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EEG Foundation Models for Brain-Computer Interfaces:Progress and Future Directions 认领 引用
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作者 DAI Renjie DONG Shenhua +1 位作者 LU Baoliang ZHENG Weilong 《Journal of Shanghai Jiaotong university(Science)》 EI 2026年第3期783-799,I0047,共17页
Electroencephalography(EEG)foundation models are increasingly used as general-purpose backbones for brain-computer interfaces(BCIs)by leveraging large-scale pretraining and task-specific adaptation.This review summari... Electroencephalography(EEG)foundation models are increasingly used as general-purpose backbones for brain-computer interfaces(BCIs)by leveraging large-scale pretraining and task-specific adaptation.This review summarizes recent progress in EEG foundation models from three perspectives:datasets and task coverage,with emphasis on how generalization goals are operationalized by split protocols and concrete evaluation procedures;model design choices,including input construction and tokenization,masked pretraining objectives,and Transformer backbones for spatiotemporal modeling across heterogeneous channel layouts;and downstream adaptation,comparing linear probing,full fine-tuning,and parameter-efficient tuning,while clarifying the conditions under which each setting is most informative.We emphasize that reported gains are often protocol-dependent,as differences in task scope,preprocessing,training budget,and baseline selection can substantially affect comparability and the extent to which conclusions generalize.Finally,we outline future directions for EEG foundation models in BCI,focusing on standardized evaluation infrastructure,EEG-tailored modeling choices,and deployment-aware adaptation under real-world constraints. 展开更多
关键词 electroencephalography(EEG) foundation model brain-computer interface deep learning
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A Personalized Brain-computer Interface Paradigm and Decoding Method for The Objective Evaluation of Auditory Frequency Difference Limen 认领 引用
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作者 LI Sheng-Ye XIAO Xiao-Lin +4 位作者 YU Shi-Hang ZHANG Bei-Bei AN Xing-Wei XU Min-Peng MING Dong 《生物化学与生物物理进展》 SCIE CAS CSCD 北大核心 2026年第7期1927-1941,共15页
Objective The frequency difference limen(FDL)serves as a fundamental metric utilized for effectively quantifying the precise perceptual capabilities of the central auditory system.However,traditional measurement metho... Objective The frequency difference limen(FDL)serves as a fundamental metric utilized for effectively quantifying the precise perceptual capabilities of the central auditory system.However,traditional measurement methods rely heavily on the active behavioral responses of subjects and are consequently highly susceptible to the negative influence of confounding subjective factors.Furthermore,existing research paradigms frequently employ uniform stimulus configurations that overlook critical individual perceptual differences.Based on brain-computer interface(BCI)technology,this comprehensive study aims to establish an objective and quantitative evaluation method for auditory frequency discrimination by systematically analyzing and decoding the specific neural responses elicited at the exact threshold state.Methods We designed a personalized rapid serial auditory presentation(RSAP)paradigm customized based on each individual’s precise FDL.A cohort of eleven healthy participants was recruited to evaluate the paradigm using pure-tone sequences at a baseline frequency of 4000 Hz.This experimental paradigm simulates a realistic auditory perception environment through the continuous presentation of acoustic stimuli,thereby allowing for an in-depth investigation into the specific neural representations evoked by weak frequency deviations at the threshold state.Given that auditory stimulus-evoked response features exhibit complex and differentiated spatiotemporal distribution patterns across multiple frequency domains,this study further deeply integrates the cross-scale feature interaction module with the dynamic spatiotemporal attention allocation strategy,innovatively proposing the Multi-Scale Spatial-Temporal Dual Attention Network(MS-STAMNet).Specifically,the network constructs parallel processing branches with multiple receptive fields and introduces a dynamic adaptive weighting strategy to precisely localize core neural activity signals,further deeply integrating multi-scale information through crossbranch feature information interaction to achieve robust