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
Active matter encompasses all systems in which each individual constituent independently dissipates energy in its environment.This definition brings together biological systems such as cellular tissues,bacterial colon...Active matter encompasses all systems in which each individual constituent independently dissipates energy in its environment.This definition brings together biological systems such as cellular tissues,bacterial colonies,cytoskeletal filaments driven by molecular motors and animal groups,as well as collections of inert self-propelled particles such as Janus particles,[1]colloidal rollers[2]or vibrated grains.[3]Because of the local persistent drive,these systems are far from thermal equilibrium and cannot be described in terms of thermodynamic potentials.This leads to surprising physics that defies some of the basic intuitions that we have from passive systems,including longrange order in two dimensions[4]and phase-separation in absence of attractive interactions.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 friction interface matching plays a deterministic role in the motor efficiency,and the microcosmic contact status of friction interface should be investigated to improve the ultrasonic motor performance.The main p...The friction interface matching plays a deterministic role in the motor efficiency,and the microcosmic contact status of friction interface should be investigated to improve the ultrasonic motor performance.The main purpose is to improve the effective output power of ultrasonic motor.Hence,one studies the contact condition of the friction interface of the ultrasonic motor,analyzes the micro condition of contact interface through finite element analysis,optimizes unreasonable structures,and compares the two different-structure ultrasonic motors through experiments.The results reflect the necessity of optimization.After optimization,the stator and rotor deform after pre-pressure and the contact interface of them full contact theoretically.When reaching heat balance the effective output of the motor is 37%,and the average effective output efficiency is 2.384 times higher than that of the unoptimized.It can be seen that the total consumption of the ultrasonic motor system decreases significantly.Therefore,when using in certain system the consumption taken from the system will decreases largely,especially in the system with a strict consumption control.展开更多
A right-hand motor imagery based brain-computer interface is proposed in this work. Such a system requires the identification of different brain states and their classification. Brain signals recorded by electroenceph...A right-hand motor imagery based brain-computer interface is proposed in this work. Such a system requires the identification of different brain states and their classification. Brain signals recorded by electroencephalography are naturally contaminated by various noises and interferences. Ocular artifact removal is performed by implementing an auto-matic method “Kmeans-ICA” which does not require a reference channel. This method starts by decomposing EEG signals into Independent Components;artefactual ones are then identified using Kmeans clustering, a non-supervised machine learning technique. After signal preprocessing, a Brain computer interface system is implemented;physiologically interpretable features extracting the wavelet-coherence, the wavelet-phase locking value and band power are computed and introduced into a statistical test to check for a significant difference between relaxed and motor imagery states. Features which pass the test are conserved and used for classification. Leave One Out Cross Validation is performed to evaluate the performance of the classifier. Two types of classifiers are compared: a Linear Discriminant Analysis and a Support Vector Machine. Using a Linear Discriminant Analysis, classification accuracy improved from 66% to 88.10% after ocular artifacts removal using Kmeans-ICA. The proposed methodology outperformed state of art feature extraction methods, namely, the mu rhythm band power.展开更多
