-walled carbon nanotube-based therapeutic platforms targeting astrocytes.},
journal = {Nanomedicine (London, England)},
volume = {},
number = {},
pages = {1-3},
doi = {10.1080/17435889.2025.2476376},
pmid = {40062568},
issn = {1748-6963},
}

@article {pmid40061257,
year = {2025},
author = {Song, Y and Han, L and Zhang, T and Xu, B},
title = {Multiscale fusion enhanced spiking neural network for invasive BCI neural signal decoding.},
journal = {Frontiers in neuroscience},
volume = {19},
number = {},
pages = {1551656},
pmid = {40061257},
issn = {1662-4548},
abstract = {Brain-computer interfaces (BCIs) are an advanced fusion of neuroscience and artificial intelligence, requiring stable and long-term decoding of neural signals. Spiking Neural Networks (SNNs), with their neuronal dynamics and spike-based signal processing, are inherently well-suited for this task. This paper presents a novel approach utilizing a Multiscale Fusion enhanced Spiking Neural Network (MFSNN). The MFSNN emulates the parallel processing and multiscale feature fusion seen in human visual perception to enable real-time, efficient, and energy-conserving neural signal decoding. Initially, the MFSNN employs temporal convolutional networks and channel attention mechanisms to extract spatiotemporal features from raw data. It then enhances decoding performance by integrating these features through skip connections. Additionally, the MFSNN improves generalizability and robustness in cross-day signal decoding through mini-batch supervised generalization learning. In two benchmark invasive BCI paradigms, including the single-hand grasp-and-touch and center-and-out reach tasks, the MFSNN surpasses traditional artificial neural network methods, such as MLP and GRU, in both accuracy and computational efficiency. Moreover, the MFSNN's multiscale feature fusion framework is well-suited for the implementation on neuromorphic chips, offering an energy-efficient solution for online decoding of invasive BCI signals.},
}

@article {pmid40060989,
year = {2025},
author = {Huang, J and Huang, L and Li, Y and Fang, F},
title = {A Bibliometric Analysis of the Application of Brain-Computer Interface in Rehabilitation Medicine Over the Past 20 Years.},
journal = {Journal of multidisciplinary healthcare},
volume = {18},
number = {},
pages = {1297-1317},
pmid = {40060989},
issn = {1178-2390},
abstract = {OBJECTIVE: This study aims to conduct a bibliometric analysis of the application of brain- computer interface (BCI) in rehabilitation medicine, assessing the current state, developmental trends, and future potential of this field. By systematically analyzing relevant literature, we seek to identify key research themes and enhance understanding of BCI technology in rehabilitation.

METHODS: We utilized bibliometric analysis tools such as VOSviewer and CiteSpace to screen and analyze 426 relevant articles from the Web of Science Core Collection (WoSCC) database. We quantitatively evaluated citation patterns, publication trends, and the collaboration networks of research institutions and authors to uncover research hotspots and frontier dynamics in the field.

RESULTS: The findings indicate a continuous increase in research publications since 2003, with a notable peak occurring between 2019 and 2021. The analysis revealed that motor imagery, motor recovery, and signal processing are the predominant research themes. Furthermore, the United States and China are leading in the publication volume related to BCI and rehabilitation medicine. Key research institutions include the University of Tübingen and the New York State Department of Health, with significant contributions from scholars like Niels Birbaumer.

CONCLUSION: Although the current research on BCI in rehabilitation medicine shows significant potential and efficacy, further exploration of certain research directions is needed, along with the promotion of interdisciplinary collaboration to comprehensively address complex real-world issues such as motor function impairment. Future research should focus on optimizing training models, enhancing technical feasibility, and exploring home rehabilitation applications to facilitate the broader adoption of BCI technology in rehabilitation medicine.},
}

@article {pmid40060849,
year = {2025},
author = {Castañeda-Valencia, G and Gama, LF and Panneerselvam, M and Vaiss, VS and Guedes, IA and Dardenne, LE and Costa, LT},
title = {Methodological Approach Based on Structural Parameters, Vibrational Frequencies, and MMFF94 Bond Charge Increments for Platinum-Based Compounds.},
journal = {ACS omega},
volume = {10},
number = {8},
pages = {8314-8335},
pmid = {40060849},
issn = {2470-1343},
abstract = {In this work, we performed a comprehensive benchmark study for the ground state of five small- and medium-sized platinum derivatives, PtH, PtCl, [PtCl4][2-], [Pt(NH3)4][2+], and cis-[Pt(NH3)2Cl2], in the gas phase and two cisplatin polymorphs in the solid phase. The benchmark encompassed 16 density functionals, including nonhybrids, hybrids, and double hybrids. Furthermore, Hartree-Fock (HF) and Post-HF by Møller-Plesset MP2 methods were also tested. Additionally, 11 basis sets were explored, comparing relativistic all-electron and RECP approaches. Our results indicate that the methodologies best suited for predicting structural parameters do not excel in predicting vibrational frequencies and vice versa. In the context of this theoretical framework, we also examine the derivation of partial atomic charges and bond charge increments (bci) as fundamental parameters within the MMFF94 classical force field. Our results show that the partial atomic charges of CHELPG present a slight charge fluctuation in Pt for all investigated levels of theory, and this behavior reproduces well the soft acid definition for Pt[2+], giving the best description of the chemical environment of platinum in the cisplatin complex. The average calculated bci values effectively capture the atomic charge variations in the chemical environment of Pt in the investigated species. The developed bci optimization tool, based on MMFF94, was implemented using a Python code made available at https://github.com/molmodcs/bci_solver. This methodology will be further implemented in the DockThor receptor-ligand docking program, allowing future molecular docking validations involving ligand compounds containing Pt atoms.},
}

@article {pmid40060267,
year = {2025},
author = {Shawki, N and Napoli, A and Vargas-Irwin, CE and Thompson, CK and Donoghue, JP and Serruya, MD},
title = {Neural signal analysis in chronic stroke: advancing intracortical brain-computer interface design.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1544397},
pmid = {40060267},
issn = {1662-5161},
abstract = {INTRODUCTION: Intracortical Brain-computer interfaces (iBCIs) are a promising technology to restore function after stroke. It remains unclear whether iBCIs will be able to use the signals available in the neocortex overlying stroke affecting the underlying white matter and basal ganglia.

METHODS: Here, we decoded both local field potentials (LFPs) and spikes recorded from intracortical electrode arrays in a person with chronic cerebral subcortical stroke performing various tasks with his paretic hand, with and without a powered orthosis. Analysis of these neural signals provides an opportunity to explore the electrophysiological activities of a stroke affected brain and inform the design of medical devices that could restore function.

RESULTS: The frequency domain analysis showed that as the distance between an array and the stroke site increased, the low frequency power decreased, and high frequency power increased. Coordinated cross-channel firing of action potentials while attempting a motor task and cross-channel simultaneous low frequency bursts while relaxing were also observed. Using several offline analysis techniques, we propose three features for decoding motor movements in stroke-affected brains.

DISCUSSION: Despite the presence of unique activities that were not reported in previous iBCI studies with intact brain functions, it is possible to decode motor intents from the neural signals collected from a subcortical stroke-affected brain.},
}

@article {pmid40059723,
year = {2025},
author = {Han, JS and Jeon, MC and Lee, CM and Velasco, GC and Park, SY and Park, SN},
title = {Comparing Tinnitus Suppression in Asymmetric Hearing Loss and Single-Sided Deafness: Cochlear Versus Bone Conduction Implants.},
journal = {The Laryngoscope},
volume = {},
number = {},
pages = {},
doi = {10.1002/lary.32090},
pmid = {40059723},
issn = {1531-4995},
support = {RS-2024-00340841//National Research Foundation of Korea/ ; },
abstract = {OBJECTIVES: Implantable hearing devices, such as cochlear implants (CI) and bone conduction implants (BCI), are options for hearing rehabilitation in patients with asymmetric hearing loss (AHL) and single-sided deafness (SSD). This study aimed to compare the effects of CI and BCI on tinnitus in AHL/SSD patients with tinnitus.

METHODS: This retrospective study enrolled adult AHL/SSD patients with significant tinnitus who underwent CI or BCI placement between 2017 and 2023. Clinical characteristics, preoperative and postoperative audiologic test results, and tinnitus questionnaires (tinnitus handicap inventory, THI; visual analog scale, VAS) were collected and analyzed.

RESULTS: Of 33 AHL/SSD patients with significant tinnitus (THI ≥ 18), 16 received CI and 17 BCI. In the CI group, all four VAS scores (loudness, awareness, annoyance, and effect on life) and THI scores significantly improved. In the BCI group, annoyance and effect on life categories of VAS and THI scores significantly improved, while VAS loudness and awareness remained similar. Linear mixed model analysis showed that the decrease in VAS loudness, awareness, and annoyance scores was significantly greater in the CI group compared to the BCI group. The CI group showed a significantly higher tinnitus cure rate (62.5.0%) compared with the BCI group (11.8%) at 6-months postoperative.

CONCLUSION: Both CI and BCI effectively improved tinnitus in AHL/SSD patients with tinnitus. However, CI is considered the first-line therapeutic option for tinnitus due to its stronger effect on tinnitus suppression as well as the higher cure rate in SSD/AHL patients with tinnitus.},
}

@article {pmid40059266,
year = {2025},
author = {Meng, J and Wei, Y and Mai, X and Li, S and Wang, X and Luo, R and Ji, M and Zhu, X},
title = {Paradigms and methods of noninvasive brain-computer interfaces in motor or communication assistance and rehabilitation: a systematic review.},
journal = {Medical & biological engineering & computing},
volume = {},
number = {},
pages = {},
pmid = {40059266},
issn = {1741-0444},
abstract = {Noninvasive brain-computer interfaces (BCIs) have rapidly developed over the past decade. This new technology utilizes magneto-electrical recording or hemodynamic imaging approaches to acquire neurophysiological signals noninvasively, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). These noninvasive signals have different temporal resolutions ranging from milliseconds to seconds and various spatial resolutions ranging from centimeters to millimeters. Thanks to these neuroimaging technologies, various BCI modalities like steady-state visual evoked potential (SSVEP), P300, and motor imagery (MI) could be proposed to rehabilitate or assist patients' lost function of mobility or communication. This review focuses on the recent development of paradigms, methods, and applications of noninvasive BCI for motor or communication assistance and rehabilitation. The selection of papers follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), obtaining 223 research articles since 2016. We have observed that EEG-based BCI has gained more research focus due to its low cost and portability, as well as more translational studies in rehabilitation, robotic device control, etc. In the past decade, decoding approaches such as deep learning and source imaging have flourished in BCI. Still, there are many challenges to be solved to date, such as designing more convenient electrodes, improving the decoding accuracy and efficiency, designing more applicable systems for target patients, etc., before this new technology matures enough to benefit clinical users.},
}

@article {pmid40058464,
year = {2025},
author = {Zhao, Z and Li, Y and Peng, Y and Camilleri, K and Kong, W},
title = {Multi-view graph fusion of self-weighted EEG feature representations for speech imagery decoding.},
journal = {Journal of neuroscience methods},
volume = {},
number = {},
pages = {110413},
doi = {10.1016/j.jneumeth.2025.110413},
pmid = {40058464},
issn = {1872-678X},
abstract = {BACKGROUND: Electroencephalogram (EEG)-based speech imagery is an emerging brain-computer interface paradigm, which enables the speech disabled to naturally and intuitively communicate with external devices or other people. Currently, speech imagery research decoding performance is limited. One of the reasons is that there is still no consensus on which domain features are more discriminative.

NEW METHOD: To adaptively capture the complementary information from different domain features, we treat each domain as a view and propose a multi-view graph fusion of self-weighted EEG feature representations (MVGSF) model by learning a consensus graph from multi-view EEG features, based on which the imagery intentions can be effectively decoded. Considering that different EEG features in each view have different discriminative abilities, the view-dependent feature importance exploration strategy is incorporated in MVGSF.

RESULTS: (1) MVGSF exhibits outstanding performance on two public speech imagery datasets (2) The learned consensus graph from multi-view features effectively characterizes the relationships of EEG samples in a progressive manner. (3) Some task-related insights are explored including the feature importance-based identification of critical EEG channels and frequency bands in speech imagery decoding.

