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
Brain-Computer interfacing(BCI)has currently added a new dimension in assistive robotics.Existing braincomputer interfaces designed for position control applications suffer from two fundamental limitations.First,most ...Brain-Computer interfacing(BCI)has currently added a new dimension in assistive robotics.Existing braincomputer interfaces designed for position control applications suffer from two fundamental limitations.First,most of the existing schemes employ open-loop control,and thus are unable to track positional errors,resulting in failures in taking necessary online corrective actions.There are examples of a few works dealing with closed-loop electroencephalography(EEG)-based position control.These existing closed-loop brain-induced position control schemes employ a fixed order link selection rule,which often creates a bottleneck preventing time-efficient control.Second,the existing brain-induced position controllers are designed to generate a position response like a traditional firstorder system,resulting in a large steady-state error.This paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential(SSVEP)induced linkselection in an arbitrary order as required for efficient control and generating a second-order response of the position-control system with gradually diminishing overshoots/undershoots to reduce steady-state errors.Other than the above,the third innovation is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin using speed reversal at the zero-crossings of positional errors.Experiments undertaken reveal that the steady-state error is reduced to 0.2%.The paper also provides a thorough analysis of the stability of the closed-loop system performance using the Root Locus technique.展开更多
A brain-computer interface(BCI)-based electric wheelchair control system was developed, which enables the users to move the wheelchair forward or backward, and turn left or right without any pre-learning. This control...A brain-computer interface(BCI)-based electric wheelchair control system was developed, which enables the users to move the wheelchair forward or backward, and turn left or right without any pre-learning. This control system makes use of the amplitude enhancement of alpha-wave blocking in electroencephalogram(EEG) when eyes close for more than 1 s to constitute a BCI for the switch control of wheelchair movements. The system was formed by BCI control panel, data acquisition, signal processing unit and interface control circuit. Eight volunteers participated in the wheelchair control experiments according to the preset routes. The experimental results show that the mean success control rate of all the subjects was 81.3%, with the highest reaching 93.7%. When one subject's triggering time was 2.8 s, i.e., the flashing time of each cycle light was 2.8 s, the average information transfer rate was 8.10 bit/min, with the highest reaching 12.54 bit/min.展开更多
In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in...In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI.展开更多
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 interfaces(BCI)use neural activity as a control signal to enable direct communication between the human brain and external devices.The electrical signals generated by the brain are captured through elec...Brain-computer interfaces(BCI)use neural activity as a control signal to enable direct communication between the human brain and external devices.The electrical signals generated by the brain are captured through electroencephalogram(EEG)and translated into neural intentions reflecting the user’s behavior.Correct decoding of the neural intentions then facilitates the control of external devices.Reinforcement learning-based BCIs enhance decoders to complete tasks based only on feedback signals(rewards)from the environment,building a general framework for dynamic mapping from neural intentions to actions that adapt to changing environments.However,using traditional reinforcement learning methods can have challenges such as the curse of dimensionality and poor generalization.Therefore,in this paper,we use deep reinforcement learning to construct decoders for the correct decoding of EEG signals,demonstrate its feasibility through experiments,and demonstrate its stronger generalization on motion imaging(MI)EEG data signals with high dynamic characteristics.展开更多
The tactile P300 brain-computer interface( BCI) is related to the somatosensory perception and response of the human brain,and is different from visual or audio BCIs. Recently,several studies focused on the tactile st...The tactile P300 brain-computer interface( BCI) is related to the somatosensory perception and response of the human brain,and is different from visual or audio BCIs. Recently,several studies focused on the tactile stimuli delivered to different parts of the human body. Most of these stimuli were symmetrically bilateral.Only a fewstudies explored the influence of tactile stimuli laterality.In the current study,we extensively tested the performance of a vibrotactile BCI system using ipsilateral stimuli and bilateral stimuli.Two vibrotactile P300-based paradigms were tested. The target stimuli were located on the left and right forearms for the left forearm and right forearm( LFRF) paradigm,and on the left forearm and calf for the left forearm and left calf( LFLC)paradigm. Ten healthy subjects participated in this study. Our experiments and analysis showed that the bilateral paradigm( LFRF) elicited larger P300 amplitude and achieved significantly higher classification accuracy than the ipsilateral paradigm( LFLC). However, both paradigms achieved classification accuracies higher than 70% after the completion of several trials on average,which was usually regarded as the minimum accuracy level required for BCI system to be deemed useful.展开更多
