摘要
研究疲劳驾驶状态下驾驶员脑电信号的特征。结合Hilbert-Huang Transform(HHT)方法和近似熵方法,提出了一种新的脑电信号处理方法:HHT近似熵方法,首先用HHT方法把脑电信号分解为多个内在的模式分量,然后求取各个模式分量的近似熵值,探讨疲劳驾驶时脑电信号的非线性特征。在汽车模拟驾驶仪上进行疲劳驾驶,同时用脑电测量仪器测量驾驶员脑电,用HHT近似熵方法对正常静坐、正常驾驶、疲劳静坐、疲劳驾驶4种脑电信号进行具体的分析处理,结果表明d_2、d_4近似熵比值可以区分4种脑电信号,可以作为疲劳驾驶时的脑电特征。为疲劳驾驶的预警系统研究提供了理论上的一些依据和参考。
EEG characters of drivers in fatigued driving were studied. Combining Hilbert-Huang Transform （HHT） method with Approximate Entropy method, a new method HHT Approximate Entropy was proposed. First, the EEG signal was decomposed into several IMFs （Intrinsic Mode Function） using HHT method.Second, the APEN （Approximate Entropy） values of IMFs were calculated and EEG＇s nonlinear characters were explored according to theses values. Driving in the Car Simulating Driving System, the driver＇ s EEG signal was measured when he was fatigued. Four kinds of EEG signals, including normal sitting, normal driving, fatigued sitting, fatigued driving, were analyzed using HHT Approximate Entropy. The results indicate that the APEN ratio of d2 to d4 can distinguish the four kinds of EEG signals accurately, so it can be the EEG character of driver when he is in fatigued driving. It can provide a reference for the research of system of preventing fatigued driving.
出处
《公路交通科技》
CAS
CSCD
北大核心
2008年第6期126-129,共4页
Journal of Highway and Transportation Research and Development
基金
国家自然科学基金(60274035，60674052)
关键词
智能运输系统
疲劳驾驶
HHT近似熵
脑电
Intelligent Transport Systems
fatigued driving
HHT approximate entropy
EEG
