Real-time single channel EEG motor imagery based Brain Computer Interface

Abstract

This paper presents a motor imagery based Brain Computer Interface (BCI) that uses single channel EEG signal from the C3 or C4 electrode placed in the motor area of the head. Time frequency analysis using Short Time Fourier Transform (STFT) is used to compute spectrogram from the EEG data. The STFT is scaled to have gray level values on which Grey Co-occurrence Matrix (GLCM) is computed. Texture descriptors such as correlation, energy, contrast, homogeneity and dissimilarity are calculated from the GLCM matrices. The texture descriptors are used to train a logistic regression classifier which is then used to classify the left and right motor imagery signals. The single-channel motor imagery classification system is tested offline with different subjects. The average offline accuracy is 87.6%. An online BCI system is implemented in openViBE with the single channel classification scheme. The stimuli presentations and feedback are implemented in Python and integrated with the openViBe BCI system.