We compared MVGSF with single-view counterparts, other multi-view models, and state-of-the-art models. MVGSF achieved the highest accuracy, with average accuracies of 78.93% on the 2020IBCIC3 dataset and 53.85% on the KaraOne dataset.

CONCLUSIONS: MVGSF effectively integrates features from multiple domains to enhance decoding capabilities. Furthermore, through the learned feature importance, MVGSF has made certain contributions to identify the EEG spatial-frequency patterns in speech imagery decoding.},
}

@article {pmid40057290,
year = {2025},
author = {Chuang, CH and Chang, KY and Huang, CS and Bessas, AM},
title = {Augmenting Brain-Computer Interfaces with ART: An Artifact Removal Transformer for Reconstructing Multichannel EEG Signals.},
journal = {NeuroImage},
volume = {},
number = {},
pages = {121123},
doi = {10.1016/j.neuroimage.2025.121123},
pmid = {40057290},
issn = {1095-9572},
abstract = {Artifact removal in electroencephalography (EEG) is a longstanding challenge that significantly impacts neuroscientific analysis and brain-computer interface (BCI) performance. Tackling this problem demands advanced algorithms, extensive noisy-clean training data, and thorough evaluation strategies. This study presents the Artifact Removal Transformer (ART), an innovative EEG denoising model employing transformer architecture to adeptly capture the transient millisecond-scale dynamics characteristic of EEG signals. Our approach offers a holistic, end-to-end denoising solution that simultaneously addresses multiple artifact types in multichannel EEG data. We enhanced the generation of noisy-clean EEG data pairs using an independent component analysis, thus fortifying the training scenarios critical for effective supervised learning. We performed comprehensive validations using a wide range of open datasets from various BCI applications, employing metrics like mean squared error and signal-to-noise ratio, as well as sophisticated techniques such as source localization and EEG component classification. Our evaluations confirm that ART surpasses other deep-learning-based artifact removal methods, setting a new benchmark in EEG signal processing. This advancement not only boosts the accuracy and reliability of artifact removal but also promises to catalyze further innovations in the field, facilitating the study of brain dynamics in naturalistic environments.},
}

@article {pmid40054446,
year = {2025},
author = {Natraj, N and Seko, S and Abiri, R and Miao, R and Yan, H and Graham, Y and Tu-Chan, A and Chang, EF and Ganguly, K},
title = {Sampling representational plasticity of simple imagined movements across days enables long-term neuroprosthetic control.},
journal = {Cell},
volume = {188},
number = {5},
pages = {1208-1225.e32},
doi = {10.1016/j.cell.2025.02.001},
pmid = {40054446},
issn = {1097-4172},
mesh = {Humans ; *Brain-Computer Interfaces ; Male ; *Imagination/physiology ; *Neuronal Plasticity ; Adult ; *Movement ; Female ; Electrocorticography ; Middle Aged ; Quadriplegia/physiopathology ; Robotics ; Young Adult ; },
abstract = {The nervous system needs to balance the stability of neural representations with plasticity. It is unclear what the representational stability of simple well-rehearsed actions is, particularly in humans, and their adaptability to new contexts. Using an electrocorticography brain-computer interface (BCI) in tetraplegic participants, we found that the low-dimensional manifold and relative representational distances for a repertoire of simple imagined movements were remarkably stable. The manifold's absolute location, however, demonstrated constrained day-to-day drift. Strikingly, neural statistics, especially variance, could be flexibly regulated to increase representational distances during BCI control without somatotopic changes. Discernability strengthened with practice and was BCI-specific, demonstrating contextual specificity. Sampling representational plasticity and drift across days subsequently uncovered a meta-representational structure with generalizable decision boundaries for the repertoire; this allowed long-term neuroprosthetic control of a robotic arm and hand for reaching and grasping. Our study offers insights into mesoscale representational statistics that also enable long-term complex neuroprosthetic control.},
}

Humans
*Brain-Computer Interfaces
Male
*Imagination/physiology
*Neuronal Plasticity
Adult
*Movement
Female
Electrocorticography
Middle Aged
Quadriplegia/physiopathology
Robotics
Young Adult

@article {pmid40051983,
year = {2025},
author = {Lingelbach, K and Rips, J and Karstensen, L and Mathis-Ullrich, F and Vukelić, M},
title = {Evaluating robotic actions: spatiotemporal brain dynamics of performance assessment in robot-assisted laparoscopic training.},
journal = {Frontiers in neuroergonomics},
volume = {6},
number = {},
pages = {1535799},
pmid = {40051983},
issn = {2673-6195},
abstract = {INTRODUCTION: Enhancing medical robot training traditionally relies on explicit feedback from physicians to identify optimal and suboptimal robotic actions during surgery. Passive brain-computer interfaces (BCIs) offer an emerging alternative by enabling implicit brain-based performance evaluations. However, effectively decoding these evaluations of robot performance requires a comprehensive understanding of the spatiotemporal brain dynamics identifying optimal and suboptimal robot actions within realistic settings.

METHODS: We conducted an electroencephalographic study with 16 participants who mentally assessed the quality of robotic actions while observing simulated robot-assisted laparoscopic surgery scenarios designed to approximate real-world conditions. We aimed to identify key spatiotemporal dynamics using the surface Laplacian technique and two complementary data-driven methods: a mass-univariate permutation-based clustering and multivariate pattern analysis (MVPA)-based temporal decoding. A second goal was to identify the optimal time interval of evoked brain signatures for single-trial classification.

RESULTS: Our analyses revealed three distinct spatiotemporal brain dynamics differentiating the quality assessment of optimal vs. suboptimal robotic actions during video-based laparoscopic training observations. Specifically, an enhanced left fronto-temporal current source, consistent with P300, LPP, and P600 components, indicated heightened attentional allocation and sustained evaluation processes during suboptimal robot actions. Additionally, amplified current sinks in right frontal and mid-occipito-parietal regions suggested prediction-based processing and conflict detection, consistent with the oERN and interaction-based ERN/N400. Both mass-univariate clustering and MVPA provided convergent evidence supporting these neural distinctions.

DISCUSSION: The identified neural signatures propose that suboptimal robotic actions elicit enhanced, sustained brain dynamics linked to continuous attention allocation, action monitoring, conflict detection, and ongoing evaluative processing. The findings highlight the importance of prioritizing late evaluative brain signatures in BCIs to classify robotic actions reliably. These insights have significant implications for advancing machine-learning-based training paradigms.},
}

@article {pmid40051611,
year = {2025},
author = {Martínez-Cagigal, V and Thielen, J and Hornero, R and Desain, P},
title = {Editorial: The role of code-modulated evoked potentials in next-generation brain-computer interfacing.},
journal = {Frontiers in human neuroscience},
volume = {19},
number = {},
pages = {1548183},
pmid = {40051611},
issn = {1662-5161},
}

@article {pmid40051554,
year = {2025},
author = {Teman, SJ and Atwood, TC and Converse, SJ and Fry, TL and Laidre, KL},
title = {Measuring polar bear health using allostatic load.},
journal = {Conservation physiology},
volume = {13},
number = {1},
pages = {coaf013},
pmid = {40051554},
issn = {2051-1434},
abstract = {The southern Beaufort Sea polar bear sub-population (Ursus maritimus) has been adversely affected by climate change and loss of sea ice habitat. Even though the sub-population is likely decreasing, it remains difficult to link individual polar bear health and physiological change to sub-population effects. We developed an index of allostatic load, which represents potential physiological dysregulation. The allostatic load index included blood- and hair-based analytes measured in physically captured southern Beaufort bears in spring. We examined allostatic load in relation to bear body condition, age, terrestrial habitat use and, over time, for bear demographic groups. Overall, allostatic load had no relationship with body condition. However, allostatic load was higher in adult females without cubs that used terrestrial habitats the prior year, indicating potential physiological dysregulation with land use. Allostatic load declined with age in adult females without cubs. Sub-adult males demonstrated decreased allostatic load over time. Our study is one of the first attempts to develop a health scoring system for free-ranging polar bears, and our findings highlight the complexity of using allostatic load as an index of health in a wild species. Establishing links between individual bear health and population dynamics is important for advancing conservation efforts.},
}

@article {pmid40050621,
year = {2025},
author = {Cui, H and Xiao, Y and Yang, Y and Pei, M and Ke, S and Fang, X and Qiao, L and Shi, K and Long, H and Xu, W and Cai, P and Lin, P and Shi, Y and Wan, Q and Wan, C},
title = {A bioinspired in-materia analog photoelectronic reservoir computing for human action processing.},
journal = {Nature communications},
volume = {16},
number = {1},
pages = {2263},
pmid = {40050621},
issn = {2041-1723},
support = {92364106//National Natural Science Foundation of China (National Science Foundation of China)/ ; 92364204//National Natural Science Foundation of China (National Science Foundation of China)/ ; BK20220121//Natural Science Foundation of Jiangsu Province (Jiangsu Provincial Natural Science Foundation)/ ; },
mesh = {Humans ; *Algorithms ; Neural Networks, Computer ; Transistors, Electronic ; },
abstract = {Current computer vision is data-intensive and faces bottlenecks in shrinking computational costs. Incorporating physics into a bioinspired visual system is promising to offer unprecedented energy efficiency, while the mismatch between physical dynamics and bioinspired algorithms makes the processing of real-world samples rather challenging. Here, we report a bioinspired in-materia analogue photoelectronic reservoir computing for dynamic vision processing. Such system is built based on InGaZnO photoelectronic synaptic transistors as the reservoir and a TaOX-based memristor array as the output layer. A receptive field inspired encoding scheme is implemented, simplifying the feature extraction process. High recognition accuracies (>90%) on four motion recognition datasets are achieved based on such system. Furthermore, falling behaviors recognition is also verified by our system with low energy consumption for processing per action (~45.78 μJ) which outperforms most previous reports on human action processing. Our results are of profound potential for advancing computer vision based on neuromorphic electronics.},
}

Humans
*Algorithms
Neural Networks, Computer
Transistors, Electronic

@article {pmid40049535,
year = {2025},
author = {Kong, L and Zhang, Q and Wang, H and Xu, Y and Xu, C and Chen, Y and Lu, J and Hu, S},
title = {Exploration of the optimized portrait of omega-3 polyunsaturated fatty acids in treating depression: A meta-analysis of randomized-controlled trials.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jad.2025.03.006},
pmid = {40049535},
issn = {1573-2517},
abstract = {BACKGROUND: According to previous studies, omega-3 polyunsaturated fatty acids (PUFAs) are controversial for the efficacy of treating depression.

AIMS: This meta-analysis aims to investigate whether omega-3 PUFAs are able to treat depression, and find out the most beneficial clinical portrait.

METHODS: More than two reviewers searched six registries, and 36 studies were eventually considered eligible. The PRISMA guidelines were used for data extraction, Cochrane Handbook for quality assessment, and random effects model for data pooling.

OUTCOMES: Significant heterogeneity and publication bias were observed. According to the results, significant efficacy was detected in the overall analysis [SMD = -0.26, 95 % CI = (-0.41, -0.11)] and several subgroups, while total daily dosage might be a potential heterogeneity source (P < 0.05). No between-group difference was observed in the rate of response [RR = 0.99, 95 % CI = (0.82, 1.20)], remission [RR = 1.17, 95 % CI = (0.92, 1.48)], and adverse events [RR = 1.07, 95 % CI = (0.90, 1.29)]. Total daily intake of eicosapentaenoic acid (EPA) and remission rate conformed to linear correlation (P < 0.05).

CONCLUSIONS: 1) Omega-3 PUFAs might be effective in treating depression; 2) For Asian patients with mild to moderate depression and no other baseline medication, over 8 weeks of omega-3 PUFAs 1000-1500 mg/day with ratio of EPA/docosahexaenoic acid (DHA) between 1:1 and 2:1 might benefit the most; 3) Omega-3 PUFAs are no superior than placebo in rates of response, remission, and adverse events. Although several limitations exist, the evidence-based information provides guidance for clinical practice and directions for further research.