single-trial decoding of weak auditory evoked responses.Results The comprehensive electrophysiological data analysis demonstrated that subtle auditory frequency deviation stimuli presented at the threshold level successfully elicited pronounced N2 and P3 event-related potential features,reflecting pre-attentive mismatch detection and subsequent cognitive evaluation,which were prominently distributed over the frontal,central,and temporal regions of the scalp.In the complex time-frequency domain,the extracted neural response characteristics exhibited distinct,statistically significant event-related synchronization within both the low-frequencyδandθfrequency bands,which was simultaneously accompanied by a widespread,prominent event-related desynchronization within the higherαband.A comparative analysis of model performance demonstrated that MS-STAMNet achieved an average unweighted average recall(UAR)of(69.67±6.12)%and area under the curve(AUC)of 0.7618±0.07,significantly outperforming the established baseline models such as EEGNet and PLNet.Furthermore,a distinct dissociation phenomenon was verified between neural decoding and behavioral performance through regression analysis(R2=0.016,P=0.709),indicating that this model can effectively capture the implicit features of subtle frequency deviations,even when they fail to trigger explicit conscious responses.Additionally,attention weight visualization analysis further reveals the highly accurate focus of the network on key features concentrated over the bilateral temporal and fronto-parietal regions.Conclusion This study systematically and comprehensively uncovers the multi-dimensional spatiotemporal evolutionary patterns of complex neural responses processing subtle acoustic variations under long-sequence threshold auditory stimulation.Concurrently,it verifies the efficacy and robustness of the proposed MS-STAMNet architecture in accurately deciphering weak,single-trial electroencephalogram signals amidst complex background noise.Ultimately,these neurophysiological and algorithmic findings lay a solid theoretical and methodological foundation for the objective and quantitative evaluation of individual auditory cognitive capabilities in clinical applications,transcending the fundamental limitations of traditional behavioral paradigms and providing robust technical support for future auditory research and related clinical assessments. 展开更多
关键词 frequency difference discrimination brain-computer interface rapid serial auditory presentation personalization deep learning
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AI Large Model-Driven Adaptive Evolution of Brain-Computer Interface Chips:Technical Architecture,Challenges,and Future Directions 认领 引用
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作者 Borui Cui 《Journal of Electronic Research and Application》 2026年第1期209-220,共12页
This paper focuses on how AI large models,such as Transformers and meta-learning can empower brain-computer interface(BCI)chips to achieve dynamic adaptation,thereby overcoming the limitations of traditional fixed dec... This paper focuses on how AI large models,such as Transformers and meta-learning can empower brain-computer interface(BCI)chips to achieve dynamic adaptation,thereby overcoming the limitations of traditional fixed decoding models that struggle to adapt to individual neural plasticity and dynamic changes in brain states.It analyzes pathways to enhance chip generalization and real-time performance across three technical dimensions:hardware architecture,algorithm optimization,and multimodal fusion.The paper also explores core challenges like data privacy and energy-efficiency tradeoffs.Building on this foundation,it proposes a neuromorphic computing design framework for next-generation chips to advance the intelligent and personalized development of BCI in medical rehabilitation and human-computer interaction. 展开更多
关键词 Brain-computer interface Artificial intelligence Chip Large model-driven Meta-learning
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Recent applications of EEG-based brain-computer-interface in the medical field 认领 引用 被引量：28
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作者 Xiu-Yun Liu Wen-Long Wang +39 位作者 Miao Liu Ming-Yi Chen Tânia Pereira Desta Yakob Doda Yu-Feng Ke Shou-Yan Wang Dong Wen Xiao-Guang Tong Wei-Guang Li Yi Yang Xiao-Di Han Yu-Lin Sun Xin Song Cong-Ying Hao Zi-Hua Zhang Xin-Yang Liu Chun-Yang Li Rui Peng Xiao-Xin Song Abi Yasi Mei-Jun Pang Kuo Zhang Run-Nan He Le Wu Shu-Geng Chen Wen-Jin Chen Yan-Gong Chao Cheng-Gong Hu Heng Zhang Min Zhou Kun Wang Peng-Fei Liu Chen Chen Xin-Yi Geng Yun Qin Dong-Rui Gao En-Ming Song Long-Long Cheng Xun Chen Dong Ming 《Military Medical Research》 SCIE CAS CSCD 2025年第8期1283-1322,共40页
Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BC... Brain-computer interfaces(BCIs)represent an emerging technology that facilitates direct communication between the brain and external devices.In recent years,numerous review articles have explored various aspects of BCIs,including their fundamental principles,technical advancements,and applications in specific domains.However,these reviews often focus on signal processing,hardware development,or limited applications such as motor rehabilitation or communication.This paper aims to offer a comprehensive review of recent electroencephalogram(EEG)-based BCI applications in the medical field across 8 critical areas,encompassing rehabilitation,daily communication,epilepsy,cerebral resuscitation,sleep,neurodegenerative diseases,anesthesiology,and emotion recognition.Moreover,the current challenges and future trends of BCIs were also discussed,including personal privacy and ethical concerns,network security vulnerabilities,safety issues,and biocompatibility. 展开更多
关键词 Brain-computer interfaces(BCIs) Medical applications Rehabilitation Communication Brain monitoring Diagnosis
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Informed consent competency assessment for brain-computer interface clinical research and application in psychiatric disorders:A systematic review 认领 引用 被引量：1
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作者 Jia-Yue Si Zi-Yan Lin +8 位作者 Di-Ga Gan Xin-Yang Zhang Yan-Nan Liu Yu-Xin Hu Yan-Ping Bao Xue-Qin Wang Hong-Qiang Sun Xin Yu Lin Lu 《World Journal of Psychiatry》 SCIE 2025年第8期404-423,共20页
BACKGROUND Brain-computer interface(BCI)technology is rapidly advancing in psychiatry.Informed consent competency(ICC)assessment among psychiatric patients is a pivotal concern in clinical research.AIM To analyze the ... BACKGROUND Brain-computer interface(BCI)technology is rapidly advancing in psychiatry.Informed consent competency(ICC)assessment among psychiatric patients is a pivotal concern in clinical research.AIM To analyze the assessment of ICC and form a framework with multi-dimensional elements involved in ICC of BCI clinical research among psychiatric disorders.METHODS A systematic review of studies regarding ICC assessments of BCI clinical research in patients with six kinds of psychiatric disorders was conducted.A systematic literature search was performed using PubMed,ScienceDirect,and Web of Science.Peer-reviewed articles and full-text studies were included in the analysis.There were no date restrictions,and all studies published up to February 27,2025,were included.RESULTS A total of 103 studies were selected for this review.Fifty-eight studies included ICC factors,and forty-five were classified in ICC related ethical issues of BCI research in six kinds of psychiatric disorders.Executive function impairment is widely recognized as the most significant factor impacting ICC,and processing speed deficits are observed in schizophrenia,mood disorders,and Alzheimer’s disease.Memory dysfunction,particularly episodic and working memory,contributes to compromised ICC.Five core ethical issues in BCI research should be addressed:BCI specificity,vulnerability,autonomy,dynamic ICC,comprehensiveness,and uncertainty.CONCLUSION A Five-Dimensional evaluative framework,including clinical,ethical,sociocultural,legal,and procedural dimensions,is constructed and proposed for future ICC research in BCI clinical research involving psychiatric disorders. 展开更多
关键词 Informed consent competency Brain-computer interface Psychiatric disorders Decision-making capacity MacArthur Competence Assessment Tool
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Frequency optimization for electrodes in implantable brain-computer interfaces 认领 引用 被引量：1
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作者 CHEN Han LIU Xiangyu +2 位作者 CHENG Jiajun QIN Jiangfan ZHANG Xueli 《Journal of Southeast University(English Edition)》 EI CAS 2025年第3期366-374,共9页
