Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented ...Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented in this paper. It applies the recognition rate of training samples to the learning progress of network parameters. The learning vector quantization is employed to group training samples and the Genetic algorithm （GA） is used for training the network＇ s smoothing parameters and hidden central vector for detemlining hidden neurons. Utilizing the standard dataset I （a） of BCI Competition 2003 and comparing with other classification methods, the experiment results show that the best performance of pattern recognition Js got in this way, and the classification accuracy can reach to 93.8%, which improves over 5% compared with the best result （88.7 % ） of the competition. This technology provides an effective way to EEG classification in practical system of BCI.展开更多
In the study of brain-computer interfaces,a method of feature extraction and classification used fortwo kinds of imaginations is proposed.It considers Euclidean distance between mean traces recorded fromthe channels w...In the study of brain-computer interfaces,a method of feature extraction and classification used fortwo kinds of imaginations is proposed.It considers Euclidean distance between mean traces recorded fromthe channels with two kinds of imaginations as a feature,and determines imagination classes using thresh-old value.It analyzed the background of experiment and theoretical foundation referring to the data sets ofBCI 2003,and compared the classification precision with the best result of the competition.The resultshows that the method has a high precision and is advantageous for being applied to practical systems.展开更多
While brain computer interfaces(BCIs)ofer the potential of allowing those sufering from loss of muscle control to once again fully engage with their environment by bypassing the afected motor system and decoding user ...While brain computer interfaces(BCIs)ofer the potential of allowing those sufering from loss of muscle control to once again fully engage with their environment by bypassing the afected motor system and decoding user intentions directly from brain activity,they are prone to errors.One possible avenue for BCI performance improvement is to detect when the BCI user perceives the BCI to have made an unintended action and thus take corrective actions.Error-related potentials(ErrPs)are neural correlates of error awareness and as such can provide an indication of when a BCI system is not performing according to the user’s intentions.Here,we investigate the brain signals of an implanted BCI user sufering from locked-in syndrome(LIS)due to late-stage ALS that prevents her from being able to speak or move but not from using her BCI at home on a daily basis to communicate,for the presence of error-related signals.We frst establish the presence of an ErrP originating from the dorsolateral pre-frontal cortex(dLPFC)in response to errors made during a discrete feedback task that mimics the click-based spelling software she uses to communicate.Then,we show that this ErrP can also be elicited by cursor movement errors in a continuous BCI cursor control task.This work represents a frst step toward detecting ErrPs during the daily home use of a communications BCI.展开更多
Brain signals refer to electrical signals or metabolic changes that occur as a consequence of brain cell activity.Among the various non-invasive measurement methods,electroencephalogram(EEG)stands out as a widely empl...Brain signals refer to electrical signals or metabolic changes that occur as a consequence of brain cell activity.Among the various non-invasive measurement methods,electroencephalogram(EEG)stands out as a widely employed technique,providing valuable insights into brain patterns.The deviations observed in EEG reading serve as indicators of abnormal brain activity,which is associated with neurological diseases.Brain‒computer interface(BCI)systems enable the direct extraction and transmission of information from the human brain,facilitating interaction with external devices.Notably,the emergence of artificial intelligence(AI)has had a profound impact on the enhancement of precision and accuracy in BCI technology,thereby broadening the scope of research in this field.AI techniques,encompassing machine learning(ML)and deep learning(DL)models,have demonstrated remarkable success in classifying and predicting various brain diseases.This comprehensive review investigates the application of AI in EEG-based brain disease diagnosis,highlighting advancements in AI algorithms.展开更多
Brain-computer interface(BCI)is a revolutionary technology that can provide human beings with an alternative communication pathway between the brain and external environment.Recent technological advances have signific...Brain-computer interface(BCI)is a revolutionary technology that can provide human beings with an alternative communication pathway between the brain and external environment.Recent technological advances have significantly expanded its application to neurological rehabilitation and healthcare for patients with severe neurology disorders.Neurostimulation based on brain interface is another promising technology for neuro-modulation and intervention for patients with various neurological,psychiatric and psychological problems.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
Brain-computer interface(BCI)is an emerging technology with significant potential in clinical neurorestoratology.Over the past two years,various BCI systems,either invasive or non-invasive,have demonstrated promising ...Brain-computer interface(BCI)is an emerging technology with significant potential in clinical neurorestoratology.Over the past two years,various BCI systems,either invasive or non-invasive,have demonstrated promising effectives in clinical neurorestorative applications.These advancements have led to notable progress in areas such as motor function recovery,neurofeedback training,assistive communication,and stroke rehabilitation.展开更多
