Estimation of effective connectivity in motor areas using partial directed coherence based on data-driven approach

Effective connectivity has been employed to study brain networks and connectivity patterns. The effective connectivity networks among primary motor area recruited by motor imagery (MI) were explored by means of empirical mode decomposition (EMD) and partial directed coherence (PDC), based on Electroencephalography (EEG) data. At the first stage, empirical mode decomposition (EMD) is used to decompose the preprocessed EEG signals into a series of IMFs. Further, the pair wise casual effect of each IMF is estimated by PDC. Finally, the estimation of effective connectivity in primary motor areas during MI tasks. Our results demonstrate that the effective connectivity is different underlying left-/right-hand MI tasks. The proposed method brings a significant tool for the detection of effective connectivity. This paper demonstrates that EMD-based PDC method can provide an effective pattern in the MI task classification and the potential for BCI applications.