As a non-invasive neurophysiologieal index for brain-computer interface(BCI),electroencephalogram(EEG)attracts much attention at present.In order to have a portable BCI,a simple and efficient pre-amplifier is crucial ...As a non-invasive neurophysiologieal index for brain-computer interface(BCI),electroencephalogram(EEG)attracts much attention at present.In order to have a portable BCI,a simple and efficient pre-amplifier is crucial in practice.In this work,a preamplifier based on the characteristics of EEG signals is designed,which consists of a highly symmetrical input stage,low-pass filter,50 Hz notch filter and a post amplifier.A prototype of this EEG module is fabricated and EEG data are obtained through an actual experiment.The results demonstrate that the EEG preamplifier will be a promising unit for BCI in the future.展开更多
Transfer learning,as a new machine learning methodology,may solve problems in related but different domains by using existing knowledge,and it is often applied to transfer training data from another domain for model t...Transfer learning,as a new machine learning methodology,may solve problems in related but different domains by using existing knowledge,and it is often applied to transfer training data from another domain for model training in the case of insuficient training data.In recent years,an increasing number of researchers who engage in brain-computer interface(BCI),have focused on using transfer learning to make most of the available electroencephalogram data from different subjects,effectively reducing the cost of expensive data acquisition and labeling as well as greatly improving the learning performance of the model.This paper surveys the development of transfer learning and reviews the transfer learning approaches in BCI.In addition,according to the"what to transfer"question in transfer learning,this review is organized into three contexts:instance-based transfer learning,parameter-based transfer learning,and feature-based transfer learning.Furthermore,the current transfer learning applications in BCI research are summarized in terms of the transfer learning methods,datasets,evaluation performance,etc.At the end of the paper,the questions to be solved in future research are put forward,laying the foundation for the popularization and in-depth research of transfer learning in BCI.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
Previous efforts to boost the performance of brain-computer interfaces (BCIs) have predominantly focused on optimizing algorithms for decoding brain signals. However, the untapped potential of leveraging brain plastic...Previous efforts to boost the performance of brain-computer interfaces (BCIs) have predominantly focused on optimizing algorithms for decoding brain signals. However, the untapped potential of leveraging brain plasticity for optimization remains underexplored. In this study, we enhanced the temporal resolution of the human brain in discriminating visual stimuli by eliminating the attentional blink (AB) through color-salient cognitive training, and we confirmed that the mechanism was an attention-based improvement. Using the rapid serial visual presentation (RSVP)-based BCI, we evaluated the behavioral and electroencephalogram (EEG) decoding performance of subjects before and after cognitive training in high target percentage (with AB) and low target percentage (without AB) surveillance tasks, respectively. The results consistently demonstrated significant improvements in the trained subjects. Further analysis indicated that this improvement was attributed to the cognitively trained brain producing more discriminative EEG. Our work highlights the feasibility of cognitive training as a means of brain enhancement to boost BCI performance.展开更多