PROSPERO REGISTRATION NUMBER: CRD42023464823.},
}

@article {pmid39983236,
year = {2025},
author = {Phang, CR and Hirata, A},
title = {Explainable multiscale temporal convolutional neural network model for sleep stage detection based on electroencephalogram activities.},
journal = {Journal of neural engineering},
volume = {22},
number = {2},
pages = {},
doi = {10.1088/1741-2552/adb90c},
pmid = {39983236},
issn = {1741-2552},
mesh = {Humans ; *Electroencephalography/methods ; *Neural Networks, Computer ; *Sleep Stages/physiology ; Male ; Female ; Adult ; Polysomnography/methods ; Young Adult ; Middle Aged ; },
abstract = {Objective.Humans spend a significant portion of their lives in sleep (an essential driver of body metabolism). Moreover, as sleep deprivation could cause various health complications, it is crucial to develop an automatic sleep stage detection model to facilitate the tedious manual labeling process. Notably, recently proposed sleep staging algorithms lack model explainability and still require performance improvement.Approach.We implemented multiscale neurophysiology-mimicking kernels to capture sleep-related electroencephalogram (EEG) activities at varying frequencies and temporal lengths; the implemented model was named 'multiscale temporal convolutional neural network (MTCNN).' Further, we evaluated its performance using an open-source dataset (Sleep-EDF Database Expanded comprising 153 d of polysomnogram data).Main results.By investigating the learned kernel weights, we observed that MTCNN detected the EEG activities specific to each sleep stage, such as the frequencies, K-complexes, and sawtooth waves. Furthermore, regarding the characterization of these neurophysiologically significant features, MTCNN demonstrated an overall accuracy (OAcc) of 91.12% and a Cohen kappa coefficient of 0.86 in the cross-subject paradigm. Notably, it demonstrated an OAcc of 88.24% and a Cohen kappa coefficient of 0.80 in the leave-few-days-out analysis. Our MTCNN model also outperformed the existing deep learning models in sleep stage classification even when it was trained with only 16% of the total EEG data, achieving an OAcc of 85.62% and a Cohen kappa coefficient of 0.75 on the remaining 84% of testing data.Significance.The proposed MTCNN enables model explainability and it can be trained with lesser amount of data, which is beneficial to its application in the real-world because large amounts of training data are not often and readily available.},
}

Humans
*Electroencephalography/methods
*Neural Networks, Computer
*Sleep Stages/physiology
Male
Female
Adult
Polysomnography/methods
Young Adult
Middle Aged

@article {pmid40049458,
year = {2025},
author = {Premchand, B and Toe, KK and Wang, C and Wan, KR and Selvaratnam, T and Toh, VE and Ng, WH and Libedinsky, C and Chen, W and Lim, R and Cheng, MY and Gao, Y and Ang, KK and So, RQY},
title = {Comparing a BCI Communication System in a Patient with Multiple System Atrophy, with an animal model.},
journal = {Brain research bulletin},
volume = {},
number = {},
pages = {111289},
doi = {10.1016/j.brainresbull.2025.111289},
pmid = {40049458},
issn = {1873-2747},
abstract = {Paralysis affects many people worldwide, and the people affected often suffer from impaired communication. We developed a microelectrode-based Brain-Computer Interface (BCI) for enabling communication in patients affected by paralysis, and implanted it in a patient with Multiple System Atrophy (MSA), a neurodegenerative disease that causes widespread neural symptoms including paralysis. To verify the effectiveness of the BCI system, it was also tested by implanting it in a non-human primate (NHP). Data from the human and NHP were used to train binary classifiers two different types of machine learning models: a Linear Discriminant Analysis (LDA) model, and a Long Short-Term Memory (LSTM)-based Artificial Neural Network (ANN). The LDA model performed at up to 72.7% accuracy for binary decoding in the human patient, however, performance was highly variable and was much lower on most recording days. The BCI system was able to accurately decode movement vs non-movement in the NHP (accuracy using LDA: 82.7 ± 3.3%, LSTM: 83.7 ± 2.2%, 95% confidence intervals), however it was not able to with recordings from the human patient (accuracy using LDA: 47.0 ± 5.1%, LSTM: 44.6 ± 9.9%, 95% confidence intervals). We discuss how neurodegenerative diseases such as MSA can impede BCI-based communication, and postulate on the mechanisms by which this may occur.},
}

@article {pmid40049046,
year = {2025},
author = {Chai, C and Yang, X and Zheng, Y and Bin Heyat, MB and Li, Y and Yang, D and Chen, YH and Sawan, M},
title = {Multimodal fusion of magnetoencephalography and photoacoustic imaging based on optical pump: Trends for wearable and noninvasive Brain-Computer interface.},
journal = {Biosensors & bioelectronics},
volume = {278},
number = {},
pages = {117321},
doi = {10.1016/j.bios.2025.117321},
pmid = {40049046},
issn = {1873-4235},
abstract = {Wearable noninvasive brain-computer interface (BCI) technologies, such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), have experienced significant progress since their inception. However, these technologies have not achieved revolutionary advancements, largely because of their inherently low signal-to-noise ratio and limited penetration depth. In recent years, the application of quantum-theory-based optically pumped (OP) technologies, particularly optical pumped magnetometers (OPMs) for magnetoencephalography (MEG) and photoacoustic imaging (PAI) utilizing OP pulsed laser sources, has opened new avenues for development in noninvasive BCIs. These advanced technologies have garnered considerable attention owing to their high sensitivity in tracking neural activity and detecting blood oxygen saturation. This paper represents the first attempt to discuss and compare technologies grounded in OP theory by examining the technical advantages of OPM-MEG and PAI over traditional EEG and fNIRS. Furthermore, the paper investigates the theoretical and structural feasibility of hardware reuse in OPM-MEG and PAI applications.},
}

@article {pmid40048825,
year = {2025},
author = {Kacker, K and Chetty, N and Feldman, AK and Bennett, J and Yoo, PE and Fry, A and Lacomis, D and Harel, N and Nogueira, RG and Majidi, S and Opie, NL and Collinger, J and Oxley, T and Putrino, D and Weber, DJ},
title = {Motor activity in gamma and high gamma bands recorded with a Stentrode from the human motor cortex in two people with ALS.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adbd78},
pmid = {40048825},
issn = {1741-2552},
abstract = {This study examined the strength and stability of motor signals in low gamma and high gamma bands of vascular electrocorticograms (VECoG) recorded with endovascular stent-electrode arrays (Stentrodes) implanted in the superior sagittal sinus of two participants with severe paralysis due to Amyotrophic Lateral Sclerosis. Methods: VECoG signals were recorded from two participants in the COMMAND trial, an Early Feasibility Study of the Stentrode brain-computer interface (BCI) (NCT05035823). The participants performed attempted movements of their ankles or hands. The signals were band-pass filtered to isolate low gamma (30-70 Hz) and high gamma (70-200 Hz) components. The strength of VECoG motor activity was measured as signal-to-noise ratio (SNR) and the percentage change in signal amplitude between the rest and attempted movement epochs, which we termed depth of modulation (DoM). We trained and tested classifiers to evaluate the accuracy and stability of detecting motor intent. Results: Both low gamma and high gamma were modulated during attempted movements. For Participant 1, the average DoM across channels and sessions was 125.41 ± 17.53 % for low gamma and 54.23 ± 4.52 % for high gamma, with corresponding SNR values of 6.75 ± 0.37 dB and 3.69 ± 0.28 dB. For Participant 2, the average DoM was 22.77 ± 4.09 % for low gamma and 22.53 ± 2.04 % for high gamma, with corresponding SNR values of 1.72 ± 0.25 dB and 1.73 ± 0.13 dB. VECoG amplitudes remained significantly different between rest and move periods over the 3 - month testing period, with > 90 % accuracy in discriminating attempted movement from rest epochs for both participants. For Participant 1, the average DoM was strongest during attempted movements of both ankles, while for Participant 2, the DoM was greatest for attempted movement of the right hand. The overall classification accuracy was 91.43 % for Participant 1 and 70.37 % for Participant 2 in offline decoding of multiple attempted movements and rest conditions. Significance: By eliminating the need for open brain surgery, the Stentrode offers a promising BCI alternative, potentially enhancing access to BCIs for individuals with severe motor impairments. This study provides preliminary evidence that the Stentrode can detect discriminable signals indicating motor intent, with motor signal modulation observed over the 3 - month testing period reported here.},
}

@article {pmid40048822,
year = {2025},
author = {Berger, LM and Maia de Oliveira Wood, G and Kober, SE},
title = {Manipulating cybersickness in virtual reality-based neurofeedback and its effects on training performance.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adbd76},
pmid = {40048822},
issn = {1741-2552},
abstract = {Virtual Reality (VR) serves as a modern and powerful tool to enrich neurofeedback (NF) and brain-computer interface (BCI) applications as well as to achieve higher user motivation and adherence to training. However, between 20-80% of all the users develop symptoms of cybersickness (CS), such as nausea, oculomotor problems or disorientation during VR interaction, which influence user performance and behaviour in VR. Hence, we investigated whether CS-inducing VR paradigms influence the success of a NF training task. We tested 39 healthy participants (20 female) in a single-session VR-based NF study. One half of the participants was presented with a high CS-inducing VR-environment where movement speed, field of view and camera angle were varied in a CS-inducing fashion throughout the session and the other half underwent NF training in a less CS-inducing VR environment, where those parameters were held constant. The NF training consisted of 6 runs of 3 min each, in which participants should increase their sensorimotor rhythm (SMR, 12-15 Hz) while keeping artifact control frequencies constant (Theta 4-7 Hz, Beta 16-30 Hz). Heart rate and subjectively experienced CS were also assessed. The high CS-inducing condition tended to lead to more subjectively experienced CS nausea symptoms than the low CS-inducing condition. Further, women experienced more CS, a higher heart rate and showed a worse NF performance compared to men. However, the SMR activity during the NF training was comparable between both the high and low CS-inducing groups. Both groups were able to increase their SMR across feedback runs, although, there was a tendency of higher SMR power for male participants in the low CS group. Hence, sickness symptoms in VR do not necessarily impair NF/BCI training success. This takes us one step further in evaluating the practicability of VR in BCI and NF applications. Nevertheless, inter-individual differences in CS susceptibility should be taken into account for VR-based NF applications.},
}

@article {pmid40048236,
year = {2025},
author = {Zhang, Y and Hedley, FE and Zhang, RY and Jin, J},
title = {Toward quantitative cognitive-behavioral modeling of psychopathology: An active inference account of social anxiety disorder.},
journal = {Journal of psychopathology and clinical science},
volume = {},
number = {},
pages = {},
doi = {10.1037/abn0000972},
pmid = {40048236},
issn = {2769-755X},
support = {//National Key R&D Program of China/ ; //National Natural Science Foundation of China/ ; //Zhejiang University; State Key Laboratory of Brain-Machine Intelligence/ ; //Ministry of Education; Key Laboratory of Brain-Machine Intelligence for Information Behavior/ ; },
abstract = {Understanding psychopathological mechanisms is a central goal in clinical science. While existing theories have demonstrated high research and clinical utility, they have provided limited quantitative explanations of mechanisms. Previous computational modeling studies have primarily focused on isolated factors, posing challenges for advancing clinical theories holistically. To address this gap and leverage the strengths of clinical theories and computational modeling in a synergetic manner, it is crucial to develop quantitative models that integrate major factors proposed by comprehensive theoretical models. In this study, using social anxiety disorder (SAD) as an example, we present a novel approach to formalize conceptual models by combining cognitive-behavioral theory (CBT) with active inference modeling, an innovative computational approach that elucidates human cognition and action. This CBT-informed active inference model integrates multiple mechanistic factors of SAD in a quantitative manner. Through a series of simulations, we systematically examined the effects of these factors on the belief about social threat and tendency of engaging in safety behaviors. The resultant model inherits the conceptual comprehensiveness of CBT and the quantitative rigor of active inference modeling, delineating previously elusive pathogenetic pathways and enabling the formulation of concrete model predictions for future research. Overall, this research presents a novel quantitative model of SAD that unifies major mechanistic factors proposed by CBT and active inference modeling. It highlights the feasibility and potential of integrating clinical theory and computational modeling to advance our understanding of psychopathology. (PsycInfo Database Record (c) 2025 APA, all rights reserved).},
}

