Classification of Motor Imagery EEG Signals with multi-input Convolutional Neural Network by augmenting STFT

Abstract

Motor imagery EEG classification is a crucial task in the Brain Computer Interface (BCI) system. In this paper, we propose a Motor Imagery EEG signal classification framework based on Convolutional Neural Network (CNN) to enhance the classification accuracy. For the classification of 2 class motor imagery signals, firstly we apply Short Time Fourier Transform (STFT) on EEG time series signals to transform signals into 2D images. Next, we train our proposed multi-input convolutional neural network with feature concatenation to achieve robust classification from the images. Batch normalization is added to regularize the network. Data augmentation is used to increase samples and as a secondary regularizer. A three input CNN was proposed to feed the three channel EEG signals. In our work, the dataset of EEG signal collected from BCI Competition IV dataset 2b and dataset III of BCI Competition II were used. Experimental results show that average classification accuracy achieved was 89.19% on dataset 2b, whereas our model achieved the best performance of 97.7% accuracy for subject 7 on dataset III. We also extended our approach and explored a transfer learning based scheme with pre-trained ResNet -50 model which showed promising result. Overall, our approach showed competitive performance when compared with other methods.