Fully implanted brain-computer interfaces(BCIs)are preferred as they eliminate signal degradation caused by interference and absorption in external tissues,a common issue in non-fully implanted systems.To optimize the... Fully implanted brain-computer interfaces(BCIs)are preferred as they eliminate signal degradation caused by interference and absorption in external tissues,a common issue in non-fully implanted systems.To optimize the design of electroencephalography electrodes in fully implanted BCI systems,this study investigates the penetration and absorption characteristics of microwave signals in human brain tissue at different frequencies.Electromagnetic simulations are used to analyze the power density distribution and specific absorption rate(SAR)of signals at various frequen-cies.The results indicate that lower-frequency signals offer advantages in terms of power density and attenuation coeffi-cients.However,SAR-normalized analysis,which considers both power density and electromagnetic radiation hazards,shows that higher-frequency signals perform better at superficial to intermediate depths.Specifically,at a depth of 2 mm beneath the cortex,the power density of a 6.5 GHz signal is 247.83%higher than that of a 0.4 GHz signal.At a depth of 5 mm,the power density of a 3.5 GHz signal exceeds that of a 0.4 GHz signal by 224.16%.The findings suggest that 6.5 GHz is optimal for electrodes at a depth of 2 mm,3.5 GHz for 5 mm,2.45 GHz for depths of 15-20 mm,and 1.8 GHz for 25 mm. 展开更多
关键词 brain-computer interfaces electromagnetic simulation electroencephalography electrodes power den-sity specific absorption rate
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A non-invasive algorithm for removing artifacts of eye movement,environment and contact sweat in frontal brain-computer interfaces based on BIAS multi-bias modeling 认领 引用
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作者 Quanhong Che Chongliang Zhong +2 位作者 Na Li Haozhang Tan Zhong Wang 《Science International Innovative Medicine》 2025年第6期54-62,共9页
Aiming at low SNR and feature distortion of valid neural signals caused by eye movement,electromagnetic interference and channel noise in frontal single/multi-channel BCI EEG tests,a BIAS-core anchor artifact removal ... Aiming at low SNR and feature distortion of valid neural signals caused by eye movement,electromagnetic interference and channel noise in frontal single/multi-channel BCI EEG tests,a BIAS-core anchor artifact removal algorithm is proposed for low-channel,low-computing wearable scenarios.It defines EOG-BIAS,ENV-BIAS,CH-BIAS,builds a"pre-calibration-real-time processing-posterior compensation"lightweight architecture,and decouples artifacts via"bias modeling-separation-compensation".With unsupervised learning and individual adaptability,it fits unshielded real-time environments.Tests show its EOG/ENV-BIAS removal rates≥95%/≥99%,neural signal retention≥90%,single-frame delay<10ms,SNR improvement≥15dB(stable≥10dB),effectively ensuring frontal BCI signal quality and supporting low-resource frontal BCI signal preprocessing. 展开更多
关键词 Non-invasive brain-computer interface Frontal EEG Artifact removal BIAS algorithm
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Application of Brain-Computer Interface in the Treatment of Mental Illness 认领 引用
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作者 Yuda Song Caiyi Yang Peichen Yu 《Journal of Clinical and Nursing Research》 2025年第9期218-224,共7页
Traditional psychological treatment methods often require a long time and have limited effects.Researchers have begun to explore the combination of brain-computer interface(BCI)technology and mental health,providing n... Traditional psychological treatment methods often require a long time and have limited effects.Researchers have begun to explore the combination of brain-computer interface(BCI)technology and mental health,providing new possibilities for the treatment and rehabilitation of mental illnesses.This paper reviews the advantages,existing risks,and challenges of BCI technology in mental health treatment,and prospects the future development of research on BCI and mental health. 展开更多
关键词 Brain-computer interface Mental health Psychotherapy
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Visual Fixation Assessment in Patients with Disorders of Consciousness Based on Brain-Computer Interface 认领 引用 被引量：15
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作者 Jun Xiao Jiahui Pan +7 位作者 Yanbin He Qiuyou Xie Tianyou Yu Haiyun Huang Wei Lv Jiechun Zhang Ronghao Yu Yuanqing Li 《Neuroscience Bulletin》 SCIE CAS CSCD 2018年第4期679-690,共12页
