Invasive as well as non-invasive neurotechnologies conceptualized to interface the central and peripheral nervous system have been probed for the past decades,which refer to electroencephalography,electrocorticography...Invasive as well as non-invasive neurotechnologies conceptualized to interface the central and peripheral nervous system have been probed for the past decades,which refer to electroencephalography,electrocorticography and microelectrode arrays.The challenges of these mentioned approaches are characterized by the bandwidth of the spatiotemporal resolution,which in turn is essential for large-area neuron recordings(Abiri et al.,2019).展开更多
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
In recent years,with the continuous advancement of technolo-gies such as artificial intelligence,neurobiology,and sensors,braincomputer interface(Bcl)technology has embraced opportunitiesfor rapid development The"...In recent years,with the continuous advancement of technolo-gies such as artificial intelligence,neurobiology,and sensors,braincomputer interface(Bcl)technology has embraced opportunitiesfor rapid development The"Guidelines for the Establishment ofNeurological Medical Service Price ltems(Trial)"recently issued bythe National Healthcare Security Administration specifically sets upseparate prospective items for new BCl technologies,which will un-doubtedly strongly facilitate the clinical application of BCl technologyas soon as possible,benefiting a broad range of patients.展开更多
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
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
The development of brain-computer interfaces(BCIs)has established a new communication channel between the brain and external devices for information transmission that requires no muscular signals[1].BCIs have been pre...The development of brain-computer interfaces(BCIs)has established a new communication channel between the brain and external devices for information transmission that requires no muscular signals[1].BCIs have been preliminarily studied to improve motor functions in patients with severe motor disabilities,especially lock-in syndrome.At present,the application of BCIs has been extensively validated.展开更多
An electroencephalogram(EEG)signal projection using kernel discriminative locality preserving canonical correlation analysis(KDLPCCA)-based correlation with steady-state visual evoked potential(SSVEP)templates for fre...An electroencephalogram(EEG)signal projection using kernel discriminative locality preserving canonical correlation analysis(KDLPCCA)-based correlation with steady-state visual evoked potential(SSVEP)templates for frequency recognition is presented in this paper.With KDLPCCA,not only a non-linear correlation but also local properties and discriminative information of each class sample are considered to extract temporal and frequency features of SSVEP signals.The new projected EEG features are classified with classical machine learning algorithms,namely,K-nearest neighbors(KNNs),naive Bayes,and random forest classifiers.To demonstrate the effectiveness of the proposed method,16-channel SSVEP data corresponding to 4 frequencies collected from 5 subjects were used to evaluate the performance.Compared with the state of the art canonical correlation analysis(CCA),experimental results show significant improvements in classification accuracy and information transfer rate(ITR),achieving 100%and 240 bits/min with 0.5 s sample block.The superior performance demonstrates that this method holds the promising potential to achieve satisfactory performance for high-accuracy SSVEP-based brain-computer interfaces.展开更多
The Brain-Computer Interfaces(BCIs)had been proposed and used in therapeutics for decades.However,the need of time-consuming calibration phase and the lack of robustness,which are caused by little-labeled data,are res...The Brain-Computer Interfaces(BCIs)had been proposed and used in therapeutics for decades.However,the need of time-consuming calibration phase and the lack of robustness,which are caused by little-labeled data,are restricting the advance and application of BCI,especially for the BCI based on motor imagery(MI).In this paper,we reviewed the recent development in the machine learning algorithm used in the MI-based BCI,which may provide potential solutions for addressing the issue.We classified these algorithms into two categories,namely,and enhancing the representation and expanding the training set.Specifically,these methods of enhancing the representation of features collected from few EEG trials are based on extracting features of multiple bands,regularization,and so on.The methods of expanding the training dataset include approaches of transfer learning(session to session transfer,subject to subject transfer)and generating artificial EEG data.The result of these techniques showed the resolution of the challenges to some extent.As a developing research area,the study of BCI algorithms in little-labeled data is increasingly requiring the advancement of human brain physiological structure research and more transfer learning algorithms research.展开更多
