Abstract
Connectivity analysis has become an essential tool for the evaluation of functional brain dynamics. The functional connectivity between different parts of the brain, or between different sensors, is assumed to provide key information for the discrimination of brain responses. In this study, we propose an estimation of effective cortical connectivity measures in frontal and parietal areas of human brain during four different Motor Imagery (MI) tasks. Feedback based brain-computer interface (BCI) technology has been successfully implemented for recovery of stroke patients as it can enhance the neural plasticity in brain areas associated with motor execution. However, it is still challenging to obtain reliable information regarding improvement in neural functioning during rehabilitation and its neuro-physiological dynamics. Brain connectivity is a reliable biomarker associated with brain functionality. Here, we evaluate to what extent partial granger causality can provide information in form of effective neural connectivity that can differentiate motor imagery tasks. Our results on nine subjects using the EEG dataset (BCI competition 2008 dataset 2A) show distinct connectivity patterns for all four MI classes, and higher information flow in the fronto-parietal network during task phase as compared to non-task phase. The results support the conclusion that effective connectivity analysis through partial granger causality can provide key information about neural interactions specific to different MI tasks. Moreover these interactions can be utilized as reliable biomarkers for assessment of motor recovery during stroke rehabilitation.
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