Classifying Single-Trial EEG During Motor Imagery Using a Multivariate Mutual Information Based Phase Synchrony Measure

In electroencephalography(EEG)-based brain computer interfaces (BCIs), interactions between different areas of the user's brain can be measured using a phase synchronization measure. In this paper, a mutual information-based multivariate phase synchrony measure is used to assess local-scale connectivity and classify EEG to BCI control condition. The results obtained using a well-known database shows that the method proposed in this paper significantly outperforms the existing technique when used for classifying right and left hand movement motor imageries of 5 different subjects using their recorded EEG signals. Specifically, the mean accuracy of the proposed method is 70% higher than that of the existing techniques based on synchrony measures. Also, statistical test shows that the channels on the right hemisphere (left hemisphere) are more synchronized during left (right) hand movement motor imagery.