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.