Visual fixation is an item in the visual function subscale of the Coma Recovery Scale-Revised （CRS-R）. Sometimes clinicians using the behavioral scales find it difficult to detect because of the motor impairment in ... Visual fixation is an item in the visual function subscale of the Coma Recovery Scale-Revised （CRS-R）. Sometimes clinicians using the behavioral scales find it difficult to detect because of the motor impairment in patients with disorders of consciousness （DOCs）. Brain- computer interface （BCI） can be used to improve clinical assessment because it directly detects the brain response to an external stimulus in the absence of behavioral expres- sion. In this study, we designed a BCI system to assist the visual fixation assessment of DOC patients. The results from 15 patients indicated that three showed visual fixation in both CRS-R and BCI assessments and one did not show such behavior in the CRS-R assessment but achieved significant online accuracy in the BCI assessment. The results revealed that electroencephalography-based BCI can detect the brain response for visual fixation. Therefore, the proposed BCI may provide a promising method for assisting behavioral assessment using the CRS-R. 展开更多
关键词 Visual fixation Brain-computer interface Disorder of consciousness Coma recovery scale-revised Electroencephalography
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Remote Navigation of Turtle by Controlling Instinct Behavior via Human Brain-computer Interface 认领 引用 被引量：9
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作者 Cheol-Hu Kim Bongjae Choi +3 位作者 Dae-Gun Kim Serin Lee Sungho Jo Phill-Seung Lee 《Journal of Bionic Engineering》 SCIE EI CSCD 2016年第3期491-503,共13页
Brain-Computer Interface （BCI） techniques have advanced to a level where it is now eliminating the need lor hand-based activation. This paper presents a novel attempt to remotely control an animal＇s behavior by hum... Brain-Computer Interface （BCI） techniques have advanced to a level where it is now eliminating the need lor hand-based activation. This paper presents a novel attempt to remotely control an animal＇s behavior by human BCI using a hybrid of Event Related Desynchronization （ERD） and Steady-State Visually Evoked Potential （SSVEP） BCI protocols. The turtle was chosen as the target animal, and we developed a head-mounted display, wireless communication, and a specially designed stimulation device for the turtle. These devices could evoke the turtle＇s instinctive escape behavior to guide its moving path, and turtles were remotely controlled in both indoor and outdoor environments. The system architecture and design were presented. To demon- strate the feasibility of the system, experimental tests were performed under various conditions. Our system could act as a framework for future human-animal interaction systems. 展开更多
关键词 brain-computer interface turtle （Trachemys scripta elegans） remote navigation instinct behaviour escape be- havior
A Hybrid Brain-Computer Interface for Closed-Loop Position Control of a Robot Arm 认领 引用 被引量：11
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作者 Arnab Rakshit Amit Konar Atulya K.Nagar 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2020年第5期1344-1360,共17页
Brain-Computer interfacing(BCI)has currently added a new dimension in assistive robotics.Existing braincomputer interfaces designed for position control applications suffer from two fundamental limitations.First,most ... Brain-Computer interfacing(BCI)has currently added a new dimension in assistive robotics.Existing braincomputer interfaces designed for position control applications suffer from two fundamental limitations.First,most of the existing schemes employ open-loop control,and thus are unable to track positional errors,resulting in failures in taking necessary online corrective actions.There are examples of a few works dealing with closed-loop electroencephalography(EEG)-based position control.These existing closed-loop brain-induced position control schemes employ a fixed order link selection rule,which often creates a bottleneck preventing time-efficient control.Second,the existing brain-induced position controllers are designed to generate a position response like a traditional firstorder system,resulting in a large steady-state error.This paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential(SSVEP)induced linkselection in an arbitrary order as required for efficient control and generating a second-order response of the position-control system with gradually diminishing overshoots/undershoots to reduce steady-state errors.Other than the above,the third innovation is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin using speed reversal at the zero-crossings of positional errors.Experiments undertaken reveal that the steady-state error is reduced to 0.2%.The paper also provides a thorough analysis of the stability of the closed-loop system performance using the Root Locus technique. 展开更多