Flexible electronic devices with compliant mechanical deformability and electrical reliability have been a focal point of research over the past decade,particularly in the fields of wearable devices,brain-computer int...Flexible electronic devices with compliant mechanical deformability and electrical reliability have been a focal point of research over the past decade,particularly in the fields of wearable devices,brain-computer interfaces(BCIs),and electronic skins.These emerging applications impose stringent requirements on flexible sensors,necessitating not only their ability to withstand dynamic strains and conform to irregular surfaces but also to ensure long-term stable monitoring.To meet these demands,onedimensional nanowires,with high aspect ratios,large surface-to-volume ratios,and programmable geometric engineering,are widely regarded as ideal candidates for constructing high-performance flexible sensors.Various innovative assembly techniques have enabled the effective integration of these nanowires with flexible substrates.More excitingly,semiconductor nanowires,prepared through low-cost and efficient catalytic growth methods,have been successfully employed in the fabrication of highly flexible and stretchable nanoprobes for intracellular sensing.Additionally,nanowire arrays can be deployed on the cerebral cortex to record and analyze neural activity,opening new avenues for the treatment of neurological disorders.This review systematically examines recent advancements in nanowire-based flexible sensing technologies applied to wearable electronics,BCIs,and electronic skins,highlighting key design principles,operational mechanisms,and technological milestones achieved through growth,assembly,and transfer processes.These developments collectively advance high-performance health monitoring,deepen our understanding of neural activities,and facilitate the creation of novel,flexible,and stretchable electronic skins.Finally,we also present a summary and perspectives on the current challenges and future opportunities for nanowirebased flexible sensors.展开更多
Brain-computer interface(BCI)technology represents a burgeoning interdisciplinary domain that facilitates direct communication between individuals and external devices.The efficacy of BCI systems is largely contingent...Brain-computer interface(BCI)technology represents a burgeoning interdisciplinary domain that facilitates direct communication between individuals and external devices.The efficacy of BCI systems is largely contingent upon the progress in signal acquisition methodologies.This paper endeavors to provide an exhaustive synopsis of signal acquisition technologies within the realm of BCI by scrutinizing research publications from the last ten years.Our review synthesizes insights from both clinical and engineering viewpoints,delineating a comprehensive two-dimensional framework for understanding signal acquisition in BCIs.We delineate nine discrete categories of technologies,furnishing exemplars for each and delineating the salient challenges pertinent to these modalities.This review furnishes researchers and practitioners with a broad-spectrum comprehension of the signal acquisition landscape in BCI,and deliberates on the paramount issues presently confronting the field.Prospective enhancements in BCI signal acquisition should focus on harmonizing a multitude of disciplinary perspectives.Achieving equilibrium between signal fidelity,invasiveness,biocompatibility,and other pivotal considerations is imperative.By doing so,we can propel BCI technology forward,bolstering its effectiveness,safety,and depend-ability,thereby contributing to an auspicious future for human-technology integration.展开更多
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
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
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
The advancement in neuroscience and computer science promotes the ability of the human brain to communicate and interact with the environment,making brain–computer interface(BCI)top interdisciplinary research.Further...The advancement in neuroscience and computer science promotes the ability of the human brain to communicate and interact with the environment,making brain–computer interface(BCI)top interdisciplinary research.Furthermore,with the modern technology advancement in artificial intelligence(AI),including machine learning(ML)and deep learning(DL)methods,there is vast growing interest in the electroencephalogram(EEG)-based BCIs for AI-related visual,literal,and motion applications.In this review study,the literature on mainstreams of AI for the EEG-based BCI applications is investigated to fill gaps in the interdisciplinary BCI field.Specifically,the EEG signals and their main applications in BCI are first briefly introduced.Next,the latest AI technologies,including the ML and DL models,are presented to monitor and feedback human cognitive states.Finally,some BCI-inspired AI applications,including computer vision,natural language processing,and robotic control applications,are presented.The future research directions of the EEG-based BCI are highlighted in line with the AI technologies and applications.展开更多
Background Clinical brain-computer interface(BCI)for mental disorders is an emerging interdisciplinary research field,posing new ethical concerns and challenges,yet lacking practical ethical governance guidelines for ...Background Clinical brain-computer interface(BCI)for mental disorders is an emerging interdisciplinary research field,posing new ethical concerns and challenges,yet lacking practical ethical governance guidelines for stakeholders and the entire community.Aims This study aims to establish a multidisciplinary consensus of principles for ethical governance of clinical BCI research for mental disorders and offer practical ethical guidance to stakeholders involved.Methods A systematic literature review,symposium and roundtable discussions,and a pre-Delphi(round 0)survey were conducted to form the questionnaire for the three-round modified Delphi study.Two rounds of surveys,followed by a third round of independent