@article {pmid40047565,
year = {2025},
author = {Haghighi, P and Jeakle, EN and Sturgill, BS and Abbott, JR and Solis, E and Devata, VS and Vijayakumar, G and Hernandez-Reynoso, AG and Cogan, SF and Pancrazio, JJ},
title = {Enhanced Performance of Novel Amorphous Silicon Carbide Microelectrode Arrays in Rat Motor Cortex.},
journal = {Micromachines},
volume = {16},
number = {2},
pages = {},
doi = {10.3390/mi16020113},
pmid = {40047565},
issn = {2072-666X},
support = {2R01TW104344-22S5/NH/NIH HHS/United States ; },
abstract = {Implantable microelectrode arrays (MEAs) enable the recording of electrical activity from cortical neurons for applications that include brain-machine interfaces. However, MEAs show reduced recording capabilities under chronic implantation conditions. This has largely been attributed to the brain's foreign body response, which is marked by neuroinflammation and gliosis in the immediate vicinity of the MEA implantation site. This has prompted the development of novel MEAs with either coatings or architectures that aim to reduce the tissue response. The present study examines the comparative performance of multi-shank planar, silicon-based devices and low-flexural-rigidity amorphous silicon carbide (a-SiC) MEAs that have a similar architecture but differ with respect to the shank cross-sectional area. Data from a-SiC arrays were previously reported in a prior study from our group. In a manner consistent with the prior work, larger cross-sectional area silicon-based arrays were implanted in the motor cortex of female Sprague-Dawley rats and weekly recordings were made for 16 weeks after implantation. Single unit metrics from the recordings were compared over the implantation period between the device types. Overall, the expression of single units measured from a-SiC devices was significantly higher than for silicon-based MEAs throughout the implantation period. Immunohistochemical analysis demonstrated reduced neuroinflammation and gliosis around the a-SiC MEAs compared to silicon-based devices. Our findings demonstrate that the a-SiC MEAs with a smaller shank cross-sectional area can record single unit activity with more stability and exhibit a reduced inflammatory response compared to the silicon-based device employed in this study.},
}

@article {pmid40046512,
year = {2025},
author = {Fang, K and Wang, Z and Tang, Y and Guo, X and Li, X and Wang, W and Liu, B and Dai, Z},
title = {Dynamically Controlled Flight Altitudes in Robo-Pigeons via Locus Coeruleus Neurostimulation.},
journal = {Research (Washington, D.C.)},
volume = {8},
number = {},
pages = {0632},
pmid = {40046512},
issn = {2639-5274},
abstract = {Robo-pigeons, a novel class of hybrid robotic systems developed using brain-computer interface technology, hold marked promise for search and rescue missions due to their superior load-bearing capacity and sustained flight performance. However, current research remains largely confined to laboratory environments, and precise control of their flight behavior, especially flight altitude regulation, in a large-scale spatial range outdoors continues to pose a challenge. Herein, we focus on overcoming this limitation by using electrical stimulation of the locus coeruleus (LoC) nucleus to regulate outdoor flight altitude. We investigated the effects of varying stimulation parameters, including stimulation frequency (SF), interstimulus interval (ISI), and stimulation cycles (SC), on the flight altitude of robo-pigeons. The findings indicate that SF functions as a pivotal switch controlling the ascending and descending flight modes of the robo-pigeons. Specifically, 60 Hz stimulation effectively induced an average ascending flight of 12.241 m with an 87.72% success rate, while 80 Hz resulted in an average descending flight of 15.655 m with a 90.52% success rate. SF below 40 Hz did not affect flight altitude change, whereas over 100 Hz caused unstable flights. The number of SC was directly correlated with the magnitude of altitude change, enabling quantitative control of flight behavior. Importantly, electrical stimulation of the LoC nucleus had no significant effects on flight direction. This study is the first to establish that targeted variation of electrical stimulation parameters within the LoC nucleus can achieve precise altitude control in robo-pigeons, providing new insights for advancing the control of flight animal-robot systems in real-world applications.},
}

@article {pmid40044089,
year = {2025},
author = {Jialin, A and Zhang, HG and Wang, XH and Wang, JF and Zhao, XY and Wang, C and Cao, MN and Li, XJ and Li, Y and Cao, LL and Zhong, BL and Deng, W},
title = {Cortical activation patterns in generalized anxiety and major depressive disorders measured by multi-channel near-infrared spectroscopy.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jad.2025.02.116},
pmid = {40044089},
issn = {1573-2517},
abstract = {BACKGROUND: Major depressive disorder (MDD) and generalized anxiety disorder (GAD) are highly prevalent mental disorders in psychiatry, but their overlapping symptoms often complicate precise diagnoses. This study aims to explore differential brain activation patterns in healthy controls (HC), MDD, and GAD groups through functional near-infrared spectroscopy (fNIRS) during the verbal fluency task (VFT) to enhance the accuracy of clinical diagnoses.

METHODS: This study recruited 30 patients with MDD, 45 patients with GAD, and 34 demographically matched HCs. Hemodynamic changes in the prefrontal cortex (PFC) and temporal lobes were measured using a 48-channel fNIRS during the VFT task. Demographics information, clinical characteristics and VFT performance were also recorded.

RESULTS: Compared to HCs, both MDD and GAD share a neurobiological phenotype of hypoactivation in the dorsolateral prefrontal cortex (DLPFC) and medial prefrontal cortex (mPFC) during VFT. Moreover, MDD patients exhibited significantly greater hypoactivation in the left DLPFC and right mPFC than GAD patients.

CONCLUSIONS: Although both GAD and MDD patients exhibit disrupted cortical function, the impairment is less severe in GAD. These findings provide preliminary neurophysiological evidence supporting the utility of the fNIRS-VFT paradigm in differentiating GAD from MDD. This approach may complement traditional diagnostic methods, inform targeted interventions, and ultimately enhance patient outcomes.},
}

@article {pmid40044088,
year = {2025},
author = {Liang, S and Gao, Y and Palaniyappan, L and Song, XM and Zhang, T and Han, JF and Tan, ZL and Li, T},
title = {Transcriptional substrates of cortical thickness alterations in anhedonia of major depressive disorder.},
journal = {Journal of affective disorders},
volume = {},
number = {},
pages = {},
doi = {10.1016/j.jad.2025.03.003},
pmid = {40044088},
issn = {1573-2517},
abstract = {BACKGROUND: Anhedonia is a core symptom of major depressive disorder (MDD), which has been shown to be associated with abnormalities in cortical morphology. However, the correlation between cortical thickness (CT) changes with anhedonia in MDD and gene expression remains unclear.

METHODS: We investigated the link between brain-wide gene expression and CT correlates of anhedonia in individuals with MDD, using 7 Tesla neuroimaging and a publicly available transcriptomic dataset. The interest-activity score was used to evaluation MDD with high anhedonia (HA) and low anhedonia (LA). Nineteen patients with HA, nineteen patients with LA, and twenty healthy controls (HC) were enrolled. We investigated CT alterations of anhedonia subgroups relative to HC and related cortical gene expression, enrichment and specific cell types. We further used Neurosynth and von Economo-Koskinas atlas to assess the meta-analytic cognitive functions and cytoarchitectural variation associated with anhedonia-related cortical changes.

RESULTS: Both patient subgroups exhibited widespread CT reduction, with HA manifesting more pronounced changes. Gene expression related to anhedonia had significant spatial correlations with CT differences. Transcriptional signatures related to anhedonia-associated cortical thinning were connected to mitochondrial dysfunction and enriched in adipogenesis, oxidative phosphorylation, mTORC1 signaling pathways, involving neurons, astrocytes, and oligodendrocytes. These CT alterations were significantly correlated with meta-analytic terms involving somatosensory processing and pain perception. HA had reduced CT within the somatomotor and ventral attention networks, and in agranular cortical regions.

LIMITATIONS: These include measuring anhedonia using interest-activity score and employing a cross-sectional design.

CONCLUSIONS: This study sheds light on the molecular basis underlying gene expression associated with anhedonia in MDD, suggesting directions for targeted therapeutic interventions.},
}

@article {pmid40043367,
year = {2025},
author = {Demchenko, I and Shavit, T and Benyamini, M and Zacksenhouse, M},
title = {Self-correcting brain computer interface based on classification of multiple error-related potentials.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adbcda},
pmid = {40043367},
issn = {1741-2552},
abstract = {ObjectiveElectroencephalogram (EEG) based brain-computer interfaces (BCIs) have shown tremendous promise in facilitating direct non-invasive brain-control over external devices. However, their practical application is hampered due to errors in command interpretation. A promising strategy for improving BCI accuracy is based on detecting error-related potentials (ErrPs), which are EEG potentials evoked in response to errors. Thus, performance can be improved by undoing actions that evoke potentials that the BCI detects as ErrPs. To achieve further improvement, we aimed to classify the type of error and correct, rather than just undo, erroneous actions. The objectives of this study are to develop an error classifier (EC) and to investigate the hypothesis that correcting the actions according to the EC decisions improves performance.ApproachTo evaluate our hypothesis we developed a BCI application to control the pose of virtual hands with three possible commands: change the pose of either the right or left hand and maintain pose. Thus, when an action elicits an ErrP, the identity of the correct command is still undecided. The self-correcting BCI included an EC and was developed in three phases: hand control, initial brain control and self-correcting brain control. The first two phases were conducted by 22 participants, and half of them (n=11) also completed the last phase.Main Results Detecting the type of error and correcting actions accordingly improved the success rate of the self-correcting BCI for each participant (n=11), with a significant average improvement of 6.6% and best improvement of 13.5%.Significance Self-correction, based on an EC, was demonstrated to improve the accuracy of BCIs for three commands. Thus, our work presents a significant step toward the development of more reliable and user-friendly non-invasive BCIs.},
}

@article {pmid40043361,
year = {2025},
author = {Susnoschi Luca, I and Vuckovic, A},
title = {How are opposite neurofeedback tasks represented at cortical and corticospinal tract levels?.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adbcdb},
pmid = {40043361},
issn = {1741-2552},
abstract = {The study objective was to characterise indices of learning and patterns of connectivity in two neurofeedback (NF) paradigms that modulate mu oscillations in opposite directions, and the relationship with change in excitability of the corticospinal tract (CST). Approach: Forty-three healthy volunteers participated in 3 NF sessions for upregulation (N=24) or downregulation (N=19) of individual alpha (IA) power at central location Cz. Brain signatures from multichannel electroencephalogram (EEG) were analysed, including oscillatory (power, spindles), non-oscillatory components (Hurst exponent), and effective connectivity (Directed Transfer Function) of participants who were successful at enhancing or suppressing IA power at Cz. CST excitability was studied through leg motor-evoked potential before and after the last NF session. We assessed whether participants modulated widespread alpha or central mu rhythm through the use of current source density derivation (CSD), and related the change in power in mu and upper half of mu band, to CST excitability change. Main results: In the last session, IA/mu power suppression was achieved by 79% of participants, while 63% enhanced IA. CSD-EEG revealed that mu power was upregulated through an increase in the incidence rate of bursts of alpha band activity, while downregulation involved changes in oscillation amplitude and temporal patterns. Neuromodulation also influenced frequencies adjacent to the targeted band, indicating the use of common mental strategies within groups. Directed transfer function analysis showed, for both groups, significant connectivity between structures associated with motor imagery tasks, known to modulate the excitability of the motor cortex, although most connections did not remain significant after correcting for multiple comparisons. CST excitability modulation was related to the absolute amplitude of upper mu modulation, rather than the modulation direction. Significance: The upregulation and downregulation of IA/mu power during NF, with respect to baseline were achieved via distinct mechanisms involving oscillatory and non-oscillatory EEG features. Mu enhancement and suppression post-NF and during the last NF block with respect to the baseline, respectively corresponded to opposite trends in motor-evoked potential changes post-NF. The ability of NF to modulate CST excitability could be a valuable rehabilitation tool for central nervous system disorders (stroke, spinal cord injury), where increased excitability and neural plasticity are desired. This work may inform future neuromodulation protocols and may improve NF training effectiveness by rewarding certain EEG signatures.},
}

