Classification of EEG in eyes-open and eyes-closed state based on limited penetrable visibility graph

Abstract

Eyes-open and eyes-closed are two physiological states with different levels of brain activity. To classify EEG in eyes-open and eyes-closed, limited penetrable visibility graph method is applied to analyze EEG. It is a signal process method based on graph theory, which bridges complex networks and EEG time series. Networks topology, average path length and clustering coefficient are used to depict EEG characteristics respectively. The results show that limited penetrable visibility graph is more effective mehod than visibility graph for classifying EEG in eyes-open and eyes-closed. Clustering coefficient obtained by limited penetrable visibility graph is statistically significantly higher in eyes-closed condition and is a valid marker of eyes-closed EEG. There is no obvious difference in average path length. Limited penetrable visibility graph provides a new idea and an effective approach in classifying EEG time series.