Classification of Motor and Mental Imagery EEG Signals in BCI Systems Based on Signal-to-Image Conversion

Brain-Computer Interface (BCI) systems establish a control and communication relationship between the human brain and computers including robots or other devices to help individuals with severe motor disabilities. The classification of motor and mental imagery electroencephalogram (EEG) signals is complicated because these signals are usually case-specific and distinct models must be trained for each subject to process and classify his/her EEG signals. Moreover, in BCI systems EEG signals are processed online, so the time latency must be very low. In this paper, we have proposed a method based on signal-to-image conversion to investigate image processing techniques in the pair-wise classification of motor and mental imagery EEG signals. We first decomposed EEG signals of each trial into four sub-bands. Then, for each sub-band, we converted EEG time series to 2-dimensional (2D) images using covariance between signals of all channels. Then, statistical, textural and PCA-based features were extracted from these images and fed to a support vector machine (SVM) classifier. Our results were promising in the offline processing and achieved an average classification accuracy of 79.57%.