Neural damage has been a great challenge to the medical field for a very long time.The emergence of brain–computer interfaces(BCIs)offered a new possibility to enhance the activity of daily living and provide a new f...Neural damage has been a great challenge to the medical field for a very long time.The emergence of brain–computer interfaces(BCIs)offered a new possibility to enhance the activity of daily living and provide a new formation of entertainment for those with disabilities.Intracortical BCIs,which require the implantation of microelectrodes,can receive neuronal signals with a high spatial and temporal resolution from the individual’s cortex.When BCI decoded cortical signals and mapped them to external devices,it displayed the ability not only to replace part of the human motor function but also to help individuals restore certain neurological functions.In this review,we focus on human intracortical BCI research using microelectrode arrays and summarize the main directions and the latest results in this field.In general,we found that intracortical BCI research based on motor neuroprosthetics and functional electrical stimulation have already achieved some simple functional replacement and treatment of motor function.Pioneering work in the posterior parietal cortex has given us a glimpse of the potential that intracortical BCIs have to control external devices and receive various sensory information.展开更多
Brain–computer interface(BCI)is a novel communication method between brain and machine.It enables signals from the human brain to influence or control external devices.Currently,much research interest is focused on t...Brain–computer interface(BCI)is a novel communication method between brain and machine.It enables signals from the human brain to influence or control external devices.Currently,much research interest is focused on the BCI-based neural rehabilitation of patients with motor and cognitive diseases.Over the decades,BCI has become an alternative treatment for motor and cognitive rehabilitation.Previous studies demonstrated the usefulness of BCI intervention in restoring motor function and recovery of the damaged brain.Electroencephalogram(EEG)-based BCI intervention could cast light on the mechanisms underlying neuroplasticity during upper limb recovery by providing feedback to the damaged brain.BCI could act as a useful tool to aid patients with daily communication and basic movement in severe motor loss cases like amyotrophic lateral sclerosis(ALS).Furthermore,recent findings have reported the therapeutic efficacy of BCI in people suffering from other diseases with different levels of motor impairment such as spastic cerebral palsy,neuropathic pain,etc.Besides motor functional recovery,BCI also plays its role in improving the behavior of patients with cognitive diseases like attention-deficit/hyperactivity disorder(ADHD).The BCI-based neurofeedback training is focused on either reducing the ratio of theta and beta rhythm,or enabling the patients to regulate their own slow cortical potentials,and both have made progress in increasing attention and alertness.With summary of several clinical studies with strong evidence,we present cutting edge results from the clinical application of BCI in motor and cognitive diseases,including stroke,spinal cord injury,ALS,and ADHD.展开更多
This study applied a steady-state visual evoked potential(SSVEP)based brain–computer interface(BCI)to a patient in lock-in state with amyotrophic lateral sclerosis(ALS)and validated its feasibility for communication....This study applied a steady-state visual evoked potential(SSVEP)based brain–computer interface(BCI)to a patient in lock-in state with amyotrophic lateral sclerosis(ALS)and validated its feasibility for communication.The developed calibration-free and asynchronous spelling system provided a natural and efficient communication experience for the patient,achieving a maximum free-spelling accuracy above 90%and an information transfer rate of over 22.203 bits/min.A set of standard frequency scanning and task spelling data were also acquired to evaluate the patient’s SSVEP response and to facilitate further personalized BCI design.The results demonstrated that the proposed SSVEP-based BCI system was practical and efficient enough to provide daily life communication for ALS patients.展开更多
A brain-computer interface(BCI)based on motor imagery(MI)provides additional control pathways by decoding the intentions of the brain.MI ability has great intra-individual variability,and the majority of MI-BCI system...A brain-computer interface(BCI)based on motor imagery(MI)provides additional control pathways by decoding the intentions of the brain.MI ability has great intra-individual variability,and the majority of MI-BCI systems are unable to adapt to this variability,leading to poor training effects.Therefore,prediction of MI ability is needed.In this study,we propose an MI ability predictor based on multi-frequency EEG features.To validate the performance of the predictor,a video-guided paradigm and a traditional MI paradigm are designed,and the predictor is applied to both paradigms.The results demonstrate that all subjects achieved>85%prediction precision in both applications,with a maximum of 96%.This study indicates that the predictor can accurately predict the individuals’MI ability in different states,provide the scientific basis for personalized training,and enhance the effect of MI-BCI training.展开更多