The control of a high Degree of Freedom(DoF) robot to grasp a target in three-dimensional space using Brain-Computer Interface(BCI) remains a very difficult problem to solve. Design of synchronous BCI requires the use...The control of a high Degree of Freedom(DoF) robot to grasp a target in three-dimensional space using Brain-Computer Interface(BCI) remains a very difficult problem to solve. Design of synchronous BCI requires the user perform the brain activity task all the time according to the predefined paradigm; such a process is boring and fatiguing. Furthermore, the strategy of switching between robotic auto-control and BCI control is not very reliable because the accuracy of Motor Imagery(MI) pattern recognition rarely reaches 100%. In this paper, an asynchronous BCI shared control method is proposed for the high DoF robotic grasping task. The proposed method combines BCI control and automatic robotic control to simultaneously consider the robotic vision feedback and revise the unreasonable control commands. The user can easily mentally control the system and is only required to intervene and send brain commands to the automatic control system at the appropriate time according to the experience of the user. Two experiments are designed to validate our method: one aims to illustrate the accuracy of MI pattern recognition of our asynchronous BCI system; the other is the online practical experiment that controls the robot to grasp a target while avoiding an obstacle using the asynchronous BCI shared control method that can improve the safety and robustness of our system.展开更多
Brain-computer interface (BCI) is a communication system that can help lock-in patients to interact with the outside environment by translating brain signals into machine commands.The present work provides a design fo...Brain-computer interface (BCI) is a communication system that can help lock-in patients to interact with the outside environment by translating brain signals into machine commands.The present work provides a design for a virtual reality (VR) based BCI system that allows human participants to control a virtual hand to make gestures by P300 signals,with a positive peak of potential about 300 ms posterior to the onset of target stimulus.In this virtual environment,the participants can obtain a more immersed experience with the BCI system,such as controlling a virtual hand or walking around in the virtual world.Methods of modeling the virtual hand and analyzing the P300 signals are also described in detail.Template matching and support vector machine were used as the P300 classifier and the experiment results showed that both algorithms perform well in the system.After a short time of practice,most participants could learn to control the virtual hand during the online experiment with greater than 70% accuracy.展开更多
This paper presents a hybrid brain-computer interface (BCI) control strategy,the goal of which is to expand control functions of a conventional motor imagery or a P300 potential based BCI in a virtual environment.The ...This paper presents a hybrid brain-computer interface (BCI) control strategy,the goal of which is to expand control functions of a conventional motor imagery or a P300 potential based BCI in a virtual environment.The hybrid control strategy utilizes P300 potential to control virtual devices and motor imagery related sensorimotor rhythms to navigate in the virtual world.The two electroencephalography (EEG) patterns serve as source signals for different control functions in their corresponding system states,and state switch is achieved in a sequential manner.In the current system,imagination of left/right hand movement was translated into turning left/right in the virtual apartment continuously,while P300 potentials were mapped to discrete virtual device control commands using a five-oddball paradigm.The combination of motor imagery and P300 patterns in one BCI system for virtual environment control was tested and the results were compared with those of a single motor imagery or P300-based BCI.Subjects obtained similar performances in the hybrid and single control tasks,which indicates the hybrid control strategy works well in the virtual environment.展开更多
Ocular artifacts cause the main interfering signals within electroencephalogram(EEG)signal measurements.An adaptive filter based on reference signals from an electrooculogram(EOG)can reduce ocular interference,but col...Ocular artifacts cause the main interfering signals within electroencephalogram(EEG)signal measurements.An adaptive filter based on reference signals from an electrooculogram(EOG)can reduce ocular interference,but collecting EOG signals during a long-term EEG recording is inconvenient and uncomfortable for the subject.To remove ocular artifacts from EEG in brain-computer interfaces(BCIs),a method named spatial constraint independent component analysis based recursive least squares(SCICA-RLS)is proposed.The method consists of two stages.In the first stage,independent component analysis(ICA)is used to decompose multiple EEG channels into an equal number of independent components(ICs).Ocular ICs are identified by an automatic artifact detection method based on kurtosis.Then empirical mode decomposition(EMD)is employed to remove any cerebral activity from the identified ocular ICs to obtain exact altifact ICs.In the second stage,first,SCICA applies exact artifact ICs obtained in the first stage as a constraint to extract artifact ICs from the given EEG signal.These extracted ICs are called spatial constraint ICs(SC-ICs).Then the RLS based adaptive filter uses SC-ICs as reference signals to reduce interference,which avoids the need for parallel EOG recordings.In addition,the proposed method has the ability of fast computation as it is not necessary for SCICA to identify all ICs like ICA.Based on the EEG data recorded from seven subjects,the new approach can lead to average classification accuracies of 3.3%and 12.6%higher than those of the standard ICA and raw EEG,respectively.In addition,the proposed method has 83.5%and 83.8%reduction in time-consumption compared with the standard ICA and ICA-RLS,respectively,which demonstrates a better and faster OA reduction.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