interviews of 25 experts from BCI-related research domains,were involved.We conducted quantitative analysis of responses and agreements among experts to reveal the consensus and differences regarding the ethical governance of mental BCI research from a multidisciplinary perspective.Results The Delphi panel emphasised important concerns of ethical review practices and ethical principles within the BCI context,identified qualified and highly influential institutions and personnel in conducting and advancing clinical BCI research,and recognised prioritised aspects in the risk-benefit evaluation.Experts expressed diverse opinions on specific ethical concerns,including concerns about invasive technology,its impact on humanity and potential social consequences.Agreement was reached that the practices of ethical governance of clinical BCI for mental disorders should focus on patient voluntariness,autonomy,long-term effects and related assessments of BCI interventions,as well as privacy protection,transparent reporting and ensuring that the research is conducted in qualified institutions with strong data security.Conclusions Ethical governance of clinical research on BCI for mental disorders should include interdisciplinary experts to balance various needs and incorporate the expertise of different stakeholders to avoid serious ethical issues.It requires scientifically grounded approaches,continuous monitoring and interdisciplinary collaboration to ensure evidence-based policies,comprehensive risk assessments and transparency,thereby promoting responsible innovations and protecting patient rights and well-being.展开更多
Recent advances in neuroelectrode interface materials and modification technologies are reviewed. Brain-computer interface is the new method of human-computer interaction, which not only can realise the exchange of in...Recent advances in neuroelectrode interface materials and modification technologies are reviewed. Brain-computer interface is the new method of human-computer interaction, which not only can realise the exchange of information between the human brain and external devices, but also provides a brand-new means for the diagnosis and treatment of brain-related diseases. The neural electrode interface part of brain-computer interface is an important area for electrical, optical and chemical signal transmission between brain tissue system and external electronic devices, which determines the performance of brain-computer interface. In order to solve the problems of insufficient flexibility, insufficient signal recognition ability and insufficient biocompatibility of traditional rigid electrodes, researchers have carried out extensive studies on the neuroelectrode interface in terms of materials and modification techniques. This paper introduces the biological reactions that occur in neuroelectrodes after implantation into brain tissue and the decisive role of the electrode interface for electrode function. Following this, the latest research progress on neuroelectrode materials and interface materials is reviewed from the aspects of neuroelectrode materials and modification technologies, firstly taking materials as a clue, and then focusing on the preparation process of neuroelectrode coatings and the design scheme of functionalised structures.展开更多
Brain-computer interfaces(BCIs)have become a hotspot in the field of neuroscience,and have been presented frequently in various media over the past few years.In general,BCIs are considered a revolutionary technology f...Brain-computer interfaces(BCIs)have become a hotspot in the field of neuroscience,and have been presented frequently in various media over the past few years.In general,BCIs are considered a revolutionary technology for building an alternative and direct link between the brain and external devices.1 In recent years,BCIs have been rapidly developed and widely applied in clinical practice for the prevention,diagnosis,treatment,rehabilitation,and functional assistance of neurological disorders.Notably,they have demonstrated promising clinical efficacy for the rehabilitation of severe neurological disorders such as stroke,spinal cord injury,and amyotrophic lateral sclerosis.2e4 As a result,BCIs remain a prominent investigational focus,attracting increasing numbers of researchers and groups in the field.However,certain misunderstandings about BCIs remain,at least in part because different media often confuse the concept of BCIs,5 which has caused confusion among readers of this journal who are engaged in basic research and the clinical applications of neurorestoration.展开更多
We perceive that some Brain-Computer Interface (BCI) researchers believe in totally different origins of invasive and non-invasive electrical BCI signals. Based on available literature we argue, however, that although...We perceive that some Brain-Computer Interface (BCI) researchers believe in totally different origins of invasive and non-invasive electrical BCI signals. Based on available literature we argue, however, that although invasive and non-invasive BCI signals are different, the underlying origin of electrical BCIs signals is the same.展开更多
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
文摘Invasive as well as non-invasive neurotechnologies conceptualized to interface the central and peripheral nervous system have been probed for the past decades,which refer to electroencephalography,electrocorticography and microelectrode arrays.The challenges of these mentioned approaches are characterized by the bandwidth of the spatiotemporal resolution,which in turn is essential for large-area neuron recordings(Abiri et al.,2019).