Addressing the vulnerability of contact-based keyboard password systems to disclosure,this paper proposes and validates the feasibility of a non-contact secure password system based on brain-computer interface(BCI)tec...Addressing the vulnerability of contact-based keyboard password systems to disclosure,this paper proposes and validates the feasibility of a non-contact secure password system based on brain-computer interface(BCI)technology that detects steady-state visual evoked potential(SSVEP)signals.The system first lets a testee look at a digital stimulus source flashing at a specific frequency,and uses a wearable dry electrode sensor to collect the SSVEP signal.Secondly,a canonical correlation analysis method is applied to analyze the frequency of the stimulus source that the testee is looking at,and feeds back a code result through headphones.Finally,after all password codes are input,the system makes a judgment and provides visual feedback to the testee.Experiments were conducted to test the accuracy of the system,where twelve stimulus target frequencies between 10-16Hz were selected within the easily recognizable flicker frequency range of human brain,and each of them was tested for 12 times.The results demonstrate that this SSVEP-BCI-based system is feasible,achieving an average accuracy rate of 97.2%,and exhibits promising applications in various domains such as financial transactions and identity recognition.展开更多
Human cooperation relies on key features of social interaction in order to reach desirable outcomes.Similarly,human-robot interaction may benefit from integration with human–human interaction factors.In this paper,we...Human cooperation relies on key features of social interaction in order to reach desirable outcomes.Similarly,human-robot interaction may benefit from integration with human–human interaction factors.In this paper,we aim to investigate brain-to-brain coupling during motor imagery(MI)-based brain-computer interface(BCI)training using eye-contact and hand-touch interaction.Twelve pairs of friends(experimental group)and 10 pairs of strangers(control group)were recruited for MI-based BCI tests concurrent with electroencephalography(EEG)hyperscanning.Event-related desynchronization(ERD)was estimated to measure cortical activation,and interbrain functional connectivity was assessed using multilevel statistical analysis.Furthermore,we compared BCI classification performance under different social interaction conditions.In the experimental group,greater ERD was found around the contralateral sensorimotor cortex under social interaction conditions compared with MI without any social interaction.Notably,EEG channels with decreased power were mainly distributed around the frontal,central,and occipital regions.A significant increase in interbrain coupling was also found under social interaction conditions.BCI decoding accuracies were significantly improved in the eye contact condition and eye and hand contact condition compared with the no-interaction condition.However,for the strangers’group,no positive effects were observed in comparisons of cortical activations between interaction and no-interaction conditions.These findings indicate that social interaction can improve the neural synchronization between familiar partners with enhanced brain activations and brain-to-brain coupling.This study may provide a novel method for enhancing MI-based BCI performance in conjunction with neural synchronization between users.展开更多
Brain-computer interface(BCI)technology is a rapidly evolving interdisciplinary field that promises to revolutionize our understanding of and interaction with the brain,particularly in neurorestoration.BCIs enable dir...Brain-computer interface(BCI)technology is a rapidly evolving interdisciplinary field that promises to revolutionize our understanding of and interaction with the brain,particularly in neurorestoration.BCIs enable direct communication between the brain and external devices,bypassing traditional pathways involving peripheral nerves and muscles.BCI technologies can be classified into several categories based on the mode of signal acquisition,with respect to both clinical and engineering aspects.1 This editorial explores the potential applications of various BCI technologies in neurorestoration,drawing insights from recent advances.展开更多
The National Natural Science Foundation of China is one of the major funding agencies for neuro rehabilitation research in China.This study reviews the frontier directions and achievements in the field of neurorehabil...The National Natural Science Foundation of China is one of the major funding agencies for neuro rehabilitation research in China.This study reviews the frontier directions and achievements in the field of neurorehabilitation in China and wo rldwide.We used data from the Web of Science Core Collection(WoSCC) database to analyze the publications and data provided by the National Natural Science Foundation of China to analyze funding information.In addition,the prospects for neurorehabilitation research in China are discussed.From 2010 to 2022,a total of 74,220 publications in neurorehabilitation were identified,with there being an overall upward tendency.During this period,the National Natural Science Foundation of China has funded 476 research projects with a total funding of 192.38 million RMB to support neuro rehabilitation research in China.With the support of the National Natural Science Foundation of China,China has made some achievements in neurorehabilitation research.Research related to neurorehabilitation is believed to be making steady and significant progress in China.展开更多
To derive critical signal features from intracranial electroencephalograms of epileptic patients in order to design instructions for feedback-type electrical stimulation systems.The Detrended Fluctuation Analysis(DFA)...To derive critical signal features from intracranial electroencephalograms of epileptic patients in order to design instructions for feedback-type electrical stimulation systems.The Detrended Fluctuation Analysis(DFA)exponent is chosen as the classification exponent,and the disparities between indicators representing distinct seizure states and the classification efficacy of rudimentary machine learning models are computed.The DFA exponent exhibited a statistically significant variation among the pre-ictal,ictal period,and post-ictal stages.The Linear Discriminant Analysis model demonstrates the highest accuracy among the three basic machine learning models,whereas the Naive Bayesian model necessitates the least amount of computational and storage space.The set of DFA exponents is employed as an intermediary variable in the machine learning process.The resultant model possesses the capability to function as a feedback trigger program for electrical stimulation systems of the feedback variety,specifically within the domain of neural modulation in epilepsy.展开更多