The development of brain-computer interfaces(BCI)based on motor imagery(MI)has greatly improved patients’quality of life with movement disorders.The classification of upper limb MI has been widely studied and applied...The development of brain-computer interfaces(BCI)based on motor imagery(MI)has greatly improved patients’quality of life with movement disorders.The classification of upper limb MI has been widely studied and applied in many fields,including rehabilitation.However,the physiological representations of left and right lower limb movements are too close and activated deep in the cerebral cortex,making it difficult to distinguish their features.Therefore,classifying lower limbs motor imagery is more challenging.In this study,we propose a feature extraction method based on functional connectivity,which utilizes phase-locked values to construct a functional connectivity matrix as the features of the left and right legs,which can effectively avoid the problem of physiological representations of the left and right lower limbs being too close to each other during movement.In addition,considering the topology and the temporal characteristics of the electroencephalogram(EEG),we designed a temporal-spatial convolutional network(TSGCN)to capture the spatiotemporal information for classification.Experimental results show that the accuracy of the proposed method is higher than that of existing methods,achieving an average classification accuracy of 73.58%on the internal dataset.Finally,this study explains the network mechanism of left and right foot MI from the perspective of graph theoretic features and demonstrates the feasibility of decoding lower limb MI.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
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
Electroencephalogram (EEG) is an efficient tool in exploring human brains. It plays a very important role in diagnosis of disorders related to epilepsy and development of new interaction techniques between machines an...Electroencephalogram (EEG) is an efficient tool in exploring human brains. It plays a very important role in diagnosis of disorders related to epilepsy and development of new interaction techniques between machines and human beings,namely,brain-computer interface (BCI). The purpose of this review is to illustrate the recent researches in EEG processing and EEG-based BCI. First,we outline several methods in removing artifacts from EEGs,and classical algorithms for fatigue detection are discussed. Then,two BCI paradigms including motor imagery and steady-state motion visual evoked potentials (SSMVEP) produced by oscillating Newton's rings are introduced. Finally,BCI systems including wheelchair controlling and electronic car navigation are elaborated. As a new technique to control equipments,BCI has promising potential in rehabilitation of disorders in central nervous system,such as stroke and spinal cord injury,treatment of attention deficit hyperactivity disorder (ADHD) in children and development of novel games such as brain-controlled auto racings.展开更多
While brain computer interfaces(BCIs)ofer the potential of allowing those sufering from loss of muscle control to once again fully engage with their environment by bypassing the afected motor system and decoding user ...While brain computer interfaces(BCIs)ofer the potential of allowing those sufering from loss of muscle control to once again fully engage with their environment by bypassing the afected motor system and decoding user intentions directly from brain activity,they are prone to errors.One possible avenue for BCI performance improvement is to detect when the BCI user perceives the BCI to have made an unintended action and thus take corrective actions.Error-related potentials(ErrPs)are neural correlates of error awareness and as such can provide an indication of when a BCI system is not performing according to the user’s intentions.Here,we investigate the brain signals of an implanted BCI user sufering from locked-in syndrome(LIS)due to late-stage ALS that prevents her from being able to speak or move but not from using her BCI at home on a daily basis to communicate,for the presence of error-related signals.We frst establish the presence of an ErrP originating from the dorsolateral pre-frontal cortex(dLPFC)in response to errors made during a discrete feedback task that mimics the click-based spelling software she uses to communicate.Then,we show that this ErrP can also be elicited by cursor movement errors in a continuous BCI cursor control task.This work represents a frst step toward detecting ErrPs during the daily home use of a communications BCI.展开更多
