A convolutional neural network and stacked autoencoders approach for motor imagery based brain-computer interface

Abstract

In this research, we are investigating Convolutional Neural Networks (CNN) and Stacked Auto Encoders (SAE) to classify EEG Motor Imagery signals. Also, we use Cohen Class Distribution (CCD) to calculate time and frequency features derived from EEG signals to feed to our network. Using this combination of CNN and SAE decrease the data dimensions. the best accuracy percentage according to our method, in an average manner, is 82%. The proposed approach was applied to the dataset IVa from BCI Competition III, a multichannel 2-class motor-imagery dataset obtained from 5 healthy subjects.