The brain-computer interface(BCI)technology has received lots of attention in the field of scientific research because it can help disabled people improve their quality of life.Steady-state visual evoked potential(SSV...The brain-computer interface(BCI)technology has received lots of attention in the field of scientific research because it can help disabled people improve their quality of life.Steady-state visual evoked potential(SSVEP)is the most researched BCI experimental paradigm,which offers the advantages of high signal-to-noise ratio and short training-time requirement by users.In a complete BCI system,the two most critical components are the experimental paradigm and decoding algorithm.However,a systematic combination of the SSVEP experimental paradigm and decoding algorithms is missing in existing studies.In the present study,the transient visual evoked potential,SSVEP,and various improved SSVEP paradigms are compared and analyzed,and the problems and development bottlenecks in the experimental paradigm are finally pointed out.Subsequently,the canonical correlation analysis and various improved decoding algorithms are introduced,and the opportunities and challenges of the SSVEP decoding algorithm are discussed.展开更多
In recent years,Brain-Computer Interface(BCI)system gained much popularity since it aims at establishing the communication between human brain and computer.BCI systems are applied in several research areas such as neu...In recent years,Brain-Computer Interface(BCI)system gained much popularity since it aims at establishing the communication between human brain and computer.BCI systems are applied in several research areas such as neuro-rehabilitation,robots,exoeskeletons,etc.Electroencephalography(EEG)is a technique commonly applied in capturing brain signals.It is incorporated in BCI systems since it has attractive features such as noninvasive nature,high time-resolution output,mobility and cost-effective.EEG classification process is highly essential in decision making process and it incorporates different processes namely,feature extraction,feature selection,and classification.With this motivation,the current research paper presents an Intelligent Optimal Fuzzy Support Vector Machine-based EEC recognition(IOFSVM-EEG)model for BCI system.Independent Component Analysis(ICA)technique is applied onto the proposed IOFSVM-EEG model to remove the artefacts that exist in EEG signal and to retain the meaningful EEG information.Besides,Common Spatial Pattern(CSP)-based feature extraction technique is utilized to derive a helpful set of feature vectors from the preprocessed EEG signals.Moreover,OFSVM method is applied in the classification of EEG signals,in which the parameters involved in FSVM are optimally tuned using Grasshopper Optimization Algorithm(GOA).In order to validate the enhanced EEG recognition outcomes of the proposed IOFSVM-EEG model,an extensive set of experiments was conducted.The outcomes were examined under distinct aspects.The experimental results highlighted the enhanced performance of the presented IOFSVM-EEG model over other state-of-the-art methods.展开更多
To collect neuronal activity data from awake, freely behaving animals, we developed miniature telemetry recording system. The integrated system consists of four major components： l） Microelectrodes and micro-driver ...To collect neuronal activity data from awake, freely behaving animals, we developed miniature telemetry recording system. The integrated system consists of four major components： l） Microelectrodes and micro-driver assembly, 2） analog front end （AFE）, 3） programmable system on chip （PSoC）, and 4） ra- dio transceiver and the LabVIEW were used as a platform for the graphic user interface. The result showed the system was able to record and analyze neuronal recordings in freely moving animals and lasted continuously for a time period of a week or more. This is very useful for the study of the interdisciplinary research of neu- roscience and information engineering techniques. The circuits and architecture of the devices can be adapted for neurobiology and research with other small animals.展开更多
Kernel adaptive algorithm is an extension of adaptive algorithm in nonlinear,and widely used in the field of non-stationary signal processing.But the distribution of classic data sets seems relatively regular and simp...Kernel adaptive algorithm is an extension of adaptive algorithm in nonlinear,and widely used in the field of non-stationary signal processing.But the distribution of classic data sets seems relatively regular and simple in time series.The distribution of the electroencephalograph(EEG)signal is more randomness and non-stationarity,so online prediction of EEG signal can further verify the robustness and applicability of kernel adaptive algorithms.What’s more,the purpose of modeling and analyzing the time series of EEG signals is to discover and extract valuable information,and to reveal the internal relations of EEG signals.The time series prediction of EEG plays an important role in EEG time series analysis.In this paper,kernel RLS tracker(KRLST)is presented to online predict the EEG signals of motor imagery and compared with other 13 kernel adaptive algorithms.The experimental results show that KRLST algorithm has the best effect on the brain computer interface(BCI)dataset.展开更多
基金Supported by the National Natural Science Foundation of China (No. 30570485)the Shanghai "Chen Guang" Project (No. 09CG69).