Patients who suffer from a high spinal cord injury have severe motor disabilities in the lower as well as in the upper extremities. Thus they rely on the help of other people in everyday life. Restoring the function o...Patients who suffer from a high spinal cord injury have severe motor disabilities in the lower as well as in the upper extremities. Thus they rely on the help of other people in everyday life. Restoring the function of the upper limbs, especially the grasp function can help them to gain some independence. Using EEG-based neuroprosthetics is a way to help tetraplegic people restore different grasp types as well as moving the arm and the elbow. In this work an overview of non-invasive EEG-based methods for restoring the hand and arm function with the use of neuroprosthetics in individuals with high spinal cord injury is given. Since the Graz BCI group is leading in this area of non-invasive research mainly, the work of this group is represented.展开更多
Motor imagery brain–computer interfaces(MI-BCIs)have great potential value in prosthetics control,neurorehabilitation,and gaming;however,currently,most such systems only operate in controlled laboratory environments....Motor imagery brain–computer interfaces(MI-BCIs)have great potential value in prosthetics control,neurorehabilitation,and gaming;however,currently,most such systems only operate in controlled laboratory environments.One of the most important obstacles is the MI-BCI inefficiency phenomenon.The accuracy of MI-BCI control varies significantly(from chance level to 100%accuracy)across subjects due to the not easily induced and unstable MI-related EEG features.An MI-BCI inefficient subject is defined as a subject who cannot achieve greater than 70%accuracy after sufficient training time,and multiple survey results indicate that inefficient subjects account for 10%–50%of the experimental population.The widespread use of MI-BCI has been seriously limited due to these large percentages of inefficient subjects.In this review,we summarize recent findings of the cause of MI-BCI inefficiency from resting-state brain function,task-related brain activity,brain structure,and psychological perspectives.These factors help understand the reasons for inter-subject MI-BCI control performance variability,and it can be concluded that the lower resting-state sensorimotor rhythm(SMR)is the key factor in MI-BCI inefficiency,which has been confirmed by multiple independent laboratories.We then propose to divide MI-BCI inefficient subjects into three categories according to the resting-state SMR and offline/online accuracy to apply more accurate approaches to solve the inefficiency problem.The potential solutions include developing transfer learning algorithms,new experimental paradigms,mindfulness meditation practice,novel training strategies,and identifying new motor imagery-related EEG features.To date,few studies have focused on improving the control accuracy of MI-BCI inefficient subjects;thus,we appeal to the BCI community to focus more on this research area.Only by reducing the percentage of inefficient subjects can we create the opportunity to expand the value and influence of MI-BCI.展开更多
Electroencephalogram （EEG） based brain-computer interfaces allow users to communicate with the external environment by means of their EEG signals, without relying on the brain＇s usual output pathways such as muscle...Electroencephalogram （EEG） based brain-computer interfaces allow users to communicate with the external environment by means of their EEG signals, without relying on the brain＇s usual output pathways such as muscles. A popular application for EEGs is the EEG-based speller, which translates EEG signals into intentions to spell particular words, thus benefiting those suffering from severe disabilities, such as amyotrophic lateral sclerosis. Although the EEG-based English speller （EEGES） has been widely studied in recent years, few studies have focused on the EEG-based Chinese speller （EEGCS）. The EEGCS is more difficult to develop than the EEGES, because the English alphabet contains only 26 letters. By contrast, Chinese contains more than 11000 logographic characters. The goal of this paper is to survey the literature on EEGCS systems. First, the taxonomy of current EEGCS systems is discussed to get the gist of the paper. Then, a common framework unifying the current EEGCS and EEGES systems is proposed, in which the concept of EEG-based choice acts as a core component. In addition, a variety of current EEGCS systems are investigated and discussed to highlight the advances, current problems, and future directions for EEGCS.展开更多
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.
