Brain-computer interface(BCI)technology is rapidly advancing in medical research and application.As an emerging biomedical engineering technology,it has garnered significant attention in the clinical research of brain...Brain-computer interface(BCI)technology is rapidly advancing in medical research and application.As an emerging biomedical engineering technology,it has garnered significant attention in the clinical research of brain disease diagnosis and treatment,neurological rehabilitation,and mental health.However,BCI also raises several challenges and ethical concerns in clinical research.In this article,the authors investigate and discuss three aspects of BCI in medicine and healthcare:the state of international ethical governance,multidimensional ethical challenges pertaining to BCI in clinical research,and suggestive concerns for ethical review.Despite the great potential of frontier BCI research and development in the field of medical care,the ethical challenges induced by itself and the complexities of clinical research and brain function have put forward new special fields for ethics in BCI.To ensure"responsible innovation"in BCI research in healthcare and medicine,the creation of an ethical global governance framework and system,along with special guidelines for cutting-edge BCI research in medicine,is suggested.展开更多
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
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
The awareness detection in patients with disorders of consciousness currently relies on behavioral observations and CRS-R tests,however,the misdiagnosis rates have been relatively high.In this study,we applied brain-c...The awareness detection in patients with disorders of consciousness currently relies on behavioral observations and CRS-R tests,however,the misdiagnosis rates have been relatively high.In this study,we applied brain-computer interface(BCI)to awareness detection with a passive auditory stimulation paradigm.12 subjects with normal hearing were invited to collect electroencephalogram(EEG)based on a BCI communication system,in which EEG signals are transmitted wirelessly.After necessary preprocessing,RBF-SVM and EEGNet were used for algorithm realization and analysis.For a single subject,RBF-SVM can distinguish his(her)name stimuli awareness with classification accuracies ranging from 60-95%.EEGNet was used to learn all subjects'data and improved accuracy to 78.04%for characteristics finding and model generalization.Moreover,we completed the supplementary analysis work from the time domain and time-frequency domain.This study applied BCI communication to human awareness detection,proposed a passive auditory paradigm,and proved the effectiveness,which could be an inspiration for brain,mental or physical diseases diagnosis and detection.展开更多
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
Abstract-Common spatial pattern （CSP） algorithm is a successful tool in feature estimate of brain-computer interface （BCI）. However, CSP is sensitive to outlier and may result in poor outcomes since it is based on...Abstract-Common spatial pattern （CSP） algorithm is a successful tool in feature estimate of brain-computer interface （BCI）. However, CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials. In this paper, we propose a simple yet effective approach, named common spatial pattern ensemble （CSPE） classifier, to improve CSP performance. Through division of recording channels, multiple CSP filters are constructed. By projection, log-operation, and subtraction on the original signal, an ensemble classifier, majority voting, is achieved and outlier contaminations are alleviated. Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.展开更多
Abstract-Two probabilistic methods are extended to research multi-class motor imagery of brain-computer interface （BCI）： support vector machine （SVM） with posteriori probability （PSVM） and Bayesian linear discr...Abstract-Two probabilistic methods are extended to research multi-class motor imagery of brain-computer interface （BCI）： support vector machine （SVM） with posteriori probability （PSVM） and Bayesian linear discriminant analysis with probabilistic output （PBLDA）. A comparative evaluation of these two methods is conducted. The results shows that： 1） probabilistie information can improve the performance of BCI for subjects with high kappa coefficient, and 2） PSVM usually results in a stable kappa coefficient whereas PBLDA is more efficient in estimating the model parameters.展开更多
The present study utilized motor imaginary-based brain-computer interface technology combined with rehabilitation training in 20 stroke patients. Results from the Berg Balance Scale and the Holden Walking Classificati...The present study utilized motor imaginary-based brain-computer interface technology combined with rehabilitation training in 20 stroke patients. Results from the Berg Balance Scale and the Holden Walking Classification were significantly greater at 4 weeks after treatment （P 〈 0.01）, which suggested that motor imaginary-based brain-computer interface technology improved balance and walking in stroke patients.展开更多
