EEG Classification Based on Time Transition of Functional Brain Connectivity

Abstract

By calculating time-series correlations of EEG data between brain regions, we can obtain the weighted graph containing the nodes of each region, which allows us to express the functional connectivity (FC) of the brain. Recently, methods for analyzing brain states in terms of such brain networks have attracted increasing attention. Studies have often focused on a single network constructed from data over a period of time, however the FC actually changes over time. This study proposes new time-series features obtained from the moving correlations of EEG. We compared the classification accuracy of the task estimation between normal and moving correlations.