Dynamic Bayesian Networks for EEG motor imagery feature extraction

Dynamic Bayesian Networks (DBNs) are efficient graphical tools that could be used to detect causal relationships in multivariate systems. Here, we utilize DBNs to infer causality among electroencephalography (EEG) electrodes during a motor imagery task. We inferred the causal relationships between EEG electrodes during each of right and left hands imagery movements from 9 different subjects. We demonstrate how using the inferred connectivity as a feature enhances the discrimination among right and left hands imagery movements compared to using traditional band power features. Our analysis reveals a distinctive connectivity pattern manifested by an increase in the number of incoming connections to the right hemisphere motor area compared to the left hemisphere during right hand imagery movements. This pattern is reversed during left hand imagery movements.