@article {pmid40043320,
year = {2025},
author = {Alcolea, PI and Ma, X and Bodkin, KL and Miller, LE and Danziger, ZC},
title = {Less is more: selection from a small set of options improves BCI velocity control.},
journal = {Journal of neural engineering},
volume = {},
number = {},
pages = {},
doi = {10.1088/1741-2552/adbcd9},
pmid = {40043320},
issn = {1741-2552},
abstract = {Decoding algorithms used in invasive brain-computer interfaces (iBCIs) typically convert neural activity into continuously varying velocity commands. We hypothesized that putting constraints on which decoded velocity commands are permissible could improve user performance. To test this hypothesis, we designed the discrete direction selection (DDS) decoder, which uses neural activity to select among a small menu of preset cursor velocities. Approach. We tested DDS in a closed-loop cursor control task against many common continuous velocity decoders in both a human-operated real-time iBCI simulator (the jaBCI) and in a monkey using an iBCI. In the jaBCI, we compared performance across four visits by each of 48 naïve, able-bodied human subjects using either DDS, direct regression with assist (an affine map from neural activity to cursor velocity, DR-A), ReFIT, or the velocity Kalman Filter (vKF). In a follow up study to verify the jaBCI results, we compared a monkey's performance using an iBCI with either DDS or the Wiener filter decoder (a direct regression decoder that includes time history, WF). Main Result. In the jaBCI, DDS substantially outperformed all other decoders with 93% mean targets hit per day compared to DR-A, ReFIT, and vKF with 56%, 39%, and 26% mean targets hit, respectively. With the iBCI, the monkey achieved a 61% success rate with DDS and a 37% success rate with WF. Significance. Discretizing the decoded velocity with DDS effectively traded high resolution velocity commands for less tortuous and lower noise trajectories, highlighting the potential benefits of discretization in simplifying online BCI control. .},
}

@article {pmid40043184,
year = {2025},
author = {Chandler, JA},
title = {Inferring Mental States from Brain Data: Ethico-legal Questions about Social Uses of Brain Data.},
journal = {The Hastings Center report},
volume = {55},
number = {1},
pages = {22-32},
doi = {10.1002/hast.4958},
pmid = {40043184},
issn = {1552-146X},
support = {//ERANET-Neuron Program/ ; /CAPMC/CIHR/Canada ; },
mesh = {Humans ; *Brain ; Privacy/legislation & jurisprudence ; Reproducibility of Results ; },
abstract = {Neurotechnologies that collect and interpret data about brain activity are already in use for medical and nonmedical applications. Refinements of existing noninvasive techniques and the discovery of new ones will likely encourage broader uptake. The increased collection and use of brain data and, in particular, their use to infer the existence of mental states have led to questions about whether mental privacy may be threatened. It may be threatened if the brain data actually support inferences about the mind or if decisions are made about a person in the belief that the inferences are justified. This article considers the chain of inferences lying between data about neural activity and a particular mental state as well as the ethico-legal issues raised by making these inferences, focusing here on what the threshold of reliability should be for using brain data to infer mental states.},
}

Humans
*Brain
Privacy/legislation & jurisprudence
Reproducibility of Results

@article {pmid40043182,
year = {2025},
author = {Yang, Y and Wang, Y and Wang, X},
title = {Harnessing psychedelics for stroke recovery: therapeutic potential and mechanisms.},
journal = {Brain : a journal of neurology},
volume = {},
number = {},
pages = {},
doi = {10.1093/brain/awaf093},
pmid = {40043182},
issn = {1460-2156},
}

@article {pmid40042910,
year = {2025},
author = {Xie, B and Xiong, T and Guo, G and Pan, C and Ma, W and Yu, P},
title = {Bioinspired ion-shuttling memristor with both neuromorphic functions and ion selectivity.},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
volume = {122},
number = {10},
pages = {e2417040122},
doi = {10.1073/pnas.2417040122},
pmid = {40042910},
issn = {1091-6490},
support = {22125406 22074149//MOST | National Natural Science Foundation of China (NSFC)/ ; },
abstract = {The fluidic memristor has attracted growing attention as a promising candidate for neuromorphic computing and brain-computer interfaces. However, a fluidic memristor with ion selectivity as that of natural ion channels remains a key challenge. Herein, inspired by the structure of natural biomembranes, we developed an ion-shuttling memristor (ISM) by utilizing organic solvents and artificial carriers to emulate ion channels embedded in biomembranes, which exhibited both neuromorphic functions and ion selectivity. Pinched hysteresis I-V loop curve, scan rate dependency, and distinctive impedance spectra confirmed the memristive characteristics of the as-prepared device. Moreover, the memory mechanism was discussed theoretically and validated by finite-element modeling. The ISM features multiple neuromorphic functions, such as paired-pulse facilitation, paired-pulse depression, and learning-experience behavior. More importantly, the ion selectivity of the ISM was observed, which allowed further emulation of ion-selective neural functions like resting membrane potential. Benefiting from the structural similarity to membrane-embedded ion channels, the ISM opens the door for ion-based neuromorphic computing and sophisticated chemical regulation by manipulating multifarious ions with neuromorphic functions.},
}

@article {pmid40042891,
year = {2025},
author = {Gielas, AM},
title = {Man, Hibernating Animals, and Poikilothermic: Fish The Present and Future of BCI Technology.},
journal = {Journal of special operations medicine : a peer reviewed journal for SOF medical professionals},
volume = {},
number = {},
pages = {},
doi = {10.55460/FA29-NVKE},
pmid = {40042891},
issn = {1553-9768},
abstract = {In 2024 and early 2025, several successful surgeries involving brain-computer interfaces (BCIs) gained media attention, including those conducted by Elon Musk's company Neuralink, which implanted BCIs in three paralyzed volunteers, allowing them to control computers through thought alone. While the concept of merging humans with machines dates back to the 1960s, BCI technology has now entered the clinical trial stage, with a focus on restoring communication, mobility, and sensation in individuals with severe disabilities and neurodegenerative disorders. For over two decades, BCIs have also been explored as tools to enhance the cognitive and physical abilities of military personnel. However, before Special Operations Forces (SOF) medical staff encounter BCIs in an enhancement capacity, they are likely to first come across them in medical settings. This article provides an overview of BCI technology, focusing on 1) how it works, 2) its potential significance for injured SOF servicemembers, 3) current challenges, and 4) its potential to enhance SOF in the future.},
}

@article {pmid40040918,
year = {2025},
author = {Jiang, Y and Liu, YL and Zhou, X and Shu, QQ and Dong, L and Xu, Z and Wan, JQ},
title = {A retrospective study of the Dual-channels Bolus Contrast Injection (Dc-BCI) technique during endovascular mechanical thrombectomy in the management of acute ischemic stroke due to large-vessel occlusion: a technical report.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1508976},
pmid = {40040918},
issn = {1664-2295},
abstract = {Endovascular mechanical thrombectomy (EMT) is an effective treatment for acute ischemic stroke and identifying the precise thrombus size remains key to a successful EMT. However, no imaging modality has been able to provide this information simultaneously and efficiently in an emergency setting. The present study introduces a novel technique named dual-channel bolus contrast injection (Dc-BCI) for determining thrombus size and location during EMT. In the in vitro study, the Dc-BCI demonstrated an accurate projection of the thrombus size, as the actual thrombus diameter (R[2] = 0.92, p < 0.01) and length (R[2] = 0.94, p < 0.01) exhibited a high degree of correlation with that of obtained from Dc-BCI. Consequently, between February 2023 and August 2024, 87 patients diagnosed with acute cerebral large vessel occlusions were enrolled in the study and received EMT for the treatment of acute cerebral large vessel occlusions. The Dc-BCI was successfully performed in all patients to measure the diameter and length of the thrombus. These information were used to select an appropriate stent-retriever for EMT. The restoration of blood flow was achieved in 84 patients (96.6%) to an mTICI score of 2b/3. Additionally, a low incidence of postoperative complications was observed (e.g., subarachnoid hemorrhage 8% and cerebral hemorrhage 5.7%). In conclusion, it can be posited that the Dc-BCI has the potential to enhance the outcomes of EMT, as it is capable of revealing the thrombus size information, which optimizes the interaction between the stent retriever and the thrombus, while simultaneously reducing the risk of vascular injury that is associated with the prolonged use of the stent retriever.},
}

@article {pmid40040909,
year = {2025},
author = {Tekin, U and Dener, M},
title = {A bibliometric analysis of studies on artificial intelligence in neuroscience.},
journal = {Frontiers in neurology},
volume = {16},
number = {},
pages = {1474484},
pmid = {40040909},
issn = {1664-2295},
abstract = {The incorporation of artificial intelligence (AI) into neuroscience has the potential to significantly enhance our comprehension of brain function and facilitate more effective diagnosis and treatment of neurological disorders. Artificial intelligence (AI) techniques, particularly deep learning and machine learning, offer transformative solutions by improving the analysis of complex neural data, facilitating early diagnosis, and enabling personalized treatment approaches. A bibliometric analysis is a method that employs quantitative techniques for the examination of scientific literature, with the objective of identifying trends in research, evaluating the impact of influential studies, and mapping the networks of collaboration. In light of the accelerated growth and interdisciplinary scope of AI applications in neuroscience, a bibliometric analysis is vital for mapping the landscape, identifying pivotal contributions, and underscoring emerging areas of interest. This study aims to address this need by examining 1,208 studies published between 1983 and 2024 from the Web of Science database. The analysis reveals a notable surge in publications since the mid-2010s, with substantial advancements in neurological imaging, brain-computer interfaces (BCI), and the diagnosis and treatment of neurological diseases. The analysis underscores the pioneering role of countries such as the United States, China, and the United Kingdom in this field and highlights the prevalence of international collaboration. This study offers a comprehensive overview of the current state and future directions of AI applications in neuroscience, as well as an examination of the transformative potential of AI in advancing neurological research and healthcare. It is recommended that future research address the ethical issues, data privacy concerns, and interpretability of AI models in order to fully capitalize on the benefits of AI in neuroscience.},
}

@article {pmid40040811,
year = {2025},
author = {Feng, X and Bao, X and Huang, H and Wang, Z and Hu, W and Xue, C and Song, Z and Cai, Y and Huang, Q and Li, Y},
title = {Frontal gamma-alpha ratio reveals neural oscillatory mechanism of attention shifting in tinnitus.},
journal = {iScience},
volume = {28},
number = {3},
pages = {111929},
pmid = {40040811},
issn = {2589-0042},
abstract = {In clinical practice, the symptoms of tinnitus patients can be temporarily alleviated by diverting their attention away from disturbing sounds. However, the precise mechanisms through which this alleviation occurs are still not well understood. Here, we aimed to directly evaluate the role of attention in tinnitus alleviation by conducting distraction tasks with multilevel loads and resting-state tests among 52 adults with tinnitus and 52 healthy controls. We demonstrated that the abnormal neural oscillations in tinnitus subjects, reflected in an altered gamma/alpha ratio index in the frontal lobe, could be regulated by attention shifting in a linear manner for which the regulatory effect increased with the load of distraction. Quantitative measures of the regulation significantly correlated with symptom severity. Altogether, our work provides proof-of-concept for the role of attention in tinnitus perception and lays a solid foundation to support evidence-based applications of attention shifting in clinical interventions for tinnitus.},
}

@article {pmid40040231,
year = {2024},
author = {Chaudhry, ZA and Baxter, RH and Fu, JL and Wang, PT and Sohn, WJ and Do, AH},
title = {Feasibility of Immersive Virtual Reality Feedback for Enhancing Learning in Brain-Computer Interface Control of Ambulation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782667},
pmid = {40040231},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Virtual Reality ; *Walking/physiology ; *Electroencephalography ; Male ; Adult ; Feasibility Studies ; Learning ; Female ; Young Adult ; Spinal Cord Injuries/rehabilitation/physiopathology ; User-Computer Interface ; },
abstract = {After prolonged paralysis, paraplegic spinal cord injury (SCI) patients typically lose the ability to generate the expected electroencephalogram (EEG) α/β modulation associated with leg movements. Brain computer interface (BCI)-controlled ambulation devices have emerged as a way to restore brain-controlled walking, but this loss of EEG signal modulation may impede the ability to operate such systems and prolonged training may be necessary to restore this physiologic phenomenon. To address this issue, this study explores the use of immersive virtual reality (VR) in providing more convincing feedback to enhance learning within a BCI training paradigm. Here, an EEG-based BCI-controlled walking simulator with an environment composed of 10 designated stop zones along a linear course was used to test this concept. Able-bodied subjects were tasked with using idling or kinesthetic motor imagery (KMI) of gait to control an avatar to either dwell at each designated stop for 5 s or advance along the course respectively. Subject performance was measured using a composite score per run and learning rate across runs. Composite scores were calculated as the geometric mean of two subscores: a stop score (reflecting the number of successful stops), and a time score (reflecting how fast the course was completed). The learning rate was calculated as the slope of the composite scores across all runs. A random walk procedure was performed to determine the statistical likelihood that each BCI run was purposeful (p≤ 0.001). Three able-bodied subjects were recruited (2 in immersive VR group and 1 in non-immersive VR group), and operated the simulator for up to 4 separate visits. The immersive VR group achieved an average composite score of 60.4% ± 12.9, while the non-VR group had an average composite score of 79.0% ± 12.2. The learning rate was 1.07%/run and 0.42%/run for the immersive and non-immersive VR groups, respectively. Purposeful control was attained in a higher proportion of runs for the immersive VR group than in the non-immersive VR group. Although limited by small sample size, this study demonstrates a conceptual framework of implementing immersive VR feedback using more convincing sensory feedback to aid training with BCI devices. Future work will test this protocol in SCI patients and with larger sample size.},
}