文摘Brain-Computer interfacing(BCI)has currently added a new dimension in assistive robotics.Existing braincomputer interfaces designed for position control applications suffer from two fundamental limitations.First,most of the existing schemes employ open-loop control,and thus are unable to track positional errors,resulting in failures in taking necessary online corrective actions.There are examples of a few works dealing with closed-loop electroencephalography(EEG)-based position control.These existing closed-loop brain-induced position control schemes employ a fixed order link selection rule,which often creates a bottleneck preventing time-efficient control.Second,the existing brain-induced position controllers are designed to generate a position response like a traditional firstorder system,resulting in a large steady-state error.This paper overcomes the above two limitations by keeping provisions for steady-state visual evoked potential(SSVEP)induced linkselection in an arbitrary order as required for efficient control and generating a second-order response of the position-control system with gradually diminishing overshoots/undershoots to reduce steady-state errors.Other than the above,the third innovation is to utilize motor imagery and P300 signals to design the hybrid brain-computer interfacing system for the said application with gradually diminishing error-margin using speed reversal at the zero-crossings of positional errors.Experiments undertaken reveal that the steady-state error is reduced to 0.2%.The paper also provides a thorough analysis of the stability of the closed-loop system performance using the Root Locus technique.
基金Supported by the National Natural Science Foundation of China(No.81222021,No.30970875,No.90920015,No.61172008 and No.81171423)National Key Technology Research and Development Program of the Ministry of Science and Technology of China(No.2012BAI34B02)Program for New Century Excellent Talents in University of the Ministry of Education of China(No.NCET-10-0618)
文摘A brain-computer interface(BCI)-based electric wheelchair control system was developed, which enables the users to move the wheelchair forward or backward, and turn left or right without any pre-learning. This control system makes use of the amplitude enhancement of alpha-wave blocking in electroencephalogram(EEG) when eyes close for more than 1 s to constitute a BCI for the switch control of wheelchair movements. The system was formed by BCI control panel, data acquisition, signal processing unit and interface control circuit. Eight volunteers participated in the wheelchair control experiments according to the preset routes. The experimental results show that the mean success control rate of all the subjects was 81.3%, with the highest reaching 93.7%. When one subject's triggering time was 2.8 s, i.e., the flashing time of each cycle light was 2.8 s, the average information transfer rate was 8.10 bit/min, with the highest reaching 12.54 bit/min.
文摘In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI.
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.
文摘Brain-computer interfaces(BCI)use neural activity as a control signal to enable direct communication between the human brain and external devices.The electrical signals generated by the brain are captured through electroencephalogram(EEG)and translated into neural intentions reflecting the user’s behavior.Correct decoding of the neural intentions then facilitates the control of external devices.Reinforcement learning-based BCIs enhance decoders to complete tasks based only on feedback signals(rewards)from the environment,building a general framework for dynamic mapping from neural intentions to actions that adapt to changing environments.However,using traditional reinforcement learning methods can have challenges such as the curse of dimensionality and poor generalization.Therefore,in this paper,we use deep reinforcement learning to construct decoders for the correct decoding of EEG signals,demonstrate its feasibility through experiments,and demonstrate its stronger generalization on motion imaging(MI)EEG data signals with high dynamic characteristics.
基金National Key Research and Development Program,China(No.2017YFB13003002)National Natural Science Foundation of China(Nos.61573142,61773164,91420302)Programme of Introducing Talents of Discipline to Universities(the 111 Project)(No.B17017)
文摘The tactile P300 brain-computer interface( BCI) is related to the somatosensory perception and response of the human brain,and is different from visual or audio BCIs. Recently,several studies focused on the tactile stimuli delivered to different parts of the human body. Most of these stimuli were symmetrically bilateral.Only a fewstudies explored the influence of tactile stimuli laterality.In the current study,we extensively tested the performance of a vibrotactile BCI system using ipsilateral stimuli and bilateral stimuli.Two vibrotactile P300-based paradigms were tested. The target stimuli were located on the left and right forearms for the left forearm and right forearm( LFRF) paradigm,and on the left forearm and calf for the left forearm and left calf( LFLC)paradigm. Ten healthy subjects participated in this study. Our experiments and analysis showed that the bilateral paradigm( LFRF) elicited larger P300 amplitude and achieved significantly higher classification accuracy than the ipsilateral paradigm( LFLC). However, both paradigms achieved classification accuracies higher than 70% after the completion of several trials on average,which was usually regarded as the minimum accuracy level required for BCI system to be deemed useful.