基金The Open Project of State Key Laboratory of Smart Grid Protection and Operation Control in 2022(No.SGNR0000KJJS2302150).
文摘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.
文摘In recent years,with the continuous advancement of technolo-gies such as artificial intelligence,neurobiology,and sensors,braincomputer interface(Bcl)technology has embraced opportunitiesfor rapid development The"Guidelines for the Establishment ofNeurological Medical Service Price ltems(Trial)"recently issued bythe National Healthcare Security Administration specifically sets upseparate prospective items for new BCl technologies,which will un-doubtedly strongly facilitate the clinical application of BCl technologyas soon as possible,benefiting a broad range of patients.
文摘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.
基金supported by the Ministry of Science and Tech-nology of the People's Republic of China(2021ZD0201900),Project 5(2021ZD0201905).
文摘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.
基金supported by the National Natural Science Foundation of China(81600919)the Beijing Nova Program(Z181100006218050).
文摘The development of brain-computer interfaces(BCIs)has established a new communication channel between the brain and external devices for information transmission that requires no muscular signals[1].BCIs have been preliminarily studied to improve motor functions in patients with severe motor disabilities,especially lock-in syndrome.At present,the application of BCIs has been extensively validated.
基金the National Natural Science Foundation of China(Nos.61702395 and 61972302)the Science and Technology Projects of Xi’an,China(No.201809170CX11JC12)。
文摘An electroencephalogram(EEG)signal projection using kernel discriminative locality preserving canonical correlation analysis(KDLPCCA)-based correlation with steady-state visual evoked potential(SSVEP)templates for frequency recognition is presented in this paper.With KDLPCCA,not only a non-linear correlation but also local properties and discriminative information of each class sample are considered to extract temporal and frequency features of SSVEP signals.The new projected EEG features are classified with classical machine learning algorithms,namely,K-nearest neighbors(KNNs),naive Bayes,and random forest classifiers.To demonstrate the effectiveness of the proposed method,16-channel SSVEP data corresponding to 4 frequencies collected from 5 subjects were used to evaluate the performance.Compared with the state of the art canonical correlation analysis(CCA),experimental results show significant improvements in classification accuracy and information transfer rate(ITR),achieving 100%and 240 bits/min with 0.5 s sample block.The superior performance demonstrates that this method holds the promising potential to achieve satisfactory performance for high-accuracy SSVEP-based brain-computer interfaces.
文摘The Brain-Computer Interfaces(BCIs)had been proposed and used in therapeutics for decades.However,the need of time-consuming calibration phase and the lack of robustness,which are caused by little-labeled data,are restricting the advance and application of BCI,especially for the BCI based on motor imagery(MI).In this paper,we reviewed the recent development in the machine learning algorithm used in the MI-based BCI,which may provide potential solutions for addressing the issue.We classified these algorithms into two categories,namely,and enhancing the representation and expanding the training set.Specifically,these methods of enhancing the representation of features collected from few EEG trials are based on extracting features of multiple bands,regularization,and so on.The methods of expanding the training dataset include approaches of transfer learning(session to session transfer,subject to subject transfer)and generating artificial EEG data.The result of these techniques showed the resolution of the challenges to some extent.As a developing research area,the study of BCI algorithms in little-labeled data is increasingly requiring the advancement of human brain physiological structure research and more transfer learning algorithms research.
基金support received from the National Key Research Program of China(No.92164201)National Natural Science Foundation of China for Distinguished Young Scholars(No.62325403)+2 种基金Natural Science Foundation of Jiangsu Province(BK20230498)Jiangsu Funding Program for Excellent Postdoctoral Talent(2024ZB427)the National Natural Science Foundation of China(61934004).