A brain-computer interface (BCI) facilitates bypassing the peripheral nervous system and directly communicating with surrounding devices. Navigation technology using BCI has developed-from exploring the prototype para...A brain-computer interface (BCI) facilitates bypassing the peripheral nervous system and directly communicating with surrounding devices. Navigation technology using BCI has developed-from exploring the prototype paradigm in the virtual environment (VE) to accurately completing the locomotion intention of the operator in the form of a powered wheelchair or mobile robot in a real environment. This paper summarizes BCI navigation applications that have been used in both real and VEs in the past 20 years. Horizontal comparisons were conducted between various paradigms applied to BCI and their unique signal-processing methods. Owing to the shift in the control mode from synchronous to asynchronous, the development trend of navigation applications in the VE was also reviewed. The contrast between high level commands and low-level commands is introduced as the main line to review the two major applications of BCI navigation in real environments: mobile robots and unmanned aerial vehicles (UAVs). Finally, applications of BCI navigation to scenarios outside the laboratory;research challenges, including human factors in navigation application interaction design;and the feasibility of hybrid BCI for BCI navigation are discussed in detail.展开更多
Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive functions.Effective rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recove...Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive functions.Effective rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by artifacts.Accurate classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific variability.To address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and robustness.GACL-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal dynamics.Evaluated on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art methods.Additionally,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive rehabilitation.These findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.展开更多
A brain-computer interface(BCI)based on motor imagery(MI)provides additional control pathways by decoding the intentions of the brain.MI ability has great intra-individual variability,and the majority of MI-BCI system...A brain-computer interface(BCI)based on motor imagery(MI)provides additional control pathways by decoding the intentions of the brain.MI ability has great intra-individual variability,and the majority of MI-BCI systems are unable to adapt to this variability,leading to poor training effects.Therefore,prediction of MI ability is needed.In this study,we propose an MI ability predictor based on multi-frequency EEG features.To validate the performance of the predictor,a video-guided paradigm and a traditional MI paradigm are designed,and the predictor is applied to both paradigms.The results demonstrate that all subjects achieved>85%prediction precision in both applications,with a maximum of 96%.This study indicates that the predictor can accurately predict the individuals’MI ability in different states,provide the scientific basis for personalized training,and enhance the effect of MI-BCI training.展开更多
The classification of motor imagery electroencephalogram(MI-EEG)signals is one of the key challenges in brain-computer interface(BCI)technology.Existing Riemannian geometry-based methods for MI-EEG signal analysis,whi...The classification of motor imagery electroencephalogram(MI-EEG)signals is one of the key challenges in brain-computer interface(BCI)technology.Existing Riemannian geometry-based methods for MI-EEG signal analysis,which rely on a single symmetric positive definite(SPD)manifold,often provide a limited geometric structure,making it difficult to fully capture the complex geometric characteristics of the signals.To address this issue,this paper proposes an innovative classification method for MI-EEG signals based on multi-Riemannian kernel fusion features(MRKFF).This method extends the classical SPD manifold by incorporating the Gaussian SPD manifold and the Grassmann manifold,extracting more discriminative kernel features from these heterogeneous manifolds for fusion-based classification.The proposed method is validated on the OpenBMI binary classification dataset and the BCI Competition IV-2a four-class dataset,achieving average classification accuracies of 75.6%and 71.0%,with Kappa values of 0.50 and 0.61,respectively.The proposed MRKFF method provides a new perspective for the geometric analysis of MI-EEG signals,enabling a deeper understanding and analysis of the complex geometric structure of these signals,thereby achieving more accurate signal classification in BCI applications.展开更多