基金supported by the National Natural Science Foundation of China under Grant No.60571019the University of Electronic Science and Technology of China Youth Foundation under Grant No.L08010901JX0772.
文摘As a non-invasive neurophysiologieal index for brain-computer interface(BCI),electroencephalogram(EEG)attracts much attention at present.In order to have a portable BCI,a simple and efficient pre-amplifier is crucial in practice.In this work,a preamplifier based on the characteristics of EEG signals is designed,which consists of a highly symmetrical input stage,low-pass filter,50 Hz notch filter and a post amplifier.A prototype of this EEG module is fabricated and EEG data are obtained through an actual experiment.The results demonstrate that the EEG preamplifier will be a promising unit for BCI in the future.
基金the National Natural Science Foundation of China(Nos.11832003 and 81471770)the Natural Science Foundation of Beijing(No.4182009)。
文摘Transfer learning,as a new machine learning methodology,may solve problems in related but different domains by using existing knowledge,and it is often applied to transfer training data from another domain for model training in the case of insuficient training data.In recent years,an increasing number of researchers who engage in brain-computer interface(BCI),have focused on using transfer learning to make most of the available electroencephalogram data from different subjects,effectively reducing the cost of expensive data acquisition and labeling as well as greatly improving the learning performance of the model.This paper surveys the development of transfer learning and reviews the transfer learning approaches in BCI.In addition,according to the"what to transfer"question in transfer learning,this review is organized into three contexts:instance-based transfer learning,parameter-based transfer learning,and feature-based transfer learning.Furthermore,the current transfer learning applications in BCI research are summarized in terms of the transfer learning methods,datasets,evaluation performance,etc.At the end of the paper,the questions to be solved in future research are put forward,laying the foundation for the popularization and in-depth research of transfer learning in BCI.
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 Natural Science Foundation of China (U19B2030, 61976167, 62301405, 62101416)the Natural Science Basic Research Program of Shaanxi, China (2022JQ-708)Fundamental Research Funds for the Central Universities, China.
文摘Previous efforts to boost the performance of brain-computer interfaces (BCIs) have predominantly focused on optimizing algorithms for decoding brain signals. However, the untapped potential of leveraging brain plasticity for optimization remains underexplored. In this study, we enhanced the temporal resolution of the human brain in discriminating visual stimuli by eliminating the attentional blink (AB) through color-salient cognitive training, and we confirmed that the mechanism was an attention-based improvement. Using the rapid serial visual presentation (RSVP)-based BCI, we evaluated the behavioral and electroencephalogram (EEG) decoding performance of subjects before and after cognitive training in high target percentage (with AB) and low target percentage (without AB) surveillance tasks, respectively. The results consistently demonstrated significant improvements in the trained subjects. Further analysis indicated that this improvement was attributed to the cognitively trained brain producing more discriminative EEG. Our work highlights the feasibility of cognitive training as a means of brain enhancement to boost BCI performance.
基金supported by the National Natural Science Foundation of China (Nos. 91420302 and 91520201)Innovation Cultivating Fund Project 17 163 12 ZT 001 019 01
文摘The control of a high Degree of Freedom(DoF) robot to grasp a target in three-dimensional space using Brain-Computer Interface(BCI) remains a very difficult problem to solve. Design of synchronous BCI requires the user perform the brain activity task all the time according to the predefined paradigm; such a process is boring and fatiguing. Furthermore, the strategy of switching between robotic auto-control and BCI control is not very reliable because the accuracy of Motor Imagery(MI) pattern recognition rarely reaches 100%. In this paper, an asynchronous BCI shared control method is proposed for the high DoF robotic grasping task. The proposed method combines BCI control and automatic robotic control to simultaneously consider the robotic vision feedback and revise the unreasonable control commands. The user can easily mentally control the system and is only required to intervene and send brain commands to the automatic control system at the appropriate time according to the experience of the user. Two experiments are designed to validate our method: one aims to illustrate the accuracy of MI pattern recognition of our asynchronous BCI system; the other is the online practical experiment that controls the robot to grasp a target while avoiding an obstacle using the asynchronous BCI shared control method that can improve the safety and robustness of our system.