文摘Flexible electronic devices with compliant mechanical deformability and electrical reliability have been a focal point of research over the past decade,particularly in the fields of wearable devices,brain-computer interfaces(BCIs),and electronic skins.These emerging applications impose stringent requirements on flexible sensors,necessitating not only their ability to withstand dynamic strains and conform to irregular surfaces but also to ensure long-term stable monitoring.To meet these demands,onedimensional nanowires,with high aspect ratios,large surface-to-volume ratios,and programmable geometric engineering,are widely regarded as ideal candidates for constructing high-performance flexible sensors.Various innovative assembly techniques have enabled the effective integration of these nanowires with flexible substrates.More excitingly,semiconductor nanowires,prepared through low-cost and efficient catalytic growth methods,have been successfully employed in the fabrication of highly flexible and stretchable nanoprobes for intracellular sensing.Additionally,nanowire arrays can be deployed on the cerebral cortex to record and analyze neural activity,opening new avenues for the treatment of neurological disorders.This review systematically examines recent advancements in nanowire-based flexible sensing technologies applied to wearable electronics,BCIs,and electronic skins,highlighting key design principles,operational mechanisms,and technological milestones achieved through growth,assembly,and transfer processes.These developments collectively advance high-performance health monitoring,deepen our understanding of neural activities,and facilitate the creation of novel,flexible,and stretchable electronic skins.Finally,we also present a summary and perspectives on the current challenges and future opportunities for nanowirebased flexible sensors.
基金supported by the National Natural Science Foun-dation of China(U2241208,62171473,61671424)the National Key Research and Development Program of China(2022YFC3602803,2023YFF1205300)Key Research and Development Program of Ningxia(2023BEG02063)。
文摘Brain-computer interface(BCI)technology represents a burgeoning interdisciplinary domain that facilitates direct communication between individuals and external devices.The efficacy of BCI systems is largely contingent upon the progress in signal acquisition methodologies.This paper endeavors to provide an exhaustive synopsis of signal acquisition technologies within the realm of BCI by scrutinizing research publications from the last ten years.Our review synthesizes insights from both clinical and engineering viewpoints,delineating a comprehensive two-dimensional framework for understanding signal acquisition in BCIs.We delineate nine discrete categories of technologies,furnishing exemplars for each and delineating the salient challenges pertinent to these modalities.This review furnishes researchers and practitioners with a broad-spectrum comprehension of the signal acquisition landscape in BCI,and deliberates on the paramount issues presently confronting the field.Prospective enhancements in BCI signal acquisition should focus on harmonizing a multitude of disciplinary perspectives.Achieving equilibrium between signal fidelity,invasiveness,biocompatibility,and other pivotal considerations is imperative.By doing so,we can propel BCI technology forward,bolstering its effectiveness,safety,and depend-ability,thereby contributing to an auspicious future for human-technology integration.
基金supported by the National Key R&D Program of China(2021YFF1200602)the National Science Fund for Excellent Overseas Scholars(0401260011)+3 种基金the National Defense Science and Technology Innovation Fund of Chinese Academy of Sciences(c02022088)the Tianjin Science and Technology Program(20JCZDJC00810)the National Natural Science Foundation of China(82202798)the Shanghai Sailing Program(22YF1404200).
文摘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.
基金Supported by the Ministry of Science and Technology of the People's Republic of China(2021ZD0201900)Project 5,No.2021ZD0201905Capital’s Funds for Health Improvement and Research,No.CFH 2022-2-4115.
文摘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.
文摘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.
文摘The advancement in neuroscience and computer science promotes the ability of the human brain to communicate and interact with the environment,making brain–computer interface(BCI)top interdisciplinary research.Furthermore,with the modern technology advancement in artificial intelligence(AI),including machine learning(ML)and deep learning(DL)methods,there is vast growing interest in the electroencephalogram(EEG)-based BCIs for AI-related visual,literal,and motion applications.In this review study,the literature on mainstreams of AI for the EEG-based BCI applications is investigated to fill gaps in the interdisciplinary BCI field.Specifically,the EEG signals and their main applications in BCI are first briefly introduced.Next,the latest AI technologies,including the ML and DL models,are presented to monitor and feedback human cognitive states.Finally,some BCI-inspired AI applications,including computer vision,natural language processing,and robotic control applications,are presented.The future research directions of the EEG-based BCI are highlighted in line with the AI technologies and applications.
基金funded by the Shanghai Philosophy and Social Science Planning Project (2021BZX008)the National Social Science Foundation of China (23BZX110)the National Office for Philosophy and Social Science (20&ZD045).
