The Motor Imagination EEG Recognition Combined with Convolution Neural Network and Gated Recurrent Unit

摘要

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 2b, compared with the traditional CSP algorithm, it can improve the recognition rate of EEG by 2.56% on average.