Deep Learning of Motor Imagery EEG Classification for Brain-Computer Interface Illiterate Subject.

BCI illiterate subject is defined as the subject who cannot achieve accuracy higher than 70%. BCI illiterate subject cannot produce stronger contralateral ERD/ERS activity, thus most of the frequency band-based algorithms cannot obtain higher accuracy. Deep learning with convolutional neural networks (CNN) has revolutionized in many recent studies to learn features and classify different types of data through end-to-end learning. We designed a CNN to extract motor imagery EEG features and then do classification for BCI illiterate subjects in this work. Results showed that the average classification accuracy increased by 18.4% compared with the CSP+LDA algorithm, and the accuracies obtained by CNN exceed 70% for 9 of 11 subjects particularly. CNN requires only a little prior knowledge, thus the features it extracted are not limited in frequency band, but because the poor interpretability of deep learning, we do not know which kind of feature CNN extracted until now. Our future study will focus on visualizing the extracted features to support our conclusions.