Humans
*Brain-Computer Interfaces
*Virtual Reality
*Walking/physiology
*Electroencephalography
Male
Adult
Feasibility Studies
Learning
Female
Young Adult
Spinal Cord Injuries/rehabilitation/physiopathology
User-Computer Interface

@article {pmid40040213,
year = {2024},
author = {Okitsu, K and Isezaki, T and Obara, K and Nishimura, Y},
title = {Enhancing Brain Machine Interface Decoding Accuracy through Domain Knowledge Integration.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782166},
pmid = {40040213},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Animals ; Macaca mulatta ; Muscle, Skeletal/physiology ; Algorithms ; Humans ; },
abstract = {This paper introduces a novel decoding approach for Brain Machine Interface (BMI) that enhances the estimation accuracy and stability of muscle activity by incorporating domain knowledge of motor control. Our approach uniquely integrates domain knowledge, focusing on the relationship between torque direction and muscle activity in isometric wrist tasks. We demonstrate the effectiveness of our approach through decoding analysis with non-human primates performing a wrist torque tracking task. By implementing a Kalman filter augmented with models of muscle activity and torque for specific movement directions, we show significant improvements compared to vanilla Kalman filter in the accuracy of muscle activity estimation. The proposed approach presents a promising direction for enhancing the performance of BMI by leveraging domain-specific insights into motor control.},
}

*Brain-Computer Interfaces
Animals
Macaca mulatta
Muscle, Skeletal/physiology
Algorithms
Humans

@article {pmid40040208,
year = {2024},
author = {Li, D and Shin, HB and Yin, K and Lee, SW},
title = {Domain-Incremental Learning Framework for Continual Motor Imagery EEG Classification Task.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10781886},
pmid = {40040208},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; Imagination/physiology ; Machine Learning ; Algorithms ; Signal Processing, Computer-Assisted ; },
abstract = {Due to inter-subject variability in electroencephalogram (EEG) signals, the generalization ability of many existing brain-computer interface (BCI) models is significantly limited. Although transfer learning (TL) offers a temporary solution, in scenarios requiring sustained knowledge transfer, the performance of TL-based models gradually declines as the number of transfers increases-a phenomenon known as catastrophic forgetting. To address this issue, we introduce a novel domain-incremental learning framework for the continual motor imagery (MI) EEG classification. Specifically, to learn and retain common features between subjects, we separate latent representations into subject-invariant and subject-specific features through adversarial training, while also proposing an extensible architecture to preserve features that are easily forgotten. Additionally, we incorporate a memory replay mechanism to reinforce previously acquired knowledge. Through extensive experiments, we demonstrate our framework's effectiveness in mitigating forgetting within the continual MI-EEG classification task.},
}

*Electroencephalography/methods
Humans
*Brain-Computer Interfaces
Imagination/physiology
Machine Learning
Algorithms
Signal Processing, Computer-Assisted

@article {pmid40040206,
year = {2024},
author = {Huang, CM and Lai, WL and Yang, CC and Hsieh, YJ and Wu, CM and Lee, CH},
title = {EEG Channel Localization and Selection via Training with Noise Injection for BCI Applications.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782774},
pmid = {40040206},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Algorithms ; Signal Processing, Computer-Assisted ; Signal-To-Noise Ratio ; },
abstract = {Electroencephalography (EEG) is crucial for monitoring brain activity in neuroscience and clinical applications. However, the multitude of channels recorded by scalp electrodes poses challenges, including impractical usage and high model complexity. This paper addresses the challenges of high dimensionality in EEG data and introduces an innovative EEG channel selection algorithm, LSvT-NI, based on model training and noise injection, achieving substantial reductions in channels, model size, and complexity while maintaining high classification accuracy. Validated through experiments on EEGNet and the BCI Competition IV 2a dataset, the algorithm proves beneficial for practical and cost-efficient scenarios. Specifically, experiments on the BCI Competition IV 2a dataset demonstrate that LSvT-NI with white noise and pink noise at 5dB SNR achieves a remarkable 77.3% and 72.7% reduction in channels, along with 11.7% and 11% reductions in model size, and 86.9% and 71.8% in computation complexity.},
}

*Electroencephalography/methods
Humans
*Brain-Computer Interfaces
*Algorithms
Signal Processing, Computer-Assisted
Signal-To-Noise Ratio

@article {pmid40040181,
year = {2024},
author = {Sartipi, S and Cetin, M},
title = {Robust EEG-based Emotion Recognition Using an Inception and Two-sided Perturbation Model.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782060},
pmid = {40040181},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; Brain-Computer Interfaces ; Signal Processing, Computer-Assisted ; Algorithms ; Deep Learning ; },
abstract = {Automated emotion recognition using electroencephalogram (EEG) signals has gained substantial attention. Although deep learning approaches exhibit strong performance, they often suffer from vulnerabilities to various perturbations, like environmental noise and adversarial attacks. In this paper, we propose an Inception feature generator and two-sided perturbation (INC-TSP) approach to enhance emotion recognition in brain-computer interfaces. INC-TSP integrates the Inception module for EEG data analysis and employs two-sided perturbation (TSP) as a defensive mechanism against input perturbations. TSP introduces worst-case perturbations to the model's weights and inputs, reinforcing the model's elasticity against adversarial attacks. The proposed approach addresses the challenge of maintaining accurate emotion recognition in the presence of input uncertainties. We validate INC-TSP in a subject-independent three-class emotion recognition scenario, demonstrating robust performance.},
}

Humans
*Electroencephalography/methods
*Emotions/physiology
Brain-Computer Interfaces
Signal Processing, Computer-Assisted
Algorithms
Deep Learning

@article {pmid40040170,
year = {2024},
author = {Meng, J and Yang, M and Zhang, S and Xu, M and Meng, L and Ming, D},
title = {An online brain-computer interface for a precise positioning of target based on rapid serial visual presentation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782815},
pmid = {40040170},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Male ; Adult ; Female ; Young Adult ; Evoked Potentials/physiology ; Brain/physiology ; Photic Stimulation ; },
abstract = {The brain-computer interface (BCI) based on rapid serial visual presentation (RSVP) provides a novel approach for efficiently optimizing traditional machine-based target detection, revealing a broad application prospect in security, entrainment, monitoring, etc. A bottleneck of current RSVP-BCI is that its detectable result is limited to a binary way, i.e., target vs. non-target, more detailed and important information about targets, such as the precise position, remains undetectable. To solve this problem, this study investigated the relationship between targets positions (up, down, left, right) and electroencephalogram (EEG) characteristics, and tested the separability of EEGs induced by the four targets positions in an online RSVP-BCI. Twelve healthy subjects participated in this study, event-related potential (ERP), topographies, laterality index (LI), discriminant canonical pattern matching (DCPM) methods were used to analyzed the EEG data. Consequently, left-right targets induced ipsilateral ERPs between bilateral hemispheres; when targets appeared at up and down positions, opposite ERPs were found between frontal and occipital areas; up-down and left-right difference reached its maximum in the 140~190ms and 190~240ms temporal window, respectively. Single-trial classification showed five-class balanced accuracy (BACC) (non-target, target at up/ down/ left/ right position) was 71.02% and 67.91% for offline and online sessions, respectively. The results provide new understanding of the RSVP features for developing BCIs.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography/methods
Male
Adult
Female
Young Adult
Evoked Potentials/physiology
Brain/physiology
Photic Stimulation

@article {pmid40040166,
year = {2024},
author = {Zhuo, F and Lv, B and Tang, F},
title = {Time Window Optimization for Riemannian Geometry-based Motor Imagery EEG Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782640},
pmid = {40040166},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Brain-Computer Interfaces ; Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; },
abstract = {The existing Riemannian geometry-based approaches for brain computer interface (BCI) employ fixed time windows. However, the inherent variability and dynamic changes among subjects necessitate robust and adaptive solutions for time window optimization. Recognizing the current limitations of Riemannian classifiers, we propose a time window selection confidence metric (TWSCM) based on Riemannian geometry. This metric operates on the manifold of symmetric positive definite (SPD) matrices, providing a theoretically grounded and computationally efficient approach for time window optimization. The optimization process is unsupervised, which is able to deal with the online scenario without training labels. Experimental results on the BCI competition IV dataset IIa demonstrate that the classification performance is significantly improved for most subjects. The average performance over six subjects improved by 7.52%. The simulated online experiment shows enhanced performance in comparison to baseline experiments without time window optimization. Additionally, an in-depth analysis of TWSCM provides insights into performance variations among subjects. Overall, this paper introduces the first time window optimization method within the Riemannian geometric framework, presenting an effective and interpretable approach for optimizing time windows in motor imagery classification, providing a novel and promising perspective in EEG signal analysis.},
}

Humans
*Electroencephalography/methods
*Brain-Computer Interfaces
Algorithms
Signal Processing, Computer-Assisted
Imagination/physiology

@article {pmid40040155,
year = {2024},
author = {Huang, J and Tostado-Marcos, P and Narasimha, SM and Patel, AN and Arneodo, EM and Gentner, TQ and Mishne, G and Gilja, V},
title = {Guiding Brain-to-Vocalization Decoder Design Using Structured Generalization Error.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782761},
pmid = {40040155},
issn = {2694-0604},
mesh = {Animals ; *Finches/physiology ; *Vocalization, Animal/physiology ; Humans ; Brain/physiology ; Brain-Computer Interfaces ; Speech/physiology ; },
abstract = {State-of-the-art intracortical neuroprostheses currently enable communication at 60+ words per minute for anarthric individuals by training on over 10K sentences to account for phoneme variability in different word contexts. There is limited understanding about whether this performance can be maintained in decoding naturalistic speech with 40K+ word vocabularies across elicited, spontaneous, and conversational speech contexts. We introduce a vocal-unit-level generalization test to explicitly evaluate neural decoder performance with an expanded and more diverse behavioral repertoire. Tested on neural decoders modeling zebra finch vocalization, an analog to human vocal production, we compare three decoders with different input types: spike trains, neural factors, and firing rates. The factors and rates are latent neural features inferred using trained Latent Factor Analysis via Dynamical Systems (LFADS) models that capture the population neural dynamics during vocal production. While the conventional random holdout generalization error measure is similar for all three decoders, factor- and rate-based decoders outperform spike-based decoders when testing vocal-unit-holdout generalization error. These results suggest the later models better adapt to flexible vocalization inference when trained with partial observation of data variation, motivating further exploration of decoders incorporating latent neural and vocalization dynamics.},
}

Animals
*Finches/physiology
*Vocalization, Animal/physiology
Humans
Brain/physiology
Brain-Computer Interfaces
Speech/physiology