关键词 Brain-computer interfacing(BCI) electroencepha-lography(EEG) Jaco robot arm motor imagery P300 steady-state visually evoked potential(SSVEP)
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Transfer Learning Algorithm Design for Feature Transfer Problem in Motor Imagery Brain-computer Interface 认领 引用 被引量：3
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作者 Yu Zhang Huaqing Li +3 位作者 Heng Dong Zheng Dai Xing Chen Zhuoming Li 《China Communications》 SCIE CSCD 2022年第2期39-46,共8页
The non-stationary of the motor imagery electroencephalography(MI-EEG)signal is one of the main limitations for the development of motor imagery brain-computer interfaces(MI-BCI).The nonstationary of the MI-EEG signal... The non-stationary of the motor imagery electroencephalography(MI-EEG)signal is one of the main limitations for the development of motor imagery brain-computer interfaces(MI-BCI).The nonstationary of the MI-EEG signal and the changes of the experimental environment make the feature distribution of the testing set and training set deviates,which reduces the classification accuracy of MI-BCI.In this paper,we propose a Kullback–Leibler divergence(KL)-based transfer learning algorithm to solve the problem of feature transfer,the proposed algorithm uses KL to measure the similarity between the training set and the testing set,adds support vector machine(SVM)classification probability to classify and weight the covariance,and discards the poorly performing samples.The results show that the proposed algorithm can significantly improve the classification accuracy of the testing set compared with the traditional algorithms,especially for subjects with medium classification accuracy.Moreover,the algorithm based on transfer learning has the potential to improve the consistency of feature distribution that the traditional algorithms do not have,which is significant for the application of MI-BCI. 展开更多
关键词 brain-computer interface motor imagery feature transfer transfer learning domain adaptation
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Electric Wheelchair Control System Using Brain-Computer Interface Based on Alpha-Wave Blocking 认领 引用 被引量：3
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作者 明东 付兰 +8 位作者 陈龙 汤佳贝 綦宏志 赵欣 周鹏 张力新 焦学军 王春慧 万柏坤 《Transactions of Tianjin University》 EI CAS 2014年第5期358-363,共6页
A brain-computer interface(BCI)-based electric wheelchair control system was developed, which enables the users to move the wheelchair forward or backward, and turn left or right without any pre-learning. This control... A brain-computer interface(BCI)-based electric wheelchair control system was developed, which enables the users to move the wheelchair forward or backward, and turn left or right without any pre-learning. This control system makes use of the amplitude enhancement of alpha-wave blocking in electroencephalogram(EEG) when eyes close for more than 1 s to constitute a BCI for the switch control of wheelchair movements. The system was formed by BCI control panel, data acquisition, signal processing unit and interface control circuit. Eight volunteers participated in the wheelchair control experiments according to the preset routes. The experimental results show that the mean success control rate of all the subjects was 81.3%, with the highest reaching 93.7%. When one subject's triggering time was 2.8 s, i.e., the flashing time of each cycle light was 2.8 s, the average information transfer rate was 8.10 bit/min, with the highest reaching 12.54 bit/min. 展开更多
关键词 electric wheelchair alpha-wave blocking brain-computer interface （BCI） success control rate
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Non-invasive EEG-based brain-computer interfaces in patients with disorders of consciousness 认领 引用 被引量：5
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作者 Emilia Mikoajewska Dariusz Mikoajewski 《Journal of Medical Colleges of PLA(China)》 2014年第2期109-114,共6页
Disorders of consciousness(DoCs) are chronic conditions resulting usually from severe neurological deficits. The limitations of the existing diagnosis systems and methodologies cause a need for additional tools for re... Disorders of consciousness(DoCs) are chronic conditions resulting usually from severe neurological deficits. The limitations of the existing diagnosis systems and methodologies cause a need for additional tools for relevant patients with DoCs assessment, including brain-computer interfaces(BCIs). Recent progress in BCIs' clinical applications may offer important breakthroughs in the diagnosis and therapy of patients with DoCs. Thus the clinical significance of BCI applications in the diagnosis of patients with DoCs is hard to overestimate. One of them may be brain-computer interfaces. The aim of this study is to evaluate possibility of non-invasive EEG-based brain-computer interfaces in diagnosis of patients with DOCs in post-acute and long-term care institutions. 展开更多
