Phase synchronization based weighted networks for classifying levels of fatigue and sleepiness

Abstract

This paper presents the variation of functional interdependency of electroencephalograph (EEG) signals from different cortical areas during a 36 hour long sleep deprived experiment using phase synchronization. Weighted undirected network structures have been constructed based on the magnitude of Phase Synchronization at various levels of wavelet decomposition. Various network parameters have been computed at each stages of the experiment to study the integration and segregation of different lobes. It has been found that few network parameters exhibit definite patterns in some frequency bands with increasing sleepiness and fatigue at successive stages of the experiment.