Discrimination of motor imagery tasks via information flow pattern of brain connectivity

The effective connectivity refers explicitly to the influence that one neural system exerts over another in frequency domain. To investigate the propagation of neuronal activity in certain frequency can help us reveal the mechanisms of information processing by brain.

This study investigates the detection of effective connectivity and analyzes the complex brain network connection mode associated with motor imagery (MI) tasks.

The effective connectivity among the primary motor area is firstly explored using partial directed coherence (PDC) combined with multivariate empirical mode decomposition (MEMD) based on electroencephalography (EEG) data. Then a new approach is proposed to analyze the connection mode of the complex brain network via the information flow pattern.

Our results demonstrate that significant effective connectivity exists in the bilateral hemisphere during the tasks, regardless of the left-/right-hand MI tasks. Furthermore, the out-in rate results of the information flow reveal the existence of the contralateral lateralization. The classification performance of left-/right-hand MI tasks can be improved by careful selection of intrinsic mode functions (IMFs).

The proposed method can provide efficient features for the detection of MI tasks and has great potential to be applied in brain computer interface (BCI).