The non-stationary of the motor imagery electroencephalography(MI-EEG)signal is one of the main limitations for the development of motor imagery brain-computer interfaces(MI-BCI).The nonstationary of the MI-EEG signal...The non-stationary of the motor imagery electroencephalography(MI-EEG)signal is one of the main limitations for the development of motor imagery brain-computer interfaces(MI-BCI).The nonstationary of the MI-EEG signal and the changes of the experimental environment make the feature distribution of the testing set and training set deviates,which reduces the classification accuracy of MI-BCI.In this paper,we propose a Kullback–Leibler divergence(KL)-based transfer learning algorithm to solve the problem of feature transfer,the proposed algorithm uses KL to measure the similarity between the training set and the testing set,adds support vector machine(SVM)classification probability to classify and weight the covariance,and discards the poorly performing samples.The results show that the proposed algorithm can significantly improve the classification accuracy of the testing set compared with the traditional algorithms,especially for subjects with medium classification accuracy.Moreover,the algorithm based on transfer learning has the potential to improve the consistency of feature distribution that the traditional algorithms do not have,which is significant for the application of MI-BCI.展开更多
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
The Brain-Computer Interfaces(BCIs)had been proposed and used in therapeutics for decades.However,the need of time-consuming calibration phase and the lack of robustness,which are caused by little-labeled data,are res...The Brain-Computer Interfaces(BCIs)had been proposed and used in therapeutics for decades.However,the need of time-consuming calibration phase and the lack of robustness,which are caused by little-labeled data,are restricting the advance and application of BCI,especially for the BCI based on motor imagery(MI).In this paper,we reviewed the recent development in the machine learning algorithm used in the MI-based BCI,which may provide potential solutions for addressing the issue.We classified these algorithms into two categories,namely,and enhancing the representation and expanding the training set.Specifically,these methods of enhancing the representation of features collected from few EEG trials are based on extracting features of multiple bands,regularization,and so on.The methods of expanding the training dataset include approaches of transfer learning(session to session transfer,subject to subject transfer)and generating artificial EEG data.The result of these techniques showed the resolution of the challenges to some extent.As a developing research area,the study of BCI algorithms in little-labeled data is increasingly requiring the advancement of human brain physiological structure research and more transfer learning algorithms research.展开更多
In this paper, we proposed a new concept: depth of drowsiness, which can more precisely describe the drowsiness than existing binary description. A set of effective markers for drowsiness: normalized band norm was suc...In this paper, we proposed a new concept: depth of drowsiness, which can more precisely describe the drowsiness than existing binary description. A set of effective markers for drowsiness: normalized band norm was successfully developed. These markers are invariant from voltage amplitude of brain waves, eliminating the need for calibrating the voltage output of the brain-computer interface devices. A new polling algorithm was designed and implemented for computing the depth of drowsiness. The time cost of data acquisition and processing for each estimate is about one second, which is well suited for real-time applications. Test results with a portable brain-computer interface device show that the depth of drowsiness computed by the method in this paper is generally invariant from ages of test subjects and sensor channels (P3 and C4). The comparison between experiment and computing results indicate that the new method is noticeably better than one of the recent methods in terms of accuracy for predicting the drowsiness.展开更多
R. Penrose and S. Hameroff have proposed an idea that the brain can attain high efficient quantum computation by functioning of microtubular structure of neurons in the cytoskelton of biological cells, including neuro...R. Penrose and S. Hameroff have proposed an idea that the brain can attain high efficient quantum computation by functioning of microtubular structure of neurons in the cytoskelton of biological cells, including neurons of the brain. But Tegmark estimated the duration of coherence of a quantum state in a warm wet brain to be on the order of 10>–13 </supseconds, which is far smaller than the one tenth of a second associated with consciousness. Contrary to his calculation, it can be shown that the microtubule in a biological brain can perform computation satisfying the time scale required for quantum computation to achieve large quantum bits calculation compared with the conventional silicon processors even at the room temperature from the assumption that tunneling photons are superluminal particles called tachyons. According to the non-local property of tachyons, it is considered that the tachyon field created inside the brain has the capability to exert an influence around the space outside the brain and it functions as a macroscopic quantum dynamical system to meditate the long-range physical correlations with the surrounding world. From standpoint of the brain model based on superluminal tunneling photons, the authors theoretically searched for the possibility to realize the brain-computer interface that allows paralyzed patient to operate computers by their thoughts and they obtained the positive result for its realization from the experiments conducted by using the prototype of a brain-computer interface system.展开更多
We perceive that some Brain-Computer Interface (BCI) researchers believe in totally different origins of invasive and non-invasive electrical BCI signals. Based on available literature we argue, however, that although...We perceive that some Brain-Computer Interface (BCI) researchers believe in totally different origins of invasive and non-invasive electrical BCI signals. Based on available literature we argue, however, that although invasive and non-invasive BCI signals are different, the underlying origin of electrical BCIs signals is the same.展开更多
