Motor Imagery EEG Recognition Based on Weight-Sharing CNN-LSTM Network

Abstract

The brain-computer interface technology enables the disabled to control external devices through the motor imagery EEG. Due to the complex changes of EEG in the time domain and frequency domain, classifiers play an important role in EEG recognition. Convolutional neural network is an excellent deep learning method, but most papers usually use one-dimensional convolution to identify EEG, and rarely consider comprehensive feature extraction and classification of time-frequency map through two-dimensional convolutional network. In this article, the time-frequency graphs of different EEG channels are superimposed by referring to the color dimension of the picture. A weighted shared two-dimensional convolutional CNN-LSTM network is proposed, which shares convolution kernels for feature maps of different channels. Compared with CNN and CNN-LSTM, the weight-sharing CNN-LSTM reduces the amount of calculation, speeds up the network training and improves the classification performance, the highest accuracy rate is 82.3%.