@article {pmid40040137,
year = {2024},
author = {Idowu, OP and Kinney-Lang, E and Gulamhusein, A and Irvine, B and Kirton, A and Abou-Zeid, H},
title = {Profiling a Raspberry Pi-Based Motor Imagery Classification to Facilitate At-Home BCI for Children with Disabilities.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-7},
doi = {10.1109/EMBC53108.2024.10781873},
pmid = {40040137},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; Child ; *Children with Disabilities/rehabilitation ; Neural Networks, Computer ; Imagination/physiology ; Electroencephalography/methods ; Machine Learning ; },
abstract = {There has been incremental progress in moving BCI out of the laboratory environment and into the homes of those who would benefit most, especially children living with severe physical disabilities. Practical issues, such as available computational resources and long calibration times, have slowed down the adoption of such systems. To develop an efficient and scalable machine learning framework consistent with early approaches that facilitate at-home BCI use, this study provides valuable insights into measuring the behavioral characteristics of a Raspberry Pi 4 (RPi4) during the operation and execution of standard BCI processes, including the training and evaluation of classifier models. The results, which evaluated ten standard classifiers, including the Riemannian Geometry (RG) framework and more advanced deep learning approaches like Artificial Neural Network (ANN), were profiled on RPi4. These were compared to Desktop and MacBook computations for metrics such as training time, inference time, peak memory, and incremental memory usage, with computational bottlenecks identified. Our assessment revealed comparable performance metrics (84.3% of accuracy, recall, and f1_score, and 84.7% precision) for the neural network models despite the lower computational resources. Profiling results, including 1.74 sec training time, 0.405 sec inference time, 1154.9 MiB peak memory, and 405.2 MiB incremental memory usage, also demonstrated that the RPi4 is a potentially viable device for low-cost BCI systems. However, high-resource demanding classifiers such as ANN may need to be carefully considered in their implementation, which, in turn, will scale down the potential cost and complexity of adopting practical, impactful at-home BCI systems.},
}

Humans
*Brain-Computer Interfaces
Child
*Children with Disabilities/rehabilitation
Neural Networks, Computer
Imagination/physiology
Electroencephalography/methods
Machine Learning

@article {pmid40040110,
year = {2024},
author = {Wang, Z and Liu, Y and Wu, W and Huang, S and An, X and Ming, D},
title = {EEG Pattern Comparison and Classification Performance of Motor Imagery Between Supernumerary and Inherent Limbs.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782967},
pmid = {40040110},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Imagination/physiology ; Brain-Computer Interfaces ; Male ; Adult ; Movement/physiology ; Robotics ; Female ; Young Adult ; },
abstract = {Adding supernumerary robotic limbs (SRLs) to humans and controlling them directly through the brain are main goals for movement augmentation. However, whether neural patterns that are distinct from the traditional inherent limbs motor imagery (MI) paradigm can be extracted, which is essential for the high-dimensional control of external equipment. In this study, a novel type of MI paradigm based on SRLs was proposed, consisting of "the sixth-finger", "the third-arm" and "the third-leg", and validated the distinctness of EEG response patterns between the novel and the traditional (hand, arm and leg) MI paradigm. The results showed that imagining extra limbs induced more obvious event-related desynchronization (ERD) phenomenon in sensorimotor areas compared to imagining inherent limbs. Classification results indicate well separable performance among different mental tasks (all above 86%, with a maximum of 90.5%). This work proposed a novel type of MI paradigm, and offered new way for widening the control bandwidth of the BCI system.},
}

Humans
*Electroencephalography/methods
*Imagination/physiology
Brain-Computer Interfaces
Male
Adult
Movement/physiology
Robotics
Female
Young Adult

@article {pmid40040096,
year = {2024},
author = {Lim, EY and Yin, K and Shin, HB and Lee, SW},
title = {Baseline-Guided Representation Learning for Noise-Robust EEG Signal Classification.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781970},
pmid = {40040096},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Brain-Computer Interfaces ; *Signal Processing, Computer-Assisted ; Deep Learning ; Algorithms ; Signal-To-Noise Ratio ; },
abstract = {Brain-computer interfaces (BCIs) suffer from limited accuracy due to noisy electroencephalography (EEG) signals. Existing denoising methods often remove artifacts such as eye movement or use techniques such as linear detrending, which inadvertently discard crucial task-relevant information. To address this issue, we present BGNet, a novel deep learning framework that leverages underutilized baseline EEG signals for dynamic noise mitigation and robust feature extraction to improve motor imagery (MI) EEG classification. Our approach employs data augmentation to strengthen model robustness, an autoencoder to extract features from baseline and MI signals, a feature alignment module to separate specific task and noise, and a classifier. We achieve state-of-the-art performance, an improvement of 5.9% and 3.7% on the BCIC IV 2a and 2b datasets, respectively. The qualitative analysis of our learned features proves superior representational power over baseline models, a critical aspect in dealing with noisy EEG signals. Our findings demonstrate the efficacy of readily available baseline signals in enhancing performance, opening possibilities for simplified BCI systems in brain-based communication applications.},
}

*Electroencephalography/methods
Humans
*Brain-Computer Interfaces
*Signal Processing, Computer-Assisted
Deep Learning
Algorithms
Signal-To-Noise Ratio

@article {pmid40040056,
year = {2024},
author = {Huang, S and Liu, Y and Xu, W and Wang, Z and Ming, D},
title = {Enhancement of Functional Connectivity in Frontal-Parietal Regions After BCI-Actuated Supernumerary Robotic Finger Training.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781807},
pmid = {40040056},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Fingers/physiology ; Male ; *Robotics ; *Parietal Lobe/physiology/diagnostic imaging ; *Frontal Lobe/physiology/diagnostic imaging ; Magnetic Resonance Imaging ; Adult ; Female ; Young Adult ; },
abstract = {The supernumerary robotic finger (SRF) can expand human hand abilities to achieve motor augmentation, and integrate with brain computer interface (BCI) to free the occupation of inherent body degrees of freedom. However, the neuro remodeling mechanisms of brain-actuated SRF training is not clear. In this study, a BCI-actuated SRF was used to investigate the concurrent changes in behavior and brain activity. After 4 weeks BCI-SRF training, the novel sequence operation accuracy rate enhanced by more than 350% compared with innate finger training (IFT). Task-based fMRI showed a significant increase in lateral activation of sensorimotor cortex and found a significant activation change in S1M1_L area. Moreover, BCI-SRF training significantly increase functional connectivity (FC) between S1M1_L and Frontal_Mid_L compared with IFT at post stage. And this FC increase in frontal-parietal is also significant at post vs pre in BCI-SRF group and significantly correlated with the improvement of motor sequence accuracy rate. Our findings provide useful insights into the enhanced human-machine interaction and this efficacy exhibited significant potential for clinical rehabilitation application.},
}

Humans
*Brain-Computer Interfaces
*Fingers/physiology
Male
*Robotics
*Parietal Lobe/physiology/diagnostic imaging
*Frontal Lobe/physiology/diagnostic imaging
Magnetic Resonance Imaging
Adult
Female
Young Adult

@article {pmid40040035,
year = {2024},
author = {Norouzi, M and Amirani, MZ and Shahriari, Y and Abiri, R},
title = {Precision Enhancement in Sustained Visual Attention Training Platforms: Offline EEG Signal Analysis for Classifier Fine-Tuning.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782784},
pmid = {40040035},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Attention/physiology ; *Brain-Computer Interfaces ; *Support Vector Machine ; *Signal Processing, Computer-Assisted ; Adult ; Male ; Female ; Young Adult ; },
abstract = {In this study, a novel open-source brain-computer interface (BCI) platform was developed to decode scalp electroencephalography (EEG) signals associated with sustained attention. The EEG signal collection was conducted using a wireless headset during a sustained visual attention task, where participants were instructed to discriminate between composite images superimposed with scenes and faces, responding only to the relevant subcategory while ignoring the irrelevant ones. Seven volunteers participated in this experiment. The data collected were subjected to analyses through event-related potential (ERP), Hilbert Transform, and Wavelet Transform to extract temporal and spectral features. For each participant, utilizing its extracted features, personalized Support Vector Machine (SVM) and Random Forest (RF) models with tuned hyperparameters were developed. The models aimed to decode the participant's attentional state towards the face and scene stimuli. The SVM models achieved a higher average accuracy of 80% and an Area Under the Curve (AUC) of 0.86, while the RF models showed an average accuracy of 78% and AUC of 0.8. This work suggests potential applications for the evaluation of visual attention and the development of closed-loop brainwave regulation systems in the future.},
}

Humans
*Electroencephalography/methods
*Attention/physiology
*Brain-Computer Interfaces
*Support Vector Machine
*Signal Processing, Computer-Assisted
Adult
Male
Female
Young Adult

@article {pmid40040033,
year = {2024},
author = {Ferdous, TR and Pollonini, L and Francis, JT},
title = {Enhancing Auditory BCI Performance: Incorporation of Connectivity Analysis.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782147},
pmid = {40040033},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Support Vector Machine ; Brain/physiology/diagnostic imaging ; Acoustic Stimulation ; },
abstract = {Brain connectivity analysis to classify auditory stimuli applicable to invasive auditory BCI technology, particularly intracranial electroencephalography (iEEG) remains an exciting frontier. This study revealed insights into brain network dynamics, improving analysis precision to distinguish related auditory stimuli such as speech and music. We thereby contribute to advancing auditory BCI systems to bridge the gap between noninvasive and invasive BCI by utilizing noninvasive BCI methodological frameworks to invasive BCI (iEEG) data. We focused on the viability of using connectivity matrices in BCI calculated across brain waves such as alpha, beta, theta, and gamma. The research highlights that the traditional machine learning classifier, Support Vector Machine (SVM), demonstrates exceptional capabilities in handling brain connectivity data, exhibiting an outstanding 97% accuracy in classifying brain states, surpassing previous relevant studies with an improvement of 9.64% The results are significant as we show that neural activity in the gamma band provides the best classification performance using connectivity matrices calculated with Phase Locking Values and Coherence methods.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography/methods
Support Vector Machine
Brain/physiology/diagnostic imaging
Acoustic Stimulation

@article {pmid40040032,
year = {2024},
author = {Veeranki, YR and Posada-Quintero, HF},
title = {High-Resolution Time-Frequency Analysis of EEG Signals for Affective Computing.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782482},
pmid = {40040032},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Signal Processing, Computer-Assisted ; *Algorithms ; Brain-Computer Interfaces ; Male ; Female ; Brain/physiology ; Adult ; Emotions/physiology ; },
abstract = {Affective computing is a critical aspect of human-computer interaction. Electroencephalographic (EEG) signals, which reflect electrical brain activity, are widely used for the understanding of human emotional states. However, these signals are nonlinear and nonstationary, making traditional analysis methods insufficient. To address these challenges, recent studies have focused on time-frequency analysis. In this paper, we propose a variable frequency complex demodulation (VFCDM) approach to obtain high-resolution time-frequency spectra (TFS) from EEG signals. First, we compute the TFS using the time-varying optimal parameter search technique to capture the spectral information. Then we generate VFCDM sub-bands and extract statistical features from each of the sub-bands. These features are then used with the Random Forest algorithm to classify arousal and valence dimensions. Our results demonstrate the robustness of this approach and its ability to accurately discriminate complex affective dimensions. The δ-VFCDM and γ-VFCDM bands produced the highest F1 scores of 71.80% for Arousal and 69.55% for Valence differentiation. This work significantly advances EEG-based affective computing and opens avenues for more emotionally attuned human-computer interaction systems.},
}

Humans
*Electroencephalography/methods
*Signal Processing, Computer-Assisted
*Algorithms
Brain-Computer Interfaces
Male
Female
Brain/physiology
Adult
Emotions/physiology

@article {pmid40039972,
year = {2024},
author = {Ye, H and Goerttler, S and He, F},
title = {EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782694},
pmid = {40039972},
issn = {2694-0604},
mesh = {*Electroencephalography/methods ; Humans ; *Signal Processing, Computer-Assisted ; *Neural Networks, Computer ; Electrodes ; Algorithms ; Brain/physiology ; Brain-Computer Interfaces ; },
abstract = {Electroencephalogram (EEG) is a valuable technique to record brain electrical activity through electrodes placed on the scalp. Analyzing EEG signals contributes to the understanding of neurological conditions and developing brain-computer interface. Graph Signal Processing (GSP) has emerged as a promising method for EEG spatial-temporal analysis, by further considering the topological relationships between electrodes. However, existing GSP studies lack interpretability of electrode importance and the credibility of prediction confidence. This work proposes an EEG Graph Mutual Attention Convolutional Network (EEG-GMACN), by introducing an 'Inverse Graph Weight Module' to output interpretable electrode graph weights, enhancing the clinical credibility and interpretability of EEG classification results. Additionally, we incorporate a mutual attention mechanism module into the model to improve its capability to distinguish critical electrodes and introduce credibility calibration to assess the uncertainty of prediction results. This study enhances the transparency and effectiveness of EEG analysis, paving the way for its widespread use in clinical and neuroscience research.},
}