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 i...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
Accurate classification of EEG left and right hand motor imagery is an important issue in brain-computer interface. Firstly, discrete wavelet transform method was used to decompose the average power of C3 electrode an...Accurate classification of EEG left and right hand motor imagery is an important issue in brain-computer interface. Firstly, discrete wavelet transform method was used to decompose the average power of C3 electrode and C4 electrode in left-right hands imagery movement during some periods of time. The reconstructed signal of approximation coefficient A6 on the 6al level was selected to build up a feature signal. Secondly, the performances by Fisher Linear Discriminant Analysis with two different threshold calculation ways and Support Vector Machine methods were compared. The final classification results showed that false classification rate by Support Vector Machine was lower and gained an ideal classification results.展开更多
Recent advances in neuroelectrode interface materials and modification technologies are reviewed. Brain-computer interface is the new method of human-computer interaction, which not only can realise the exchange of in...Recent advances in neuroelectrode interface materials and modification technologies are reviewed. Brain-computer interface is the new method of human-computer interaction, which not only can realise the exchange of information between the human brain and external devices, but also provides a brand-new means for the diagnosis and treatment of brain-related diseases. The neural electrode interface part of brain-computer interface is an important area for electrical, optical and chemical signal transmission between brain tissue system and external electronic devices, which determines the performance of brain-computer interface. In order to solve the problems of insufficient flexibility, insufficient signal recognition ability and insufficient biocompatibility of traditional rigid electrodes, researchers have carried out extensive studies on the neuroelectrode interface in terms of materials and modification techniques. This paper introduces the biological reactions that occur in neuroelectrodes after implantation into brain tissue and the decisive role of the electrode interface for electrode function. Following this, the latest research progress on neuroelectrode materials and interface materials is reviewed from the aspects of neuroelectrode materials and modification technologies, firstly taking materials as a clue, and then focusing on the preparation process of neuroelectrode coatings and the design scheme of functionalised structures.展开更多
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
基金supported by the Ministry of Science and Tech-nology of the People's Republic of China(2021ZD0201900),Project 5(2021ZD0201905).
文摘Brain-computer interface(BCI)technology is rapidly advancing in medical research and application.As an emerging biomedical engineering technology,it has garnered significant attention in the clinical research of brain disease diagnosis and treatment,neurological rehabilitation,and mental health.However,BCI also raises several challenges and ethical concerns in clinical research.In this article,the authors investigate and discuss three aspects of BCI in medicine and healthcare:the state of international ethical governance,multidimensional ethical challenges pertaining to BCI in clinical research,and suggestive concerns for ethical review.Despite the great potential of frontier BCI research and development in the field of medical care,the ethical challenges induced by itself and the complexities of clinical research and brain function have put forward new special fields for ethics in BCI.To ensure"responsible innovation"in BCI research in healthcare and medicine,the creation of an ethical global governance framework and system,along with special guidelines for cutting-edge BCI research in medicine,is suggested.
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.
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.
基金supported by the Science and Technology Commission of Shanghai Municipality(STCSM)Research Fund(21JC1405300)to Fan Minthe National Key Research and Development Program of China(2018YFC0831102)sponsored by the Shanghai Key Research Laboratory of NSAI。
文摘The awareness detection in patients with disorders of consciousness currently relies on behavioral observations and CRS-R tests,however,the misdiagnosis rates have been relatively high.In this study,we applied brain-computer interface(BCI)to awareness detection with a passive auditory stimulation paradigm.12 subjects with normal hearing were invited to collect electroencephalogram(EEG)based on a BCI communication system,in which EEG signals are transmitted wirelessly.After necessary preprocessing,RBF-SVM and EEGNet were used for algorithm realization and analysis.For a single subject,RBF-SVM can distinguish his(her)name stimuli awareness with classification accuracies ranging from 60-95%.EEGNet was used to learn all subjects'data and improved accuracy to 78.04%for characteristics finding and model generalization.Moreover,we completed the supplementary analysis work from the time domain and time-frequency domain.This study applied BCI communication to human awareness detection,proposed a passive auditory paradigm,and proved the effectiveness,which could be an inspiration for brain,mental or physical diseases diagnosis and detection.