关键词 neurological disorders disorders of consciousness brain-computer interfaces EEG-based BCIs
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Individualization of Data-Segment-Related Parameters for Improvement of EEG Signal Classification in Brain-Computer Interface 认领 引用 被引量：1
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作者 曹红宝 BESIO Walter G +1 位作者 JONES Steven 周鹏 《Transactions of Tianjin University》 EI CAS 2010年第3期235-238,共4页
In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in... In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI. 展开更多
关键词 data segment parameter selection EEG classification brain-computer interface （BCI）
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Neurological rehabilitation of stroke patients via motor imaginary-based brain-computer interface technology 认领 引用
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作者 Hongyu Sun Yang Xiang Mingdao Yang 《Neural Regeneration Research》 SCIE CAS CSCD 2011年第28期2198-2202,共5页
The present study utilized motor imaginary-based brain-computer interface technology combined with rehabilitation training in 20 stroke patients. Results from the Berg Balance Scale and the Holden Walking Classificati... The present study utilized motor imaginary-based brain-computer interface technology combined with rehabilitation training in 20 stroke patients. Results from the Berg Balance Scale and the Holden Walking Classification were significantly greater at 4 weeks after treatment （P 〈 0.01）, which suggested that motor imaginary-based brain-computer interface technology improved balance and walking in stroke patients. 展开更多
关键词 brain-computer interface motor cortex neuronal plasticity rehabilitation stroke neural regeneration
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Brain-computer Interaction in the Smart Era 认领 引用
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作者 Zi-neng YAN Peng-ran LIU +9 位作者 Hong ZHOU Jia-yao ZHANG Song-xiang LIU Yi XIE Hong-lin WANG Jin-bo YU Yu ZHOU Chang-mao NI Li HUANG Zhe-wei YE 《Current Medical Science》 SCIE CAS 2024年第6期1123-1131,共9页
The brain-computer interface(BCI)system serves as a critical link between external output devices and the human brain.A monitored object’s mental state,sensory cognition,and even higher cognition are reflected in its... The brain-computer interface(BCI)system serves as a critical link between external output devices and the human brain.A monitored object’s mental state,sensory cognition,and even higher cognition are reflected in its electroencephalography(EEG)signal.Nevertheless,unprocessed EEG signals are frequently contaminated with a variety of artifacts,rendering the analysis and elimination of impurities from the collected EEG data exceedingly challenging,not to mention the manual adjustment thereof.Over the last few decades,the rapid advancement of artificial intelligence(AI)technology has contributed to the development of BCI technology.Algorithms derived from AI and machine learning have significantly enhanced the ability to analyze and process EEG electrical signals,thereby expanding the range of potential interactions between the human brain and computers.As a result,the present BCI technology with the help of AI can assist physicians in gaining a more comprehensive understanding of their patients’physical and psychological status,thereby contributing to improvements in their health and quality of life. 展开更多
关键词 artificial intelligence brain-computer interaction
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Challenges and Suggestions of Ethical Review on Clinical Research Involving Brain-Computer Interfaces 认领 引用 被引量：3
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作者 Xue-Qin Wang Hong-Qiang Sun +3 位作者 Jia-Yue Si Zi-Yan Lin Xiao-Mei Zhai Lin Lu 《Chinese Medical Sciences Journal》 CAS CSCD 2024年第2期131-139,共9页
