Motor imagery(MI)based electroencephalogram(EEG)represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation.This study introduces a novel time embedding technique,te...Motor imagery(MI)based electroencephalogram(EEG)represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation.This study introduces a novel time embedding technique,termed traveling-wave based time embedding,utilized as a pseudo channel to enhance the decoding accuracy of MI-EEG signals across various neural network architectures.Unlike traditional neural network methods that fail to account for the temporal dynamics in MI-EEG in individual difference,our approach captures time-related changes for different participants based on a priori knowledge.Through extensive experimentation with multiple participants,we demonstrate that this method not only improves classification accuracy but also exhibits greater adaptability to individual differences compared to position encoding used in Transformer architecture.Significantly,our results reveal that traveling-wave based time embedding crucially enhances decoding accuracy,particularly for participants typically considered“EEG-illiteracy”.As a novel direction in EEG research,the traveling-wave based time embedding not only offers fresh insights for neural network decoding strategies but also expands new avenues for research into attention mechanisms in neuroscience and a deeper understanding of EEG signals.展开更多
Classifying single-trial electroencephalogram(EEG)based motor imagery(MI)tasks is extensively used to control brain-computer interface(BCI)applications,as a communication bridge between humans and computers.However,th...Classifying single-trial electroencephalogram(EEG)based motor imagery(MI)tasks is extensively used to control brain-computer interface(BCI)applications,as a communication bridge between humans and computers.However,the low signal-to-noise ratio and individual differences of EEG can affect the classification results negatively.In this paper,we propose an improved common spatial pattern(B-CSP)method to extract features for alleviating these adverse effects.First,for different subjects,the method of Bhattacharyya distance is used to select the optimal frequency band of each electrode including strong event-related desynchronization(ERD)and event-related synchronization(ERS)patterns;then the signals of the optimal frequency band are decomposed into spatial patterns,and the features that can describe the maximum differences of two classes of MI are extracted from the EEG data.The proposed method is applied to the public data set and experimental data set to extract features which are input into a back propagation neural network(BPNN)classifier to classify single-trial MI EEG.Another two conventional feature extraction methods,original common spatial pattern(CSP)and autoregressive(AR),are used for comparison.An improved classification performance for both data sets(public data set:91.25%±1.77%for left hand vs.foot and84.50%±5.42%for left hand vs.right hand;experimental data set:90.43%±4.26%for left hand vs.foot)verifies the advantages of the B-CSP method over conventional methods.The results demonstrate that our proposed B-CSP method can classify EEG-based MI tasks effectively,and this study provides practical and theoretical approaches to BCI applications.展开更多
In stroke rehabilitation,rehabilitation equipments can help with the training.But traditional equipments are not convenient to carry,which limits patients to use related rehabilitation techniques.To solve this kind of...In stroke rehabilitation,rehabilitation equipments can help with the training.But traditional equipments are not convenient to carry,which limits patients to use related rehabilitation techniques.To solve this kind of problem,a new embedded rehabilitation system based on brain computer interface(BCI)is proposed in this paper.The system is based on motor imagery(MI)therapy,in which electroencephalogram(EEG)is evoked by grasping motor imageries of left and right hands,then collected by a wearable device.The EEG is transmitted to a Raspberry Pie processing unit through Bluetooth and decoded as the instructions to control the equipment extension.Users experience the limb movement through the visual feedback so as to achieve active rehabilitation.A pilot study shows that the user can control the movement of the rehabilitation equipment through his mind,and the equipment is convenient to carry.The study provides a new way to stroke rehabilitation.展开更多
文摘Motor imagery(MI)based electroencephalogram(EEG)represents a frontier in enabling direct neural control of external devices and advancing neural rehabilitation.This study introduces a novel time embedding technique,termed traveling-wave based time embedding,utilized as a pseudo channel to enhance the decoding accuracy of MI-EEG signals across various neural network architectures.Unlike traditional neural network methods that fail to account for the temporal dynamics in MI-EEG in individual difference,our approach captures time-related changes for different participants based on a priori knowledge.Through extensive experimentation with multiple participants,we demonstrate that this method not only improves classification accuracy but also exhibits greater adaptability to individual differences compared to position encoding used in Transformer architecture.Significantly,our results reveal that traveling-wave based time embedding crucially enhances decoding accuracy,particularly for participants typically considered“EEG-illiteracy”.As a novel direction in EEG research,the traveling-wave based time embedding not only offers fresh insights for neural network decoding strategies but also expands new avenues for research into attention mechanisms in neuroscience and a deeper understanding of EEG signals.
基金Project supported by the National Natural Science Foundation of China(Nos.61702454 and 61772468)the MOE Project of Humanities and Social Sciences,China(No.17YJC870018)+1 种基金the Fundamental Research Funds for the Provincial Universities of Zhejiang Province,China(No.GB201901006)the Philosophy and Social Science Planning Fund Project of Zhejiang Province,China(No.20NDQN260YB)
文摘Classifying single-trial electroencephalogram(EEG)based motor imagery(MI)tasks is extensively used to control brain-computer interface(BCI)applications,as a communication bridge between humans and computers.However,the low signal-to-noise ratio and individual differences of EEG can affect the classification results negatively.In this paper,we propose an improved common spatial pattern(B-CSP)method to extract features for alleviating these adverse effects.First,for different subjects,the method of Bhattacharyya distance is used to select the optimal frequency band of each electrode including strong event-related desynchronization(ERD)and event-related synchronization(ERS)patterns;then the signals of the optimal frequency band are decomposed into spatial patterns,and the features that can describe the maximum differences of two classes of MI are extracted from the EEG data.The proposed method is applied to the public data set and experimental data set to extract features which are input into a back propagation neural network(BPNN)classifier to classify single-trial MI EEG.Another two conventional feature extraction methods,original common spatial pattern(CSP)and autoregressive(AR),are used for comparison.An improved classification performance for both data sets(public data set:91.25%±1.77%for left hand vs.foot and84.50%±5.42%for left hand vs.right hand;experimental data set:90.43%±4.26%for left hand vs.foot)verifies the advantages of the B-CSP method over conventional methods.The results demonstrate that our proposed B-CSP method can classify EEG-based MI tasks effectively,and this study provides practical and theoretical approaches to BCI applications.
基金Supported by the National Natural Science Foundation of China(61671193)Science and Technology Program of Zhejiang Province(2018C04012,2017C33049)Science and Technology Platform Construction Project of Fujian Science and Technology Department(2015Y2001)
文摘In stroke rehabilitation,rehabilitation equipments can help with the training.But traditional equipments are not convenient to carry,which limits patients to use related rehabilitation techniques.To solve this kind of problem,a new embedded rehabilitation system based on brain computer interface(BCI)is proposed in this paper.The system is based on motor imagery(MI)therapy,in which electroencephalogram(EEG)is evoked by grasping motor imageries of left and right hands,then collected by a wearable device.The EEG is transmitted to a Raspberry Pie processing unit through Bluetooth and decoded as the instructions to control the equipment extension.Users experience the limb movement through the visual feedback so as to achieve active rehabilitation.A pilot study shows that the user can control the movement of the rehabilitation equipment through his mind,and the equipment is convenient to carry.The study provides a new way to stroke rehabilitation.
