Mapping Information Flow of Independent Source to Predict Conscious Level: A Granger Causality Based Brain-Computer Interface

Recent studies have shown that the various brain networks over different cognitive states. In contrast to measure a physiological change over a single region, the information flows between brain regions described by effective connectivity provides an informative dynamic over the whole brain. In this study, we proposed a source information flow network based on the combination of Granger causality and support vector regression to predict driver´s conscious level. This work provides the first application of using brain network to develop a brain-computer interface and obtain a sound result of performance.