基于多导脑电特征的生理性精神疲劳分析

Physiological Mental Fatigue Analysis Based on Multichannel Electroencephalogram Features

通过对4种生理性精神疲劳状态下4导脑电信号进行功率谱和小波熵特征分析,研究了脑电信号各节律相对功率以及小波熵与生理性精神疲劳程度之间的关系,并分析了它们在不同生理性精神疲劳状态下的变化规律及其相关性.实验分析结果表明,脑电信号各节律的相对功率以及小波熵平均值与生理性精神疲劳程度之间存在很强的关联性,对于不同的生理性精神疲劳状态,随着生理性精神疲劳程度的增加,其脑电信号的小波熵平均值逐渐降低,θ、α和β节律高频快波相对功率的平均值逐渐降低,而δ节律高幅度慢波相对功率平均值逐渐增加.脑电信号各节律的相对功率以及小波熵平均值有望成为衡量生理性精神疲劳程度的指标.

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 βrhythms 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.