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The motor imagination eeg Recognition Combined with Convolution Neural Network and Gated Recurrent Unit

The Motor Imagination EEG Recognition Combined with Convolut...

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第37届中国控制会议

作者： Jun CAI Chang WEI Xian-lun TANG Can XUE Quan CHANG Chongqing Key Laboratory of Complex Systems and Bionic Control Chongqing University of Posts and Telecommunications

For the common electroencephalogram（eeg） feature extraction methods, the temporal sequence of eeg signals is often neglected. A new model called Convolutional Gated Recurrent Neural Network that combines Convolution Neural Network（CNN） and Gated Recurrent Unit（GRU） is proposed. The model extracts the combinatorial features of the preprocessed motor imagination eeg by CNN, it enriches the GRU input, and then it uses GRU to extract some sequence information hidden in the eeg signals to improve the recognition accuracy of MI eeg signals. In addition, batch normalization（BN） is added between the CNN and the GRU to speed up the operation of the network, and a dropout is introduced to sparse networks’ connection to prevent over-fitting. Experiments results show that the average recognition accuracy of the eeg data collected by ourselves is 93.57%. In the open data set BCI competition IV data set 2 b, compared with the traditional CSP algorithm, it can improve the recognition rate of eeg by 2.56% on average.

关键词： motor imagination eeg feature extraction Convolution Neural Network Gated Recurrent Unit

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Research on Classification of motor imagination eeg Signals Based on Multi-domain Features

Research on Classification of Motor Imagination EEG Signals ...

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第40届中国控制会议

作者： Xiaoke Fang Xiaobin Li Jiacan Xu Jianhui Wang School of Information Science and Engineering Northeastern University

In view of the non-stationary and nonlinear characteristics of motor imagination eeg signals,single Angle features cannot effectively reflect the comprehensiveness of signal *** this paper,a method combining time-domain features,nonlinear dynamics and spatial features is ***,the original eeg signals were decomposed by variational mode（VMD）,then all the modal components（IMF） were decomposed by Hilbert transform,and the average instantaneous energy of each mode was calculated as a set of features;The eeg data of each experiment were segmented to obtain the sample entropy value as a group of *** the common spatial pattern（CSP） processing of the eeg signals is carried out,and the CSP projection matrix is obtained through the spatial filter,and the root mean square of this matrix is taken as the third set of ***,support vector machine（SVM） was used to classify eeg *** experimental results show that the accuracy rate reaches 94.28%,which is greatly improved compared with other methods and has a good effect.

关键词： motor imagination eeg Variational Mode Decomposition Common Spatial Pattern

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