Multi-class Motor Imagery EEG Classification Using Convolution Neural Network

Electroencephalogram (EEG) signals based on Motor Imagery (MI) are a widely used form of input in Brain Computer Interface (BCI). Although there are several ways to classify data, a question remains as to which method to use in EEG signals based on motor imagery. This article presents an attempt to reach the best classification method based on deep learning methods by comparing two models: Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), on the same basic data set. The BCI Competition IV dataset 2a was used as the base dataset to test the two classification methods. Experimental results show that the proposed CNN model outperforms the LSTM model, with an accuracy value of 74%, and other stateof-the-art methods.