Deep learning methods have been widely applied in motor imagery(MI)-based brain-computer interfaces(BCI)for decoding electroencephalogram(EEG)signals.High temporal resolution and asymmetric spatial activation are fund...Deep learning methods have been widely applied in motor imagery(MI)-based brain-computer interfaces(BCI)for decoding electroencephalogram(EEG)signals.High temporal resolution and asymmetric spatial activation are fundamental properties of EEG during MI processes.However,due to the limited receptive field of convolutional kernels,traditional convolutional neural networks(CNNs)often focus only on local features,and are insufficient to cover neural processes across different frequency bands and duration scales.This limitation hinders the effective characterization of rhythmic activity changes in MI-EEG signals over time.Additionally,MI-EEG signals exhibit significant asymmetric activation between the left and right hemispheres.Traditional spatial feature extraction methods overlook the interaction between global and local regions at the spatial scale of EEG signals,resulting in inadequate spatial representation and ultimately limiting decoding accuracy.To address these limitations,in this study,a novel deep learning network that integrates multi-modal temporal features with spatially asymmetric feature modeling was proposed.The network first extracts multi-modal temporal information from EEG data channels,and then captures global and hemispheric spatial features in the spatial dimension and fuses them through an advanced fusion layer.Global dependencies are captured using a self-attention module,and a multi-scale convolutional fusion module is introduced to explore the relationships between the two types of temporal features.The fused features are classified through a classification layer to accomplish motor imagery task classification.To mitigate the issue of limited sample size,a data augmentation strategy based on signal segmentation and recombination is designed.Experimental results on the BCI Competition IV-2a(bbic-IV-2a)and BCI Competition IV-2b(bbic-IV-2a)datasets demonstrated that the proposed method achieved superior accuracy in multi-class motor imagery classification compared with existing models.On the BCI-IV-2a dataset,it attained an average classification accuracy of 84.36%,while also showing strong performance on the binary classification BCI-IV-2b dataset.These outcomes validate the capability of the proposed network to enhance MI-EEG classification accuracy.展开更多
背景:近年来随着脑机接口技术的发展,它在脑卒中康复过程中的疗效已得到证实,并取得了丰富成果,亟需进行可视化分析以了解研究前沿与热点。目的:应用文献计量学可视化软件分析脑机接口在脑卒中康复领域应用的前沿热点及研究趋势。方法:...背景:近年来随着脑机接口技术的发展,它在脑卒中康复过程中的疗效已得到证实,并取得了丰富成果,亟需进行可视化分析以了解研究前沿与热点。目的:应用文献计量学可视化软件分析脑机接口在脑卒中康复领域应用的前沿热点及研究趋势。方法:以Web of Science核心合集与中国知网数据库作为研究基础,利用Citespace 6.4.1、VOSviewer 1.6.20和Excel 2021工具对检索所得的与脑机接口技术在脑卒中功能恢复中应用相关的中英文相关文献进行可视化数据分析,通过科学计量手段深入剖析脑机接口技术在脑卒中康复领域的研究现状、热点议题及未来趋势。结果与结论:①共纳入2003-2025年中英文文献985篇(英文879篇,中文106篇),该领域国内外年发文量均持续增长;②中国、美国与德国是该领域年发文量最多的国家;该领域最具影响力的机构为德国图宾根大学,中文发文量最高的机构为复旦大学附属华山医院;瑞士的《FRONTIERS IN NEUROSCIENCE》是英文发文量最高的期刊,《中国康复医学杂志》为中文发文量最高的期刊;英文发文量最高的作者为德国的Birbaumer Niels,中文发文量最高的作者为贾杰;③文献分析可见,国际研究侧重理论与临床效果的验证,且关注上肢功能与神经的恢复;国内研究更关注技术与系统的优化与开发,侧重康复领域应用的广泛探索;④运动想象为中英文文献共同的高频关键词,研究热点聚焦在基于脑电图、运动想象的脑机接口系统开发;⑤多模态结合、人工智能融合、康复手段拓展及国际合作深化可能是该领域未来发展的主要趋势。展开更多
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.
文摘Active matter encompasses all systems in which each individual constituent independently dissipates energy in its environment.This definition brings together biological systems such as cellular tissues,bacterial colonies,cytoskeletal filaments driven by molecular motors and animal groups,as well as collections of inert self-propelled particles such as Janus particles,[1]colloidal rollers[2]or vibrated grains.[3]Because of the local persistent drive,these systems are far from thermal equilibrium and cannot be described in terms of thermodynamic potentials.This leads to surprising physics that defies some of the basic intuitions that we have from passive systems,including longrange order in two dimensions[4]and phase-separation in absence of attractive interactions.
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 friction interface matching plays a deterministic role in the motor efficiency,and the microcosmic contact status of friction interface should be investigated to improve the ultrasonic motor performance.The main purpose is to improve the effective output power of ultrasonic motor.Hence,one studies the contact condition of the friction interface of the ultrasonic motor,analyzes the micro condition of contact interface through finite element analysis,optimizes unreasonable structures,and compares the two different-structure ultrasonic motors through experiments.The results reflect the necessity of optimization.After optimization,the stator and rotor deform after pre-pressure and the contact interface of them full contact theoretically.When reaching heat balance the effective output of the motor is 37%,and the average effective output efficiency is 2.384 times higher than that of the unoptimized.It can be seen that the total consumption of the ultrasonic motor system decreases significantly.Therefore,when using in certain system the consumption taken from the system will decreases largely,especially in the system with a strict consumption control.
