基于EEG的驾驶疲劳识别算法及其有效性验证  

Driver's Fatigue Recognition Algorithm Based on EEG and Its Validity Verification

作　　者：郭孜政[1] 牛琳博 吴志敏[1] 肖琼[1] 史磊[1] GUO Zizheng NIU Linbo WU Zhimin XIAO Qiong SHI Lei(School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031,China)

机构地区：[1]西南交通大学交通运输与物流学院,成都610031

出　　处：《北京工业大学学报》2017年第6期929-934,共6页Journal of Beijing University of Technology

基　　金：国家自然科学基金资助项目(51108390);国家重点研发计划资助课题(2016YFC0802209)

摘　　要：为有效识别驾驶员疲劳状态,基于脑电信号(electroencephalogram,EEG)提出了一种驾驶疲劳状态识别方法.首先,以时间段划分疲劳等级,并采用主、客观测评指标对疲劳等级划分的合理性进行验证.然后,利用快速傅里叶变换对脑电信号进行分析,在此基础上选取3种频段的平均幅值和5项合成指标,通过核主元分析(kernel principal component analysis,KPCA)构建疲劳识别脑电指标,结合支持向量机(support vector machine,SVM),构建了驾驶员疲劳状态识别模型.最后,采用30名驾驶员连续驾驶2 h的脑电数据,对该模型方法进行试算.试算结果表明:疲劳状态识别正确率为79.17%~92.03%,平均正确率为84.62%,该方法可用于驾驶疲劳识别.In order to recognize the driver＇s fatigue state effectively, a method of driving fatigue state identification was constructed based on electroencephalogram （EEG）. Firstly, combined with the driver＇s subjective indicators, driving behavior performance was taken as an objective evaluation index, to verify the rationality of the different levels of fatigue. Then, three bands of average amplitude and five synthetic indicators were chosen as characteristic indexes after the EEG signals were analyzed by fast Fourier transform （FFT）. Meanwhile constructing fatigue recognition bispectral index through kernel principal component analysis （KPCA）, a driver fatigue state recognition model was proposed with the support vector machine （SVM）. Finally, 2 hours continual driving EEG data was collected from 30 drivers to test the model. Result show that the recognition accuracy rate is between 79.17% - 92.03%, and the average accuracy rate is 84.62%, which proves the validity of the model.

关 键 词：驾驶疲劳 核主元分析 支持向量机 识别模型 

分 类 号：U491.254[交通运输工程—交通运输规划与管理]