基金Project supported by the National Natural Science Foundation of China (No.60873125)the National Institute of Biomedical Imaging and Bioengineering (No.1R03EB008235-01A1)+1 种基金the Shanghai Commission of Science and Technology (No.10440710200)the Fundamental Research Funds for the Central Universities
文摘Brain-computer interface (BCI) is a communication system that can help lock-in patients to interact with the outside environment by translating brain signals into machine commands.The present work provides a design for a virtual reality (VR) based BCI system that allows human participants to control a virtual hand to make gestures by P300 signals,with a positive peak of potential about 300 ms posterior to the onset of target stimulus.In this virtual environment,the participants can obtain a more immersed experience with the BCI system,such as controlling a virtual hand or walking around in the virtual world.Methods of modeling the virtual hand and analyzing the P300 signals are also described in detail.Template matching and support vector machine were used as the P300 classifier and the experiment results showed that both algorithms perform well in the system.After a short time of practice,most participants could learn to control the virtual hand during the online experiment with greater than 70% accuracy.
基金supported by the National Natural Science Foundation of China (Nos.30800287,60703038,60873125,61001172,and 61031002)the Zhejiang Provincial Natural Science Foundation of China (No.Y2090707)the Fundamental Research Funds for the Central Universities of China
文摘This paper presents a hybrid brain-computer interface (BCI) control strategy,the goal of which is to expand control functions of a conventional motor imagery or a P300 potential based BCI in a virtual environment.The hybrid control strategy utilizes P300 potential to control virtual devices and motor imagery related sensorimotor rhythms to navigate in the virtual world.The two electroencephalography (EEG) patterns serve as source signals for different control functions in their corresponding system states,and state switch is achieved in a sequential manner.In the current system,imagination of left/right hand movement was translated into turning left/right in the virtual apartment continuously,while P300 potentials were mapped to discrete virtual device control commands using a five-oddball paradigm.The combination of motor imagery and P300 patterns in one BCI system for virtual environment control was tested and the results were compared with those of a single motor imagery or P300-based BCI.Subjects obtained similar performances in the hybrid and single control tasks,which indicates the hybrid control strategy works well in the virtual environment.
基金Project supported by the National Natural Science Foundation of China(Nos.31100709 and 60975079)the Shanghai Pujiang Program,China(No.14PJ1431300)
文摘Ocular artifacts cause the main interfering signals within electroencephalogram(EEG)signal measurements.An adaptive filter based on reference signals from an electrooculogram(EOG)can reduce ocular interference,but collecting EOG signals during a long-term EEG recording is inconvenient and uncomfortable for the subject.To remove ocular artifacts from EEG in brain-computer interfaces(BCIs),a method named spatial constraint independent component analysis based recursive least squares(SCICA-RLS)is proposed.The method consists of two stages.In the first stage,independent component analysis(ICA)is used to decompose multiple EEG channels into an equal number of independent components(ICs).Ocular ICs are identified by an automatic artifact detection method based on kurtosis.Then empirical mode decomposition(EMD)is employed to remove any cerebral activity from the identified ocular ICs to obtain exact altifact ICs.In the second stage,first,SCICA applies exact artifact ICs obtained in the first stage as a constraint to extract artifact ICs from the given EEG signal.These extracted ICs are called spatial constraint ICs(SC-ICs).Then the RLS based adaptive filter uses SC-ICs as reference signals to reduce interference,which avoids the need for parallel EOG recordings.In addition,the proposed method has the ability of fast computation as it is not necessary for SCICA to identify all ICs like ICA.Based on the EEG data recorded from seven subjects,the new approach can lead to average classification accuracies of 3.3%and 12.6%higher than those of the standard ICA and raw EEG,respectively.In addition,the proposed method has 83.5%and 83.8%reduction in time-consumption compared with the standard ICA and ICA-RLS,respectively,which demonstrates a better and faster OA reduction.