文摘Aiming at the topic of electroencephalogram （EEG） pattern recognition in brain computer interface （BCI）, a classification method based on probabilistic neural network （PNN） with supervised learning is presented in this paper. It applies the recognition rate of training samples to the learning progress of network parameters. The learning vector quantization is employed to group training samples and the Genetic algorithm （GA） is used for training the network＇ s smoothing parameters and hidden central vector for detemlining hidden neurons. Utilizing the standard dataset I （a） of BCI Competition 2003 and comparing with other classification methods, the experiment results show that the best performance of pattern recognition Js got in this way, and the classification accuracy can reach to 93.8%, which improves over 5% compared with the best result （88.7 % ） of the competition. This technology provides an effective way to EEG classification in practical system of BCI.
基金supported by the Shanghai Education Commission Foundation for Excellent Young High Education Teacher(No.sdj08001)
文摘In the study of brain-computer interfaces,a method of feature extraction and classification used fortwo kinds of imaginations is proposed.It considers Euclidean distance between mean traces recorded fromthe channels with two kinds of imaginations as a feature,and determines imagination classes using thresh-old value.It analyzed the background of experiment and theoretical foundation referring to the data sets ofBCI 2003,and compared the classification precision with the best result of the competition.The resultshows that the method has a high precision and is advantageous for being applied to practical systems.
文摘While brain computer interfaces(BCIs)ofer the potential of allowing those sufering from loss of muscle control to once again fully engage with their environment by bypassing the afected motor system and decoding user intentions directly from brain activity,they are prone to errors.One possible avenue for BCI performance improvement is to detect when the BCI user perceives the BCI to have made an unintended action and thus take corrective actions.Error-related potentials(ErrPs)are neural correlates of error awareness and as such can provide an indication of when a BCI system is not performing according to the user’s intentions.Here,we investigate the brain signals of an implanted BCI user sufering from locked-in syndrome(LIS)due to late-stage ALS that prevents her from being able to speak or move but not from using her BCI at home on a daily basis to communicate,for the presence of error-related signals.We frst establish the presence of an ErrP originating from the dorsolateral pre-frontal cortex(dLPFC)in response to errors made during a discrete feedback task that mimics the click-based spelling software she uses to communicate.Then,we show that this ErrP can also be elicited by cursor movement errors in a continuous BCI cursor control task.This work represents a frst step toward detecting ErrPs during the daily home use of a communications BCI.
基金supported by the National Key Research and Development Project of China(No.2021ZD0200405)the National Natural Science Foundation of China(Nos.62271443,32250008,and 82330064).
文摘Brain signals refer to electrical signals or metabolic changes that occur as a consequence of brain cell activity.Among the various non-invasive measurement methods,electroencephalogram(EEG)stands out as a widely employed technique,providing valuable insights into brain patterns.The deviations observed in EEG reading serve as indicators of abnormal brain activity,which is associated with neurological diseases.Brain‒computer interface(BCI)systems enable the direct extraction and transmission of information from the human brain,facilitating interaction with external devices.Notably,the emergence of artificial intelligence(AI)has had a profound impact on the enhancement of precision and accuracy in BCI technology,thereby broadening the scope of research in this field.AI techniques,encompassing machine learning(ML)and deep learning(DL)models,have demonstrated remarkable success in classifying and predicting various brain diseases.This comprehensive review investigates the application of AI in EEG-based brain disease diagnosis,highlighting advancements in AI algorithms.
文摘Brain-computer interface(BCI)is a revolutionary technology that can provide human beings with an alternative communication pathway between the brain and external environment.Recent technological advances have significantly expanded its application to neurological rehabilitation and healthcare for patients with severe neurology disorders.Neurostimulation based on brain interface is another promising technology for neuro-modulation and intervention for patients with various neurological,psychiatric and psychological problems.
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.
文摘Brain-computer interface(BCI)is an emerging technology with significant potential in clinical neurorestoratology.Over the past two years,various BCI systems,either invasive or non-invasive,have demonstrated promising effectives in clinical neurorestorative applications.These advancements have led to notable progress in areas such as motor function recovery,neurofeedback training,assistive communication,and stroke rehabilitation.
基金supported by National key R&D plan,China(No.2017YFC1308500)the Public Projects of Zhejiang Province,China(No.2019C03033).
