Recognition of multi-class motor imagery EEG signals based on convolutional neural network

An automatic classification method of motor imagery (MI) signals based on deep learning was proposed, aiming at the traditional multi-class MI recognition method of electroencephalogram (EEG) signals that requires cumbersome preprocessing and feature extraction. In terms of sample representation, the multi-channel EEG signals were converted into a one-dimensional sequence signal for processing, which increased the number of samples while neglecting the influence of spatial information related to the channel position. And according to the characteristics of the input signal, the multi-layer one-dimensional convolutional neural network was used to learn time-frequency information in EEG signals under different motor imagery states, and automatically complete the feature extraction and classification. The proposed method was compared with a variety of methods on the public dataset and the classification of the actual collected data was completed. The proposed method was used to do end-to-end learning of EEG signals without prior knowledge. Experimental results show that the method can obtain higher multi-classification accuracy and reduce the impact of individual differences on classification. The proposed method is conducive to the development of MI-based brain-computer interface systems. © 2021, Zhejiang University Press. All right reserved.