文摘A right-hand motor imagery based brain-computer interface is proposed in this work. Such a system requires the identification of different brain states and their classification. Brain signals recorded by electroencephalography are naturally contaminated by various noises and interferences. Ocular artifact removal is performed by implementing an auto-matic method “Kmeans-ICA” which does not require a reference channel. This method starts by decomposing EEG signals into Independent Components;artefactual ones are then identified using Kmeans clustering, a non-supervised machine learning technique. After signal preprocessing, a Brain computer interface system is implemented;physiologically interpretable features extracting the wavelet-coherence, the wavelet-phase locking value and band power are computed and introduced into a statistical test to check for a significant difference between relaxed and motor imagery states. Features which pass the test are conserved and used for classification. Leave One Out Cross Validation is performed to evaluate the performance of the classifier. Two types of classifiers are compared: a Linear Discriminant Analysis and a Support Vector Machine. Using a Linear Discriminant Analysis, classification accuracy improved from 66% to 88.10% after ocular artifacts removal using Kmeans-ICA. The proposed methodology outperformed state of art feature extraction methods, namely, the mu rhythm band power.
基金supported in part by the National Natural Science Foundation of China under Grant 62172368the Natural Science Foundation of Zhejiang Province under Grant LR22F020003.
文摘The development of brain-computer interfaces(BCI)based on motor imagery(MI)has greatly improved patients’quality of life with movement disorders.The classification of upper limb MI has been widely studied and applied in many fields,including rehabilitation.However,the physiological representations of left and right lower limb movements are too close and activated deep in the cerebral cortex,making it difficult to distinguish their features.Therefore,classifying lower limbs motor imagery is more challenging.In this study,we propose a feature extraction method based on functional connectivity,which utilizes phase-locked values to construct a functional connectivity matrix as the features of the left and right legs,which can effectively avoid the problem of physiological representations of the left and right lower limbs being too close to each other during movement.In addition,considering the topology and the temporal characteristics of the electroencephalogram(EEG),we designed a temporal-spatial convolutional network(TSGCN)to capture the spatiotemporal information for classification.Experimental results show that the accuracy of the proposed method is higher than that of existing methods,achieving an average classification accuracy of 73.58%on the internal dataset.Finally,this study explains the network mechanism of left and right foot MI from the perspective of graph theoretic features and demonstrates the feasibility of decoding lower limb MI.
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.
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National Natural Science Foundation of China(No.51005176)Research Fund for the Doctoral Program of Higher Education of China(No.20100201120003)
文摘Electroencephalogram (EEG) is an efficient tool in exploring human brains. It plays a very important role in diagnosis of disorders related to epilepsy and development of new interaction techniques between machines and human beings,namely,brain-computer interface (BCI). The purpose of this review is to illustrate the recent researches in EEG processing and EEG-based BCI. First,we outline several methods in removing artifacts from EEGs,and classical algorithms for fatigue detection are discussed. Then,two BCI paradigms including motor imagery and steady-state motion visual evoked potentials (SSMVEP) produced by oscillating Newton's rings are introduced. Finally,BCI systems including wheelchair controlling and electronic car navigation are elaborated. As a new technique to control equipments,BCI has promising potential in rehabilitation of disorders in central nervous system,such as stroke and spinal cord injury,treatment of attention deficit hyperactivity disorder (ADHD) in children and development of novel games such as brain-controlled auto racings.