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.
基金supported by the National Natural Science Foundation of China under Grant No. 30525030, 60701015, and 60736029.
文摘Abstract-Common spatial pattern （CSP） algorithm is a successful tool in feature estimate of brain-computer interface （BCI）. However, CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials. In this paper, we propose a simple yet effective approach, named common spatial pattern ensemble （CSPE） classifier, to improve CSP performance. Through division of recording channels, multiple CSP filters are constructed. By projection, log-operation, and subtraction on the original signal, an ensemble classifier, majority voting, is achieved and outlier contaminations are alleviated. Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.
基金supported by the National Natural Science Foundation of China under Grant No. 30525030, 60701015, and 60736029.
文摘Abstract-Two probabilistic methods are extended to research multi-class motor imagery of brain-computer interface （BCI）： support vector machine （SVM） with posteriori probability （PSVM） and Bayesian linear discriminant analysis with probabilistic output （PBLDA）. A comparative evaluation of these two methods is conducted. The results shows that： 1） probabilistie information can improve the performance of BCI for subjects with high kappa coefficient, and 2） PSVM usually results in a stable kappa coefficient whereas PBLDA is more efficient in estimating the model parameters.
基金the National Natural Science Foundation of China,No.60970062the Shanghai Pujiang Program,No.09PJ1410200
文摘The present study utilized motor imaginary-based brain-computer interface technology combined with rehabilitation training in 20 stroke patients. Results from the Berg Balance Scale and the Holden Walking Classification were significantly greater at 4 weeks after treatment （P 〈 0.01）, which suggested that motor imaginary-based brain-computer interface technology improved balance and walking in stroke patients.
文摘The non-stationary of the motor imagery electroencephalography(MI-EEG)signal is one of the main limitations for the development of motor imagery brain-computer interfaces(MI-BCI).The nonstationary of the MI-EEG signal and the changes of the experimental environment make the feature distribution of the testing set and training set deviates,which reduces the classification accuracy of MI-BCI.In this paper,we propose a Kullback–Leibler divergence(KL)-based transfer learning algorithm to solve the problem of feature transfer,the proposed algorithm uses KL to measure the similarity between the training set and the testing set,adds support vector machine(SVM)classification probability to classify and weight the covariance,and discards the poorly performing samples.The results show that the proposed algorithm can significantly improve the classification accuracy of the testing set compared with the traditional algorithms,especially for subjects with medium classification accuracy.Moreover,the algorithm based on transfer learning has the potential to improve the consistency of feature distribution that the traditional algorithms do not have,which is significant for the application of MI-BCI.
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.
文摘The Brain-Computer Interfaces(BCIs)had been proposed and used in therapeutics for decades.However,the need of time-consuming calibration phase and the lack of robustness,which are caused by little-labeled data,are restricting the advance and application of BCI,especially for the BCI based on motor imagery(MI).In this paper,we reviewed the recent development in the machine learning algorithm used in the MI-based BCI,which may provide potential solutions for addressing the issue.We classified these algorithms into two categories,namely,and enhancing the representation and expanding the training set.Specifically,these methods of enhancing the representation of features collected from few EEG trials are based on extracting features of multiple bands,regularization,and so on.The methods of expanding the training dataset include approaches of transfer learning(session to session transfer,subject to subject transfer)and generating artificial EEG data.The result of these techniques showed the resolution of the challenges to some extent.As a developing research area,the study of BCI algorithms in little-labeled data is increasingly requiring the advancement of human brain physiological structure research and more transfer learning algorithms research.
