Causal Connectivity Brain Network: A Novel Method of Motor Imagery Classification for Brain-Computer Interface Applications

The effective connectivity among overlapped core regions recruited by motor imagery (MI) was explored by means of Granger causality and graph-theoretic method, based on Electroencephalography (EEG) data. In this paper, causal connectivity brain network (CCBN) was proposed for the classification of motor imagery for brain-Ccomputer interface applications, by means of source analysis of scalp-recorded EEGs and effective connectivity networks. A classification rate of about 90% was achieved in the human subject studied using both the equivalent dipole analysis and the granger causality analysis. The present promising results suggest that the CCBN could manifest a clearer picture on the cortical activity and explore the causal relation among the independent sources, and thus facilitate the classification of MI tasks from scalp EEGs for brain-computer interface (BCI).