文摘Neural damage has been a great challenge to the medical field for a very long time.The emergence of brain–computer interfaces(BCIs)offered a new possibility to enhance the activity of daily living and provide a new formation of entertainment for those with disabilities.Intracortical BCIs,which require the implantation of microelectrodes,can receive neuronal signals with a high spatial and temporal resolution from the individual’s cortex.When BCI decoded cortical signals and mapped them to external devices,it displayed the ability not only to replace part of the human motor function but also to help individuals restore certain neurological functions.In this review,we focus on human intracortical BCI research using microelectrode arrays and summarize the main directions and the latest results in this field.In general,we found that intracortical BCI research based on motor neuroprosthetics and functional electrical stimulation have already achieved some simple functional replacement and treatment of motor function.Pioneering work in the posterior parietal cortex has given us a glimpse of the potential that intracortical BCIs have to control external devices and receive various sensory information.
基金supported by High Level-Hospital Program,Health Commission of Guangdong Province,China(HKUSZH201902033).
文摘Brain–computer interface(BCI)is a novel communication method between brain and machine.It enables signals from the human brain to influence or control external devices.Currently,much research interest is focused on the BCI-based neural rehabilitation of patients with motor and cognitive diseases.Over the decades,BCI has become an alternative treatment for motor and cognitive rehabilitation.Previous studies demonstrated the usefulness of BCI intervention in restoring motor function and recovery of the damaged brain.Electroencephalogram(EEG)-based BCI intervention could cast light on the mechanisms underlying neuroplasticity during upper limb recovery by providing feedback to the damaged brain.BCI could act as a useful tool to aid patients with daily communication and basic movement in severe motor loss cases like amyotrophic lateral sclerosis(ALS).Furthermore,recent findings have reported the therapeutic efficacy of BCI in people suffering from other diseases with different levels of motor impairment such as spastic cerebral palsy,neuropathic pain,etc.Besides motor functional recovery,BCI also plays its role in improving the behavior of patients with cognitive diseases like attention-deficit/hyperactivity disorder(ADHD).The BCI-based neurofeedback training is focused on either reducing the ratio of theta and beta rhythm,or enabling the patients to regulate their own slow cortical potentials,and both have made progress in increasing attention and alertness.With summary of several clinical studies with strong evidence,we present cutting edge results from the clinical application of BCI in motor and cognitive diseases,including stroke,spinal cord injury,ALS,and ADHD.
基金supported by the Key Clinical Projects of Peking University Third Hospital(No.Y76437-01)National Key Research and Development Program of China(No.2017YFB1002505)+2 种基金National Natural Science Foundation of China under Grant(No.61431007)Key Research and Development Program of Guangdong Province(No.2018B030339001)Doctoral Brain+X Seed Grand Program of Tsinghua University.
文摘This study applied a steady-state visual evoked potential(SSVEP)based brain–computer interface(BCI)to a patient in lock-in state with amyotrophic lateral sclerosis(ALS)and validated its feasibility for communication.The developed calibration-free and asynchronous spelling system provided a natural and efficient communication experience for the patient,achieving a maximum free-spelling accuracy above 90%and an information transfer rate of over 22.203 bits/min.A set of standard frequency scanning and task spelling data were also acquired to evaluate the patient’s SSVEP response and to facilitate further personalized BCI design.The results demonstrated that the proposed SSVEP-based BCI system was practical and efficient enough to provide daily life communication for ALS patients.
基金supported by the Natural Science Foundation of Hebei Province(F2024202019)the National Natural Science Foundation of China(32201072).
文摘A brain-computer interface(BCI)based on motor imagery(MI)provides additional control pathways by decoding the intentions of the brain.MI ability has great intra-individual variability,and the majority of MI-BCI systems are unable to adapt to this variability,leading to poor training effects.Therefore,prediction of MI ability is needed.In this study,we propose an MI ability predictor based on multi-frequency EEG features.To validate the performance of the predictor,a video-guided paradigm and a traditional MI paradigm are designed,and the predictor is applied to both paradigms.The results demonstrate that all subjects achieved>85%prediction precision in both applications,with a maximum of 96%.This study indicates that the predictor can accurately predict the individuals’MI ability in different states,provide the scientific basis for personalized training,and enhance the effect of MI-BCI training.