文摘In this paper, we proposed a new concept: depth of drowsiness, which can more precisely describe the drowsiness than existing binary description. A set of effective markers for drowsiness: normalized band norm was successfully developed. These markers are invariant from voltage amplitude of brain waves, eliminating the need for calibrating the voltage output of the brain-computer interface devices. A new polling algorithm was designed and implemented for computing the depth of drowsiness. The time cost of data acquisition and processing for each estimate is about one second, which is well suited for real-time applications. Test results with a portable brain-computer interface device show that the depth of drowsiness computed by the method in this paper is generally invariant from ages of test subjects and sensor channels (P3 and C4). The comparison between experiment and computing results indicate that the new method is noticeably better than one of the recent methods in terms of accuracy for predicting the drowsiness.
文摘R. Penrose and S. Hameroff have proposed an idea that the brain can attain high efficient quantum computation by functioning of microtubular structure of neurons in the cytoskelton of biological cells, including neurons of the brain. But Tegmark estimated the duration of coherence of a quantum state in a warm wet brain to be on the order of 10>–13 </supseconds, which is far smaller than the one tenth of a second associated with consciousness. Contrary to his calculation, it can be shown that the microtubule in a biological brain can perform computation satisfying the time scale required for quantum computation to achieve large quantum bits calculation compared with the conventional silicon processors even at the room temperature from the assumption that tunneling photons are superluminal particles called tachyons. According to the non-local property of tachyons, it is considered that the tachyon field created inside the brain has the capability to exert an influence around the space outside the brain and it functions as a macroscopic quantum dynamical system to meditate the long-range physical correlations with the surrounding world. From standpoint of the brain model based on superluminal tunneling photons, the authors theoretically searched for the possibility to realize the brain-computer interface that allows paralyzed patient to operate computers by their thoughts and they obtained the positive result for its realization from the experiments conducted by using the prototype of a brain-computer interface system.
文摘We perceive that some Brain-Computer Interface (BCI) researchers believe in totally different origins of invasive and non-invasive electrical BCI signals. Based on available literature we argue, however, that although invasive and non-invasive BCI signals are different, the underlying origin of electrical BCIs signals is the same.
基金supported by the National 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 Open Project of the State Key Laboratory of Robotics and System (HIT)the State Key Laboratory of Cognitive Neuroscience and Learning+3 种基金Natural Science Fund for Colleges and Universities in Jiangsu Provincegrant number:105TB51003Natural Science Fund in Changzhougrant number:CJ20110023
文摘Accurate classification of EEG left and right hand motor imagery is an important issue in brain-computer interface. Firstly, discrete wavelet transform method was used to decompose the average power of C3 electrode and C4 electrode in left-right hands imagery movement during some periods of time. The reconstructed signal of approximation coefficient A6 on the 6al level was selected to build up a feature signal. Secondly, the performances by Fisher Linear Discriminant Analysis with two different threshold calculation ways and Support Vector Machine methods were compared. The final classification results showed that false classification rate by Support Vector Machine was lower and gained an ideal classification results.
基金the National Key Research and Development Program,No.2021YFB3800800the National Natural Science Foundation of China,Nos.31922041,32171341,32301113,the 111 Project,No.B14018+3 种基金the Science and Technology Innovation Project and Excellent Academic Leader Project of Shanghai Science and Technology Committee,Nos.21S31901500,21XD1421100the National Postdoctoral Program for Innovative Talents,No.BX20230122the Shanghai Sailing Program,No.23YF1409700the China Postdoctoral Science Foundation,No.D100-5R-22114.
文摘Recent advances in neuroelectrode interface materials and modification technologies are reviewed. Brain-computer interface is the new method of human-computer interaction, which not only can realise the exchange of information between the human brain and external devices, but also provides a brand-new means for the diagnosis and treatment of brain-related diseases. The neural electrode interface part of brain-computer interface is an important area for electrical, optical and chemical signal transmission between brain tissue system and external electronic devices, which determines the performance of brain-computer interface. In order to solve the problems of insufficient flexibility, insufficient signal recognition ability and insufficient biocompatibility of traditional rigid electrodes, researchers have carried out extensive studies on the neuroelectrode interface in terms of materials and modification techniques. This paper introduces the biological reactions that occur in neuroelectrodes after implantation into brain tissue and the decisive role of the electrode interface for electrode function. Following this, the latest research progress on neuroelectrode materials and interface materials is reviewed from the aspects of neuroelectrode materials and modification technologies, firstly taking materials as a clue, and then focusing on the preparation process of neuroelectrode coatings and the design scheme of functionalised structures.
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.
