TSC-MI: A Temporal Spatial Convolution Neural Network Fused with Mutual Information for Motor Imagery Based EEG Classification.

Electroencephalography (EEG) classification is an important part in brain-computer interface system. Motor imagery is a novel experimental paradigm that has been proved effective clinically in recognizing EEG from different limb motions. Our object is to finish motor imagery based EEG classification. Due to EEG signals followed by some features, e.g. noisy, weak signal, personalization and so on, traditional methods could encounter limit from the single feature extraction. In this work, we propose a multi-scale spatio-temporal features fusion deep learning model. Given raw EEG signals, we calculate mutual information matrix among different channels. It incorporates spatio-temporal feature extraction and mutual information matrix. We deploy experiments on two datasets that consists of the High Gamma Dataset and BCI IV 2A dataset. Experiment results show that the proposed temporal spatial convolution neural network fused with mutual information model outperform other methods.