Granger Causality Analysis in EEG

Abstract

Brain’s large-scale networks may interrelate in a dynamic, flexible and adaptive manner to function either cooperatively or competitively depending on circumstances or context. To estimate the extent of the network change in specific tasks or conditions, Granger causality (GC) analysis is a commonly used tool to model directed information transfer, or effective connectivity, between sources of EEG activity. In this talk, I would like to share my experience of Granger causality analysis in EEG. I will demonstrate how we used GC to understand fluctuations in human attention during driving and to explore brain signatures of different chronic pain disorders.