文摘While brain computer interfaces(BCIs)ofer the potential of allowing those sufering from loss of muscle control to once again fully engage with their environment by bypassing the afected motor system and decoding user intentions directly from brain activity,they are prone to errors.One possible avenue for BCI performance improvement is to detect when the BCI user perceives the BCI to have made an unintended action and thus take corrective actions.Error-related potentials(ErrPs)are neural correlates of error awareness and as such can provide an indication of when a BCI system is not performing according to the user’s intentions.Here,we investigate the brain signals of an implanted BCI user sufering from locked-in syndrome(LIS)due to late-stage ALS that prevents her from being able to speak or move but not from using her BCI at home on a daily basis to communicate,for the presence of error-related signals.We frst establish the presence of an ErrP originating from the dorsolateral pre-frontal cortex(dLPFC)in response to errors made during a discrete feedback task that mimics the click-based spelling software she uses to communicate.Then,we show that this ErrP can also be elicited by cursor movement errors in a continuous BCI cursor control task.This work represents a frst step toward detecting ErrPs during the daily home use of a communications BCI.
基金Supported by Key-Area Research and Development Program of Guangdong Province (2019B010149001)the National NaturalScience Foundation of China (61960206007)the 111 Project (B18005)
文摘A brain-computer interface (BCI) facilitates bypassing the peripheral nervous system and directly communicating with surrounding devices. Navigation technology using BCI has developed-from exploring the prototype paradigm in the virtual environment (VE) to accurately completing the locomotion intention of the operator in the form of a powered wheelchair or mobile robot in a real environment. This paper summarizes BCI navigation applications that have been used in both real and VEs in the past 20 years. Horizontal comparisons were conducted between various paradigms applied to BCI and their unique signal-processing methods. Owing to the shift in the control mode from synchronous to asynchronous, the development trend of navigation applications in the VE was also reviewed. The contrast between high level commands and low-level commands is introduced as the main line to review the two major applications of BCI navigation in real environments: mobile robots and unmanned aerial vehicles (UAVs). Finally, applications of BCI navigation to scenarios outside the laboratory;research challenges, including human factors in navigation application interaction design;and the feasibility of hybrid BCI for BCI navigation are discussed in detail.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT under Grant NRF-2022R1A2C1005316.
文摘Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive functions.Effective rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by artifacts.Accurate classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific variability.To address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and robustness.GACL-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal dynamics.Evaluated on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art methods.Additionally,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive rehabilitation.These findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.
基金supported by the Natural Science Foundation of Hebei Province(F2024202019)the National Natural Science Foundation of China(32201072).
文摘A brain-computer interface(BCI)based on motor imagery(MI)provides additional control pathways by decoding the intentions of the brain.MI ability has great intra-individual variability,and the majority of MI-BCI systems are unable to adapt to this variability,leading to poor training effects.Therefore,prediction of MI ability is needed.In this study,we propose an MI ability predictor based on multi-frequency EEG features.To validate the performance of the predictor,a video-guided paradigm and a traditional MI paradigm are designed,and the predictor is applied to both paradigms.The results demonstrate that all subjects achieved>85%prediction precision in both applications,with a maximum of 96%.This study indicates that the predictor can accurately predict the individuals’MI ability in different states,provide the scientific basis for personalized training,and enhance the effect of MI-BCI training.
基金Supported by the Natural Science Foundation of Shandong Province(No.ZR2022MF309)the Science and Technology Small and Mediumsized Enterprise Innovation Ability Enhancement Projec of Shandong Province(No.2022TSGC2554).
文摘The classification of motor imagery electroencephalogram(MI-EEG)signals is one of the key challenges in brain-computer interface(BCI)technology.Existing Riemannian geometry-based methods for MI-EEG signal analysis,which rely on a single symmetric positive definite(SPD)manifold,often provide a limited geometric structure,making it difficult to fully capture the complex geometric characteristics of the signals.To address this issue,this paper proposes an innovative classification method for MI-EEG signals based on multi-Riemannian kernel fusion features(MRKFF).This method extends the classical SPD manifold by incorporating the Gaussian SPD manifold and the Grassmann manifold,extracting more discriminative kernel features from these heterogeneous manifolds for fusion-based classification.The proposed method is validated on the OpenBMI binary classification dataset and the BCI Competition IV-2a four-class dataset,achieving average classification accuracies of 75.6%and 71.0%,with Kappa values of 0.50 and 0.61,respectively.The proposed MRKFF method provides a new perspective for the geometric analysis of MI-EEG signals,enabling a deeper understanding and analysis of the complex geometric structure of these signals,thereby achieving more accurate signal classification in BCI applications.