Brain-computer interface(BCI)technology is rapidly advancing in medical research and application.As an emerging biomedical engineering technology,it has garnered significant attention in the clinical research of brain... Brain-computer interface(BCI)technology is rapidly advancing in medical research and application.As an emerging biomedical engineering technology,it has garnered significant attention in the clinical research of brain disease diagnosis and treatment,neurological rehabilitation,and mental health.However,BCI also raises several challenges and ethical concerns in clinical research.In this article,the authors investigate and discuss three aspects of BCI in medicine and healthcare:the state of international ethical governance,multidimensional ethical challenges pertaining to BCI in clinical research,and suggestive concerns for ethical review.Despite the great potential of frontier BCI research and development in the field of medical care,the ethical challenges induced by itself and the complexities of clinical research and brain function have put forward new special fields for ethics in BCI.To ensure"responsible innovation"in BCI research in healthcare and medicine,the creation of an ethical global governance framework and system,along with special guidelines for cutting-edge BCI research in medicine,is suggested. 展开更多
关键词 brain-computer interface clinical research bioethics ethical governance ethical review
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Application of Brain-Computer-Interface in Awareness Detection Using Machine Learning Methods 认领 引用 被引量：2
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作者 Kaiqiang Feng Weilong Lin +6 位作者 Feng Wu Chengxin Pang Liang Song Yijia Wu Rong Cao Huiliang Shang Xinhua Zeng 《China Communications》 SCIE CSCD 2022年第6期279-291,共13页
The awareness detection in patients with disorders of consciousness currently relies on behavioral observations and CRS-R tests,however,the misdiagnosis rates have been relatively high.In this study,we applied brain-c... The awareness detection in patients with disorders of consciousness currently relies on behavioral observations and CRS-R tests,however,the misdiagnosis rates have been relatively high.In this study,we applied brain-computer interface(BCI)to awareness detection with a passive auditory stimulation paradigm.12 subjects with normal hearing were invited to collect electroencephalogram(EEG)based on a BCI communication system,in which EEG signals are transmitted wirelessly.After necessary preprocessing,RBF-SVM and EEGNet were used for algorithm realization and analysis.For a single subject,RBF-SVM can distinguish his(her)name stimuli awareness with classification accuracies ranging from 60-95%.EEGNet was used to learn all subjects'data and improved accuracy to 78.04%for characteristics finding and model generalization.Moreover,we completed the supplementary analysis work from the time domain and time-frequency domain.This study applied BCI communication to human awareness detection,proposed a passive auditory paradigm,and proved the effectiveness,which could be an inspiration for brain,mental or physical diseases diagnosis and detection. 展开更多
关键词 brain-computer interface EEG awareness detection machine learning deep learning
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Double Deep Q-Network Decoder Based on EEG Brain-Computer Interface 认领 引用 被引量：1
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作者 REN Min XU Renyu ZHU Ting 《ZTE Communications》 2023年第3期3-10,共8页
Brain-computer interfaces(BCI)use neural activity as a control signal to enable direct communication between the human brain and external devices.The electrical signals generated by the brain are captured through elec... Brain-computer interfaces(BCI)use neural activity as a control signal to enable direct communication between the human brain and external devices.The electrical signals generated by the brain are captured through electroencephalogram(EEG)and translated into neural intentions reflecting the user’s behavior.Correct decoding of the neural intentions then facilitates the control of external devices.Reinforcement learning-based BCIs enhance decoders to complete tasks based only on feedback signals(rewards)from the environment,building a general framework for dynamic mapping from neural intentions to actions that adapt to changing environments.However,using traditional reinforcement learning methods can have challenges such as the curse of dimensionality and poor generalization.Therefore,in this paper,we use deep reinforcement learning to construct decoders for the correct decoding of EEG signals,demonstrate its feasibility through experiments,and demonstrate its stronger generalization on motion imaging(MI)EEG data signals with high dynamic characteristics. 展开更多
关键词 brain-computer interface(BCI) electroencephalogram(EEG) deep reinforcement learning(Deep RL) motion imaging(MI)generalizability
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