Physiological mental fatigue analysis based on multichannel electroencephalogram features

Abstract

Investigating the effects on physiological mental fatigue with continuous studying and short rest, the relationships between relative power in different rhythms of electroencephalogram (EEG) and physiological mental fatigue, and between wavelet entropy of EEG and physiological mental fatigue are explored by analyzing power spectrum and wavelet entropy features of four channels' EEG in four mental fatigue states, and the variations of relative power and wavelet entropy of EEG at different physiological mental fatigue levels are sought out. The experimental results show that physiological mental fatigue level increases with the increase of the studying time and intensity, and short rest can relieve the physiological mental fatigue level. The relative power and wavelet entropy of EEG are strongly correlated with the physiological mental fatigue level. The average relative power in θ, α and β rhythms and the wavelet entropy of EEG decrease, while the average relative power in δ rhythm of EEG increases with the increasing physiological mental fatigue level. The average relative power in different rhythms and the wavelet entropy of EEG are expected to serve as the index for detecting physiological mental fatigue level.