文摘Deep learning methods have been widely applied in motor imagery(MI)-based brain-computer interfaces(BCI)for decoding electroencephalogram(EEG)signals.High temporal resolution and asymmetric spatial activation are fundamental properties of EEG during MI processes.However,due to the limited receptive field of convolutional kernels,traditional convolutional neural networks(CNNs)often focus only on local features,and are insufficient to cover neural processes across different frequency bands and duration scales.This limitation hinders the effective characterization of rhythmic activity changes in MI-EEG signals over time.Additionally,MI-EEG signals exhibit significant asymmetric activation between the left and right hemispheres.Traditional spatial feature extraction methods overlook the interaction between global and local regions at the spatial scale of EEG signals,resulting in inadequate spatial representation and ultimately limiting decoding accuracy.To address these limitations,in this study,a novel deep learning network that integrates multi-modal temporal features with spatially asymmetric feature modeling was proposed.The network first extracts multi-modal temporal information from EEG data channels,and then captures global and hemispheric spatial features in the spatial dimension and fuses them through an advanced fusion layer.Global dependencies are captured using a self-attention module,and a multi-scale convolutional fusion module is introduced to explore the relationships between the two types of temporal features.The fused features are classified through a classification layer to accomplish motor imagery task classification.To mitigate the issue of limited sample size,a data augmentation strategy based on signal segmentation and recombination is designed.Experimental results on the BCI Competition IV-2a(bbic-IV-2a)and BCI Competition IV-2b(bbic-IV-2a)datasets demonstrated that the proposed method achieved superior accuracy in multi-class motor imagery classification compared with existing models.On the BCI-IV-2a dataset,it attained an average classification accuracy of 84.36%,while also showing strong performance on the binary classification BCI-IV-2b dataset.These outcomes validate the capability of the proposed network to enhance MI-EEG classification accuracy.
文摘背景:近年来随着脑机接口技术的发展,它在脑卒中康复过程中的疗效已得到证实,并取得了丰富成果,亟需进行可视化分析以了解研究前沿与热点。目的:应用文献计量学可视化软件分析脑机接口在脑卒中康复领域应用的前沿热点及研究趋势。方法:以Web of Science核心合集与中国知网数据库作为研究基础,利用Citespace 6.4.1、VOSviewer 1.6.20和Excel 2021工具对检索所得的与脑机接口技术在脑卒中功能恢复中应用相关的中英文相关文献进行可视化数据分析,通过科学计量手段深入剖析脑机接口技术在脑卒中康复领域的研究现状、热点议题及未来趋势。结果与结论:①共纳入2003-2025年中英文文献985篇(英文879篇,中文106篇),该领域国内外年发文量均持续增长;②中国、美国与德国是该领域年发文量最多的国家;该领域最具影响力的机构为德国图宾根大学,中文发文量最高的机构为复旦大学附属华山医院;瑞士的《FRONTIERS IN NEUROSCIENCE》是英文发文量最高的期刊,《中国康复医学杂志》为中文发文量最高的期刊;英文发文量最高的作者为德国的Birbaumer Niels,中文发文量最高的作者为贾杰;③文献分析可见,国际研究侧重理论与临床效果的验证,且关注上肢功能与神经的恢复;国内研究更关注技术与系统的优化与开发,侧重康复领域应用的广泛探索;④运动想象为中英文文献共同的高频关键词,研究热点聚焦在基于脑电图、运动想象的脑机接口系统开发;⑤多模态结合、人工智能融合、康复手段拓展及国际合作深化可能是该领域未来发展的主要趋势。