基金Supported by Innovative Talents Training Project in the Basic Educational Stage of Beijing(“Soaring Program”Instrument and Student Training in Aerospace Field,Under No.631306)。
文摘Addressing the vulnerability of contact-based keyboard password systems to disclosure,this paper proposes and validates the feasibility of a non-contact secure password system based on brain-computer interface(BCI)technology that detects steady-state visual evoked potential(SSVEP)signals.The system first lets a testee look at a digital stimulus source flashing at a specific frequency,and uses a wearable dry electrode sensor to collect the SSVEP signal.Secondly,a canonical correlation analysis method is applied to analyze the frequency of the stimulus source that the testee is looking at,and feeds back a code result through headphones.Finally,after all password codes are input,the system makes a judgment and provides visual feedback to the testee.Experiments were conducted to test the accuracy of the system,where twelve stimulus target frequencies between 10-16Hz were selected within the easily recognizable flicker frequency range of human brain,and each of them was tested for 12 times.The results demonstrate that this SSVEP-BCI-based system is feasible,achieving an average accuracy rate of 97.2%,and exhibits promising applications in various domains such as financial transactions and identity recognition.
基金supported by the National Natural Science Foundation of China(52305315)China Postdoctoral Science Foundation(2023M731938)+1 种基金National Key Research and Development Program of China(2022YFC3601104)Beijing Nova Program(20230484288).
文摘Human cooperation relies on key features of social interaction in order to reach desirable outcomes.Similarly,human-robot interaction may benefit from integration with human–human interaction factors.In this paper,we aim to investigate brain-to-brain coupling during motor imagery(MI)-based brain-computer interface(BCI)training using eye-contact and hand-touch interaction.Twelve pairs of friends(experimental group)and 10 pairs of strangers(control group)were recruited for MI-based BCI tests concurrent with electroencephalography(EEG)hyperscanning.Event-related desynchronization(ERD)was estimated to measure cortical activation,and interbrain functional connectivity was assessed using multilevel statistical analysis.Furthermore,we compared BCI classification performance under different social interaction conditions.In the experimental group,greater ERD was found around the contralateral sensorimotor cortex under social interaction conditions compared with MI without any social interaction.Notably,EEG channels with decreased power were mainly distributed around the frontal,central,and occipital regions.A significant increase in interbrain coupling was also found under social interaction conditions.BCI decoding accuracies were significantly improved in the eye contact condition and eye and hand contact condition compared with the no-interaction condition.However,for the strangers’group,no positive effects were observed in comparisons of cortical activations between interaction and no-interaction conditions.These findings indicate that social interaction can improve the neural synchronization between familiar partners with enhanced brain activations and brain-to-brain coupling.This study may provide a novel method for enhancing MI-based BCI performance in conjunction with neural synchronization between users.
基金supported by the National Natural Science Foundation 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 is a rapidly evolving interdisciplinary field that promises to revolutionize our understanding of and interaction with the brain,particularly in neurorestoration.BCIs enable direct communication between the brain and external devices,bypassing traditional pathways involving peripheral nerves and muscles.BCI technologies can be classified into several categories based on the mode of signal acquisition,with respect to both clinical and engineering aspects.1 This editorial explores the potential applications of various BCI technologies in neurorestoration,drawing insights from recent advances.
文摘The National Natural Science Foundation of China is one of the major funding agencies for neuro rehabilitation research in China.This study reviews the frontier directions and achievements in the field of neurorehabilitation in China and wo rldwide.We used data from the Web of Science Core Collection(WoSCC) database to analyze the publications and data provided by the National Natural Science Foundation of China to analyze funding information.In addition,the prospects for neurorehabilitation research in China are discussed.From 2010 to 2022,a total of 74,220 publications in neurorehabilitation were identified,with there being an overall upward tendency.During this period,the National Natural Science Foundation of China has funded 476 research projects with a total funding of 192.38 million RMB to support neuro rehabilitation research in China.With the support of the National Natural Science Foundation of China,China has made some achievements in neurorehabilitation research.Research related to neurorehabilitation is believed to be making steady and significant progress in China.
基金funded with the Key Project of Beijing Municipal Commission of Science and Technology(Z221100007422016)the Joint Project of Beijing Natural Science Foundation(L222107)the Sailing Project of Beijing Hospitals Authority in Clinical Medicine Development(ZLRK202319).
文摘To derive critical signal features from intracranial electroencephalograms of epileptic patients in order to design instructions for feedback-type electrical stimulation systems.The Detrended Fluctuation Analysis(DFA)exponent is chosen as the classification exponent,and the disparities between indicators representing distinct seizure states and the classification efficacy of rudimentary machine learning models are computed.The DFA exponent exhibited a statistically significant variation among the pre-ictal,ictal period,and post-ictal stages.The Linear Discriminant Analysis model demonstrates the highest accuracy among the three basic machine learning models,whereas the Naive Bayesian model necessitates the least amount of computational and storage space.The set of DFA exponents is employed as an intermediary variable in the machine learning process.The resultant model possesses the capability to function as a feedback trigger program for electrical stimulation systems of the feedback variety,specifically within the domain of neural modulation in epilepsy.