文摘Background Clinical brain-computer interface(BCI)for mental disorders is an emerging interdisciplinary research field,posing new ethical concerns and challenges,yet lacking practical ethical governance guidelines for stakeholders and the entire community.Aims This study aims to establish a multidisciplinary consensus of principles for ethical governance of clinical BCI research for mental disorders and offer practical ethical guidance to stakeholders involved.Methods A systematic literature review,symposium and roundtable discussions,and a pre-Delphi(round 0)survey were conducted to form the questionnaire for the three-round modified Delphi study.Two rounds of surveys,followed by a third round of independent interviews of 25 experts from BCI-related research domains,were involved.We conducted quantitative analysis of responses and agreements among experts to reveal the consensus and differences regarding the ethical governance of mental BCI research from a multidisciplinary perspective.Results The Delphi panel emphasised important concerns of ethical review practices and ethical principles within the BCI context,identified qualified and highly influential institutions and personnel in conducting and advancing clinical BCI research,and recognised prioritised aspects in the risk-benefit evaluation.Experts expressed diverse opinions on specific ethical concerns,including concerns about invasive technology,its impact on humanity and potential social consequences.Agreement was reached that the practices of ethical governance of clinical BCI for mental disorders should focus on patient voluntariness,autonomy,long-term effects and related assessments of BCI interventions,as well as privacy protection,transparent reporting and ensuring that the research is conducted in qualified institutions with strong data security.Conclusions Ethical governance of clinical research on BCI for mental disorders should include interdisciplinary experts to balance various needs and incorporate the expertise of different stakeholders to avoid serious ethical issues.It requires scientifically grounded approaches,continuous monitoring and interdisciplinary collaboration to ensure evidence-based policies,comprehensive risk assessments and transparency,thereby promoting responsible innovations and protecting patient rights and well-being.
基金the National Key Research and Development Program,No.2021YFB3800800the National Natural Science Foundation of China,Nos.31922041,32171341,32301113,the 111 Project,No.B14018+3 种基金the Science and Technology Innovation Project and Excellent Academic Leader Project of Shanghai Science and Technology Committee,Nos.21S31901500,21XD1421100the National Postdoctoral Program for Innovative Talents,No.BX20230122the Shanghai Sailing Program,No.23YF1409700the China Postdoctoral Science Foundation,No.D100-5R-22114.
文摘Recent advances in neuroelectrode interface materials and modification technologies are reviewed. Brain-computer interface is the new method of human-computer interaction, which not only can realise the exchange of information between the human brain and external devices, but also provides a brand-new means for the diagnosis and treatment of brain-related diseases. The neural electrode interface part of brain-computer interface is an important area for electrical, optical and chemical signal transmission between brain tissue system and external electronic devices, which determines the performance of brain-computer interface. In order to solve the problems of insufficient flexibility, insufficient signal recognition ability and insufficient biocompatibility of traditional rigid electrodes, researchers have carried out extensive studies on the neuroelectrode interface in terms of materials and modification techniques. This paper introduces the biological reactions that occur in neuroelectrodes after implantation into brain tissue and the decisive role of the electrode interface for electrode function. Following this, the latest research progress on neuroelectrode materials and interface materials is reviewed from the aspects of neuroelectrode materials and modification technologies, firstly taking materials as a clue, and then focusing on the preparation process of neuroelectrode coatings and the design scheme of functionalised structures.
文摘Brain-computer interfaces(BCIs)have become a hotspot in the field of neuroscience,and have been presented frequently in various media over the past few years.In general,BCIs are considered a revolutionary technology for building an alternative and direct link between the brain and external devices.1 In recent years,BCIs have been rapidly developed and widely applied in clinical practice for the prevention,diagnosis,treatment,rehabilitation,and functional assistance of neurological disorders.Notably,they have demonstrated promising clinical efficacy for the rehabilitation of severe neurological disorders such as stroke,spinal cord injury,and amyotrophic lateral sclerosis.2e4 As a result,BCIs remain a prominent investigational focus,attracting increasing numbers of researchers and groups in the field.However,certain misunderstandings about BCIs remain,at least in part because different media often confuse the concept of BCIs,5 which has caused confusion among readers of this journal who are engaged in basic research and the clinical applications of neurorestoration.
文摘We perceive that some Brain-Computer Interface (BCI) researchers believe in totally different origins of invasive and non-invasive electrical BCI signals. Based on available literature we argue, however, that although invasive and non-invasive BCI signals are different, the underlying origin of electrical BCIs signals is the same.
基金supported by the National Key Research and Development Program of China (2017YFB1002505)the National Natural Science Foundation of China (61633010, 91420302, and 61503143)+1 种基金the Natural Science Foundation of Guangdong Province, China (2014A030312005 and 2014A030310244)the Pearl River S&T Nova Program of Guangzhou Municipality, China (201710010038)
文摘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.
