Brain networks using nonlinear interdependence-based EEG synchronization: A study of human fatigue

Abstract

Degradation in performance of human subjects due to mental or physical fatigue can be suitably predicted by the use of the electroencephalogram (EEG). Synchronization measures between EEG signals from different regions of the brain are often employed to characterize the interaction of brain areas during mental and physical activity. Analysis of fatigue induced by loss of sleep using EEG synchronization presents a promising field of research. The present paper employs Nonlinear Interdependencebased synchronization between EEG data recorded from various brain areas to analyze advancing levels of fatigue in human drivers in a sleep-deprivation experiment. The synchronization values are used to form a brain network at each stage of the experiment and values of parameters from networks corresponding to different brain regions have been compared to study the variation in connectivity between brain regions along successive stages of the experiment.