The characteristic of electroencephalograph ( EEG ) signal in drowsy driving was researched. Based on independent component analysis ( ICA) algorithm, a method of determining the drowsiness degree was proposed. In a simulated driving system, the EEG signals of subjects, in both sober and drowsy (driving continuously for more than four hours) states, were captured by EEG instrument of NT-9200. The multi channel signals were analyzed with ICA algorithm, and removed ocular electric, myoelectric and power frequency interferences , and power spectral densities were calculated after fast fourier transform ( FFT) , so the fatigue index F was obtained at last. Experimental results show that the index F of drowsy state was significantly higher than the index F of sober state. The method presented in this paper can be used for determining the drowsiness degree from EEG signal effectually.%通过研究疲劳驾驶时脑电信号的特征,提出了一种基于独立分量分析(independent component analysis,ICA)的脑波疲劳状态判断方法.利用模拟驾驶系统,采用NT-9200动态脑电仪采集驾驶员在清醒和疲劳状态下(连续驾驶4h以上)的脑电信号,对采集的多导信号进行独立分量分析,去除EEG信号中的眼电、肌电及工频等干扰,经过快速傅里叶变换(fast fourier transform,FFT)后计算出脑波中多种功率谱密度,求得疲劳指数F.实验结果表明,在疲劳状态下的疲劳指数F明显高于清醒状态下的F.本文提出的脑波疲劳状态判断方法可有效用以判断驾驶员的疲劳程度.
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