*Electroencephalography/methods
Humans
*Signal Processing, Computer-Assisted
*Neural Networks, Computer
Electrodes
Algorithms
Brain/physiology
Brain-Computer Interfaces

@article {pmid40039945,
year = {2024},
author = {Kanda, T and Isezaki, T and Okitsu, K},
title = {A Study on Changes in Estimation Accuracy for EEG Data During Calibration and Operation in MI-BCI.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782616},
pmid = {40039945},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Calibration ; Male ; Female ; Adult ; Young Adult ; Signal Processing, Computer-Assisted ; Imagination/physiology ; Deep Learning ; },
abstract = {Changes in psychological factors have been suggested to cause variations in brain-computer interface (BCI) performance. More specifically, differences in psychological variables between the calibration and operation phases may cause a decrease in accuracy during operation, presenting a potential challenge for the adoption of BCI technology. The purpose of this study is to analyze the differences in accuracy between the calibration and operation phases of a BCI using a deep learning model. We structured tasks to simulate the calibration and operation phases, and participants performed motor imagery tasks under both conditions. The analysis revealed a significant decrease in accuracy for data obtained under the operation condition, highlighting the need for techniques capable of adapting to the electroencephalography signal data produced when users execute operations.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography/methods
Calibration
Male
Female
Adult
Young Adult
Signal Processing, Computer-Assisted
Imagination/physiology
Deep Learning

@article {pmid40039943,
year = {2024},
author = {Wang, J and Li, X and Huang, Y and Xiao, D and Fan, Y and Huang, W and Hu, Y},
title = {Patient-Involved Validation of A Somatosensory ERP-BCI Facilitated by Electric Stimulation for Stroke Rehabilitation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-6},
doi = {10.1109/EMBC53108.2024.10781706},
pmid = {40039943},
issn = {2694-0604},
mesh = {Humans ; *Stroke Rehabilitation/methods ; *Brain-Computer Interfaces ; Male ; Electroencephalography/methods ; Electric Stimulation/methods ; Female ; Evoked Potentials, Somatosensory/physiology ; Adult ; },
abstract = {Brain-computer interface (BCI) is emerging as an effective complementary solution in the field of rehabilitation for the interaction between patients and robotic assistive devices. Specifically, the somatosensory event-related potentials (ERP) BCI has unique advantage for post-stroke motor rehabilitation scenarios and has been proven feasible on healthy subjects. We conducted the first patient-involved somatosensory ERP-BCI experiment with electric stimulation to evaluate its feasibility for real-world clinical usage. In the experiment, participant selectively attended to electric stimuli applied on either left or right wrist, which represented the operation of robot-assisted exercise of corresponding hand. An integrated platform that included exercise, stimulation, and electroencephalography (EEG) sampling modules was used. For evaluation, we used convolutional neural network (CNN) with transformer module to construct subject-specific intent decoder. The network demonstrated on average 58.95% accuracy in classifying target response from a single ERP trial. When using the classification from multiple consecutive trials, the decoder achieved a maximum of 80.12% mean accuracy in recognizing participants intent, and the highest rate from a single participant was 97.21%. The best information transfer rate (ITR) achieved was 1.956 Bit/min. These results demonstrated that the proposed BCI paradigm could be a valid choice for stroke rehabilitation. In the next stage, we anticipate the involvement of larger patient population, real-time feedback training, and the subsequent quantified motor function recovery results.},
}

Humans
*Stroke Rehabilitation/methods
*Brain-Computer Interfaces
Male
Electroencephalography/methods
Electric Stimulation/methods
Female
Evoked Potentials, Somatosensory/physiology
Adult

@article {pmid40039935,
year = {2024},
author = {Han, HT and Kim, SJ and Lee, DH and Lee, SW},
title = {Proxy-based Masking Module for Revealing Relevance of Characteristics in Motor Imagery.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782698},
pmid = {40039935},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; *Imagination/physiology ; Movement/physiology ; Signal Processing, Computer-Assisted ; Algorithms ; },
abstract = {Brain-computer interface (BCI) has been developed for communication between users and external devices by reflecting users' status and intentions. Motor imagery (MI) is one of the BCI paradigms for controlling external devices by imagining muscle movements. MI-based EEG signals generally tend to contain signals with sparse MI characteristics (sparse MI signals). When conducting domain adaptation (DA) on MI signals with sparse MI signals, it could interrupt the training process. In this paper, we proposed the proxy-based masking module (PMM) for masking sparse MI signals within MI signals. The proposed module was designed to suppress the amplitude of sparse MI signals using the negative similarity-based mask generated between the proxy of rest signals and the feature vectors of MI signals. We attached our proposed module to the conventional DA methods (i.e., the DJDAN, the MAAN, and the DRDA) to verify the effectiveness in the cross-subject environment on dataset 2a of BCI competition IV. When our proposed module was attached to each conventional DA method, the average accuracy was improved by much as 4.67 %, 0.76 %, and 1.72 %, respectively. Hence, we demonstrated that our proposed module could emphasize the information related to MI characteristics. The code of our implementation is accessible on GitHub.[1].},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography/methods
*Imagination/physiology
Movement/physiology
Signal Processing, Computer-Assisted
Algorithms

@article {pmid40039926,
year = {2024},
author = {Zhong, Y and Yao, L and Wang, Y},
title = {Enhanced BCI Performance using Diffusion Model for EEG Generation.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-5},
doi = {10.1109/EMBC53108.2024.10782900},
pmid = {40039926},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Electroencephalography/methods ; Algorithms ; Signal Processing, Computer-Assisted ; Imagination/physiology ; },
abstract = {In the realm of Motor Imagery (MI)-based Brain-Computer Interface (BCI), the widespread adoption of deep learning-based algorithms has resulted in an increased demand for a larger training sample size, thereby placing a heightened burden on users. This study advocates the utilization of one of the most advanced generative models, the denoising diffusion probabilistic model (DDPM), for the artificial synthesis of Electroencephalogram (EEG) raw signals. The quality of the generated EEG signals is evaluated through both qualitative and quantitative analyses. Through dimensionality reduction projection, we observed a notable similarity in the data distributions between the generated EEG signals and real EEG signals. Additionally, spectral analysis indicates a striking similarity in energy distribution between the two, accompanied by the presence of an event-related synchronization (ERS) phenomenon in the generated EEG signals. Quantitative analysis reveals that the accuracy of generated EEG signals for left and right-hand motor imagery tasks is 89.81 ± 2.11%, with discriminative information related to classes predominantly concentrated in the motor-sensory cortex area and alpha-beta frequency band. Furthermore, the integration of generated EEG samples contributes to a 3.17% improvement in the classification performance of BCI-deficiency subjects. These artificially generated EEG signals exhibit promising potential for application in calibrating MI-BCI deep learning models, thereby alleviating the burden on participants.},
}

*Brain-Computer Interfaces
Humans
*Electroencephalography/methods
Algorithms
Signal Processing, Computer-Assisted
Imagination/physiology

@article {pmid40039924,
year = {2024},
author = {Hoshino, T and Kanoga, S and Aoyama, A},
title = {Channel- and Label-Flip Data Augmentation for Motor Imagery-Based Brain-Computer Interfaces.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782028},
pmid = {40039924},
issn = {2694-0604},
mesh = {Humans ; *Brain-Computer Interfaces ; *Electroencephalography/methods ; Imagination/physiology ; Signal Processing, Computer-Assisted ; Machine Learning ; Movement/physiology ; Algorithms ; Brain/physiology ; },
abstract = {Achieving high classification accuracy in motor-imagery-based brain-computer interfaces (BCIs) requires substantial amounts of training data. A challenge arises because of the impracticality of measuring large amounts of data from users. Data augmentation (DA) has emerged as a promising solution for this challenge. We propose a novel DA method called channel&label-flip DA that involves not only flipping channels but also flipping class labels. This method is based on the neuroscience finding that motor imageries of left- and right-hand movements are roughly symmetrical. The efficiency of the proposed method was evaluated using the OpenBMI dataset, which comprises electroencephalograms collected from 54 participants engaged in left- and right-hand motor imagery tasks. To compare the impact on classifiers, we employed three classical machine learning models utilizing filter bank common spatial pattern features, along with a deep learning-based model that uses raw signal input. As a result, the channel&label-flip DA improved the classification accuracy on average, whereas simple flipping of the channels reduced the classification accuracy compared to the case without DA.},
}

Humans
*Brain-Computer Interfaces
*Electroencephalography/methods
Imagination/physiology
Signal Processing, Computer-Assisted
Machine Learning
Movement/physiology
Algorithms
Brain/physiology

@article {pmid40039912,
year = {2024},
author = {Tan, J and Wang, Y},
title = {Dynamic Inverse Reinforcement Learning for Feedback-driven Reward Estimation in Brain Machine Interface Tasks.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10782800},
pmid = {40039912},
issn = {2694-0604},
mesh = {*Brain-Computer Interfaces ; Humans ; *Reward ; *Algorithms ; Reinforcement, Psychology ; Machine Learning ; Feedback ; },
abstract = {Reinforcement learning (RL)-based brain machine interfaces (BMIs) provide a promising solution for paralyzed people. Enhancing the decoding performance of RL-based BMIs relies on the design of effective reward signals. Inverse reinforcement learning (IRL) offers an approach to infer subjects' own evaluation from the observed behavior. However, applying IRL to extract reward information in complex BMI tasks requires consideration of the dynamics of subjects' goal during the control process. This dynamic nature of subjects' evaluation requires the IRL method to be able to estimate a time varying reward function. Previous IRL methods applied in BMI systems only estimated a static reward function. Existing IRL algorithms for dynamic reward estimation employ optimization methods to approximate the reward map for each state at each time, which demands substantial amounts of data to achieve convergence. In this paper, we propose a dynamic IRL method to estimate the feedback-driven reward of subjects during BMI tasks. We utilize a state-observation model to continuously infer the reward value for each state, with sensory feedback serving as the external input to model the transition process of the reward. We evaluate our proposed method on a simulated BMI fetch task, which is a multistep task with a time varying reward function. Our method demonstrates improved reward estimation close to the ground truth value, and it significantly outperforms the existing dynamic IRL method when the map size exceeds 25(p<0.01). These preliminary results suggests that the dynamic IRL method for feedback-driven reward estimation holds potential for improving the design of RL-based BMIs.},
}

*Brain-Computer Interfaces
Humans
*Reward
*Algorithms
Reinforcement, Psychology
Machine Learning
Feedback

@article {pmid40039908,
year = {2024},
author = {Patel, K and Safavi, F and Chandramouli, R and Vinjamuri, R},
title = {Transformer-Based Emotion Recognition with EEG.},
journal = {Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference},
volume = {2024},
number = {},
pages = {1-4},
doi = {10.1109/EMBC53108.2024.10781700},
pmid = {40039908},
issn = {2694-0604},
mesh = {Humans ; *Electroencephalography/methods ; *Emotions/physiology ; *Signal Processing, Computer-Assisted ; Brain-Computer Interfaces ; Male ; Female ; Brain/physiology ; },
abstract = {Emotion recognition via electroencephalography (EEG) has emerged as a pivotal domain in biomedical signal processing, offering valuable insights into affective states. This paper presents a novel approach utilizing a tailored Transformer-based model to predict valence and arousal levels from EEG signals. Diverging from traditional Transformers handling singular sequential data, our model adeptly accommodates multiple EEG channels concurrently, enhancing its ability to discern intricate temporal patterns across the brain. The modified Transformer architecture enables comprehensive exploration of spatiotemporal dynamics linked with emotional states. Demonstrating robust performance, the model achieves mean accuracies of 92.66% for valence and 91.17% for arousal prediction, validated through 10-fold cross-validation across subjects on the DEAP dataset. Trained for subject-specific analysis, our methodology offers promising avenues for enhancing understanding and applications in emotion recognition through EEG. This research contributes to a broader discourse in biomedical signal processing, paving the way for refined methodologies in decoding neural correlates of emotions with implications across various domains including brain-computer interfaces, and human-robot interaction.},
}

Humans
*Electroencephalography/methods
*Emotions/physiology
*Signal Processing, Computer-Assisted
Brain-Computer Interfaces
Male
Female
Brain/physiology