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汽车安全与节能学报

汽车安全与节能学报 ›› 2016, Vol. 07 ›› Issue (02): 160-166.DOI: 10.3969/j.issn.1674-8484.2016.02.004

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监测疲劳驾驶时定量脑电图特征量化指标分析

陈朝阳1，王文军2，张超飞2，成波2，曾超3，孟祥杰2，John M CAVANAUGH1   

  1. 1. 韦恩州立大学 生物医学工程系，密歇根 48201，美国；
    2. 清华大学 汽车安全与节能国家重点实验室，北京 100083，中国；
    3. 石河子大学 信息科学与技术学院，石河子 832003，中国
  • 收稿日期:2015-10-25 出版日期:2016-06-25 发布日期:2016-07-06
  • 作者简介:陈朝阳/ Chaoyang CHEN (1965—)，男( 美国籍)， 副教授。E-mail: cchen@wayne.edu
  • 基金资助:

    国家自然科学基金资助项目(51575303，51565051) ；汽车安全与节能国家重点实验室开放基金(KF14222，KF16182)

Quantitative electroencephalography analysis for index of drowsy driving

CHEN Chaoyang 1, WANG Wenjun 2, ZHANG Chaofei 2, CHENG Bo2, ZENG Chao 3,MENG Xiangjie 2, John M CAVANAUGH 1   

  1. 1. Department of Biomedical Engineering, Wayne State University, Detroit, Michigan 48201, USA;
    2. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China;
    3. College of Information Science and Technology, Shihezi University, Shihezi 832003, China
  • Received:2015-10-25 Online:2016-06-25 Published:2016-07-06

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摘要：

为界定驾驶疲劳生理指标，使用驾驶模拟平台，研究疲劳驾驶时的脑电图(EEG) 参数的变化。用定量脑电图(qEEG) 信号分析技术，提取22 名驾驶员δ波功率以及90% 边缘频谱值， 分析不同记录部位的信号差异。进行了二元回归分析、分类辨别和受试者工作曲线(ROC) 的分析。结果表明：60 min的单一环境驾驶会产生驾驶困意，脑电信号的δ波的相对功率谱(比功率) 增大，90% 边缘频谱值减小；颞部记录到的脑电信号量化改变比前额记录到的信号更为明显。因此，δ波的比功率增加可以作为量化指标区分清醒与困意驾驶；90% 边缘频谱值的减少可以作为判别驾驶困意的另一个量化指标。

关键词: 汽车安全, 定量脑电图(qEEG) , 驾驶疲劳, 二元回归分析, 受试者工作曲线(ROC) , 90%边缘频谱值

Abstract:

The characteristics of electroencephalograph (EEG) parameters among drowsy drivers were investigated to define physiologic index of driving fatigue. Quantitative EEG (qEEG) analysis techniques were used among 22 subjects using a driving simulator to extract δ band power and 90% spectral edge frequency (SEF90) and to analyze the parameters' changes during drowsy driving and the differences from various recording locations by using binary logistic regression, discriminant classification, and receiver operating characteristic (ROC) curve. The results show that 60 minutes’ mono- environment driving lets to driving drowsiness with increase of relative δ band power and decrease of SEF90. The EEG quantitative changes recorded at the temporal regions are more than that recorded at the frontal regions. Therefore, the increase of normalized δ band power can be used as an indicator to discriminate the alert from drowsy state during driving. Decrease of SEM90 can be another indicator to determine the drowsiness during driving.

Key words: automotive safety, quantitative electroencephalography (qEEG), drowsy driving, binary logistic regression, receiver operating characteristic (ROC) curve, 90% spectral edge frequency