东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2005, Vol. 26 ›› Issue (12): 1174-1177.DOI: -

• 论著 • 上一篇    下一篇

基于脑电α波的非线性参数人体疲劳状态判定

王黎;于涛;闻邦椿;   

  1. 东北大学机械工程与自动化学院;东北大学机械工程与自动化学院;东北大学机械工程与自动化学院 辽宁沈阳110004;辽宁沈阳110004;辽宁沈阳110004
  • 收稿日期:2013-06-24 修回日期:2013-06-24 出版日期:2005-12-15 发布日期:2013-06-24
  • 通讯作者: Wang, L.
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(10402008)

Assessment based on nonlinear parameters of EEG α waves for human-body fatigues

Wang, Li (1); Yu, Tao (1); Wen, Bang-Chun (1)   

  1. (1) School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110004, China
  • Received:2013-06-24 Revised:2013-06-24 Online:2005-12-15 Published:2013-06-24
  • Contact: Wang, L.
  • About author:-
  • Supported by:
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摘要： 提取脑电信号(EEG)的α波并计算其三种非线性参数,在此基础上归纳出用于评估人体疲劳状态的综合判据.首先,在小波变换的基础上,从原始信号中提取EEG的α波,然后计算其最大李亚普诺夫指数、复杂度和近似熵.这些非线性参数的数值可以定量地反映人的生理活动,进而可以用于评价疲劳状态.对现有EEG数据进行计算和统计,归纳出建立在上述三种非线性参数基础上的疲劳状态的综合评估判据.针对18组已知数据,采用上述综合判据得到相应的不同状态的判定结果.与实际情况相对照,对疲劳和非疲劳状态的评估准确率接近100%,但对轻微、中等和严重疲劳状态之间的区分精度稍低一些.

关键词: 人体疲劳, 脑电信号(EEG), α波, 非线性参数, 小波变换, 状态评估

Abstract: Based on the three calculated nonlinear parameters of the waves, i.e., maximum Lyapunov exponents, complexity and approximate entropy, which are extracted from the original electroencephalograph (EEG) signals with wavelet transform technique, a comprehensive criterion is proposed to assess the human-body fatigue levels. The three nonlinear parameters are available to quantitatively reflect human's physiological activities and, further, evaluate the fatigue level of human body. Computing and analyzing statistically the existing EEG data, a comprehensive assessment criterion is set up for fatigue levels in terms of the nonlinear parameters as above. For the known 18 sets of different EEG signal data measured, the assessment results are obtained correspondingly. Compare with actualities, the identification accuracy of fatigue got in this way tends to 100%. However, the accuracy to differ being a bit tired from medium tiredness or being extremely fatigued is relatively lower than expected.

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