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.
文摘Patients who suffer from a high spinal cord injury have severe motor disabilities in the lower as well as in the upper extremities. Thus they rely on the help of other people in everyday life. Restoring the function of the upper limbs, especially the grasp function can help them to gain some independence. Using EEG-based neuroprosthetics is a way to help tetraplegic people restore different grasp types as well as moving the arm and the elbow. In this work an overview of non-invasive EEG-based methods for restoring the hand and arm function with the use of neuroprosthetics in individuals with high spinal cord injury is given. Since the Graz BCI group is leading in this area of non-invasive research mainly, the work of this group is represented.
基金supported by grants from the National Natural Science Foundation of China(NSFC,Grant No.61603344,No.61961160705,No.#U19A2082)the Key Research Projects of Henan Higher Education Institutions(Project No.16A120008)
文摘Motor imagery brain–computer interfaces(MI-BCIs)have great potential value in prosthetics control,neurorehabilitation,and gaming;however,currently,most such systems only operate in controlled laboratory environments.One of the most important obstacles is the MI-BCI inefficiency phenomenon.The accuracy of MI-BCI control varies significantly(from chance level to 100%accuracy)across subjects due to the not easily induced and unstable MI-related EEG features.An MI-BCI inefficient subject is defined as a subject who cannot achieve greater than 70%accuracy after sufficient training time,and multiple survey results indicate that inefficient subjects account for 10%–50%of the experimental population.The widespread use of MI-BCI has been seriously limited due to these large percentages of inefficient subjects.In this review,we summarize recent findings of the cause of MI-BCI inefficiency from resting-state brain function,task-related brain activity,brain structure,and psychological perspectives.These factors help understand the reasons for inter-subject MI-BCI control performance variability,and it can be concluded that the lower resting-state sensorimotor rhythm(SMR)is the key factor in MI-BCI inefficiency,which has been confirmed by multiple independent laboratories.We then propose to divide MI-BCI inefficient subjects into three categories according to the resting-state SMR and offline/online accuracy to apply more accurate approaches to solve the inefficiency problem.The potential solutions include developing transfer learning algorithms,new experimental paradigms,mindfulness meditation practice,novel training strategies,and identifying new motor imagery-related EEG features.To date,few studies have focused on improving the control accuracy of MI-BCI inefficient subjects;thus,we appeal to the BCI community to focus more on this research area.Only by reducing the percentage of inefficient subjects can we create the opportunity to expand the value and influence of MI-BCI.
基金Project supported by the China Scholarship Council,the National Natural Science Foundation of China(Nos.61673328,61673322,61402386,61305061,61203336,and 61273338)the Fundamental Research Funds for the Central Universities,China(No.20720160126)the National Basic Research Program(973)of China(No.2013CB329502)
文摘Electroencephalogram （EEG） based brain-computer interfaces allow users to communicate with the external environment by means of their EEG signals, without relying on the brain＇s usual output pathways such as muscles. A popular application for EEGs is the EEG-based speller, which translates EEG signals into intentions to spell particular words, thus benefiting those suffering from severe disabilities, such as amyotrophic lateral sclerosis. Although the EEG-based English speller （EEGES） has been widely studied in recent years, few studies have focused on the EEG-based Chinese speller （EEGCS）. The EEGCS is more difficult to develop than the EEGES, because the English alphabet contains only 26 letters. By contrast, Chinese contains more than 11000 logographic characters. The goal of this paper is to survey the literature on EEGCS systems. First, the taxonomy of current EEGCS systems is discussed to get the gist of the paper. Then, a common framework unifying the current EEGCS and EEGES systems is proposed, in which the concept of EEG-based choice acts as a core component. In addition, a variety of current EEGCS systems are investigated and discussed to highlight the advances, current problems, and future directions for EEGCS.