基金supported by the National Natural Science Foundation of China(Grant Nos.U20A20191,61727807,82071912,12104049)the Beijing Municipal Science&Technology Commission(Grant No.Z201100007720009)+4 种基金the Fundamental Research Funds for the Central Universities(Grant No.2021CX11011)the China Postdoctoral Science Foundation(Grant No.2020TQ0040)the National Key Research and Development Program of China(Grant No.2020YFC2007305)the BIT Research and Innovation Promoting Project(Grant No.2022YCXZ026)the Ensan Foundation(Grant No.2022026)。
文摘The brain-computer interface(BCI)technology has received lots of attention in the field of scientific research because it can help disabled people improve their quality of life.Steady-state visual evoked potential(SSVEP)is the most researched BCI experimental paradigm,which offers the advantages of high signal-to-noise ratio and short training-time requirement by users.In a complete BCI system,the two most critical components are the experimental paradigm and decoding algorithm.However,a systematic combination of the SSVEP experimental paradigm and decoding algorithms is missing in existing studies.In the present study,the transient visual evoked potential,SSVEP,and various improved SSVEP paradigms are compared and analyzed,and the problems and development bottlenecks in the experimental paradigm are finally pointed out.Subsequently,the canonical correlation analysis and various improved decoding algorithms are introduced,and the opportunities and challenges of the SSVEP decoding algorithm are discussed.
文摘In recent years,Brain-Computer Interface(BCI)system gained much popularity since it aims at establishing the communication between human brain and computer.BCI systems are applied in several research areas such as neuro-rehabilitation,robots,exoeskeletons,etc.Electroencephalography(EEG)is a technique commonly applied in capturing brain signals.It is incorporated in BCI systems since it has attractive features such as noninvasive nature,high time-resolution output,mobility and cost-effective.EEG classification process is highly essential in decision making process and it incorporates different processes namely,feature extraction,feature selection,and classification.With this motivation,the current research paper presents an Intelligent Optimal Fuzzy Support Vector Machine-based EEC recognition(IOFSVM-EEG)model for BCI system.Independent Component Analysis(ICA)technique is applied onto the proposed IOFSVM-EEG model to remove the artefacts that exist in EEG signal and to retain the meaningful EEG information.Besides,Common Spatial Pattern(CSP)-based feature extraction technique is utilized to derive a helpful set of feature vectors from the preprocessed EEG signals.Moreover,OFSVM method is applied in the classification of EEG signals,in which the parameters involved in FSVM are optimally tuned using Grasshopper Optimization Algorithm(GOA).In order to validate the enhanced EEG recognition outcomes of the proposed IOFSVM-EEG model,an extensive set of experiments was conducted.The outcomes were examined under distinct aspects.The experimental results highlighted the enhanced performance of the presented IOFSVM-EEG model over other state-of-the-art methods.
基金supported by the Shandong Province Nature Science Foundation(Grant No.ZR2010CM055)Science Development Plan Project(Grant No.2010GGX10133)
文摘To collect neuronal activity data from awake, freely behaving animals, we developed miniature telemetry recording system. The integrated system consists of four major components： l） Microelectrodes and micro-driver assembly, 2） analog front end （AFE）, 3） programmable system on chip （PSoC）, and 4） ra- dio transceiver and the LabVIEW were used as a platform for the graphic user interface. The result showed the system was able to record and analyze neuronal recordings in freely moving animals and lasted continuously for a time period of a week or more. This is very useful for the study of the interdisciplinary research of neu- roscience and information engineering techniques. The circuits and architecture of the devices can be adapted for neurobiology and research with other small animals.
基金the National Natural Science Foundation of China(No.61672070,62173010)the Beijing Municipal Natural Science Foundation(No.4192005,4202025)+1 种基金the Beijing Municipal Education Commission Project(No.KM201910005008,KM201911232003)the Beijing Innovation Center for Future Chips(No.KYJJ2018004).
文摘Kernel adaptive algorithm is an extension of adaptive algorithm in nonlinear,and widely used in the field of non-stationary signal processing.But the distribution of classic data sets seems relatively regular and simple in time series.The distribution of the electroencephalograph(EEG)signal is more randomness and non-stationarity,so online prediction of EEG signal can further verify the robustness and applicability of kernel adaptive algorithms.What’s more,the purpose of modeling and analyzing the time series of EEG signals is to discover and extract valuable information,and to reveal the internal relations of EEG signals.The time series prediction of EEG plays an important role in EEG time series analysis.In this paper,kernel RLS tracker(KRLST)is presented to online predict the EEG signals of motor imagery and compared with other 13 kernel adaptive algorithms.The experimental results show that KRLST algorithm has the best effect on the brain computer interface(BCI)dataset.
