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基于EEG信号特征的脑力疲劳快速检测方法

张朋 周前祥 于洪强 王川

张朋,周前祥,于洪强,等. 基于EEG信号特征的脑力疲劳快速检测方法[J]. 北京航空航天大学学报,2023,49(1):145-154 doi: 10.13700/j.bh.1001-5965.2021.0211
引用本文: 张朋,周前祥,于洪强,等. 基于EEG信号特征的脑力疲劳快速检测方法[J]. 北京航空航天大学学报,2023,49(1):145-154 doi: 10.13700/j.bh.1001-5965.2021.0211
ZHANG P,ZHOU Q X,YU H Q,et al. Fast detection method of mental fatigue based on EEG signal characteristics[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(1):145-154 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0211
Citation: ZHANG P,ZHOU Q X,YU H Q,et al. Fast detection method of mental fatigue based on EEG signal characteristics[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(1):145-154 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0211

基于EEG信号特征的脑力疲劳快速检测方法

doi: 10.13700/j.bh.1001-5965.2021.0211
基金项目: 载人航天空间医学试验项目(HYZHXM03003);武器装备军内科研项目基金(20AZ0702)
详细信息
    作者简介:

    张朋

    周前祥

    王川等:基于EEG信号特征的脑力疲劳快速检测方法研究 5

    通讯作者:

    E-mail: hg04381@163.com

  • 中图分类号: V7

Fast detection method of mental fatigue based on EEG signal characteristics

Funds: Manned Space Medical Experiment Project (HYZHXM03003); Military Scientific Research Project Fund of Weapons and Equipment of China (20AZ0702)
More Information
  • 摘要:

    空间站飞行过程中航天员容易产生脑力疲劳,其是影响作业效率和引起失误的主要因素。为此,研究人体脑力疲劳的快速检测方法,将有利于保障在轨运行安全。脑电波(EEG)的特征变化能够反映出大脑疲劳状态,但现有EEG方法分析脑力疲劳时需要多个导联的信号,这严重限制了其在空间站环境中的实际应用。通过地基实验,采用36 h睡眠剥夺的方式成功诱发出45名受试者的多种脑力疲劳状态。针对EEG信号的非平稳性,设计的8层db4小波变换结构,有效分解出了δθαβ脑节律波。先使用方差分析( ANOVA)和Logistic回归筛选出脑力疲劳敏感特征,再依据脑力疲劳敏感特征数量进一步筛选出脑力疲劳敏感导联,应用6个敏感导联的特征分别构建了随机森林回归模型。加权融合6个导联处的回归模型,形成脑力疲劳快速检测模型,其平均精确率高达85.25%。

     

  • 图 1  脑电信号采集场景

    Figure 1.  EEG acquisition scene

    图 2  小波变换结构

    Figure 2.  Wavelet transform structure

    图 3  小波变换提取的脑节律波

    Figure 3.  Brain rhythm wave extracted by wavelet transform

    图 4  脑力疲劳状态统计结果

    Figure 4.  Statistical results of mental fatigue

    图 5  前4个敏感特征的分布差异

    Figure 5.  Distribution differences of the first 4 sensitive features

    图 6  脑力疲劳敏感导联的特征统计

    Figure 6.  Feature statistics of sensitive leads of mental fatigue

    图 7  脑力疲劳检测模型的ROC曲线

    Figure 7.  ROC curve of mental fatigue detection model

    表  1  脑力疲劳主观评分及等级划分

    Table  1.   Subjective score and grade division of mental fatigue

    脑力疲劳分级主观评分
    无疲劳感s<2
    轻微疲劳2≤s<4
    中度疲劳4≤s<6
    较严重疲劳6≤s<8
    很严重疲劳s≥8
    下载: 导出CSV

    表  2  脑力疲劳的敏感特征统计

    Table  2.   Statistics of sensitive characteristics of mental fatigue

    敏感特征导联位置PAUC
    α波绝对能量F30.000 40.695±0.054
    θ/β相对能量FZ0.000 60.682±0.049
    Hjorth复杂性OZ0.000 80.669±0.057
    Renyi熵FZ0.000 80.655±0.062
    α/(δ+θ+α+β)相对能量O10.001 20.647±0.053
    δ波绝对能量O20.001 80.649±0.042
    Tsallis熵F30.003 70.603±0.057
    Shannon熵F40.005 10.612±0.046
    δ/(δ+θ+α+β)相对能量FC10.008 30.623±0.052
    Hjorth活动性F40.012 90.598±0.051
    (α+θ)/(α+β)相对能量PZ0.014 60.587±0.045
    β波绝对能量O10.017 30.591±0.050
    Teager平均能量F40.024 00.584±0.049
    对数能量熵OZ0.026 90.575±0.054
    (α+θ)/β相对能量F30.030 50.572±0.043
    θ波绝对能量O10.034 90.565±0.038
    Hjorth移动性CZ0.038 50.560±0.047
    α/β相对能量FZ0.042 70.552±0.039
    β/(δ+θ+α+β)相对能量FZ0.048 30.554±0.042
    下载: 导出CSV

    表  3  脑力疲劳敏感导联的重要特征

    Table  3.   Important characteristics of mental fatigue sensitive leads

    FZO1F4O2OZF3
    θ/β相对能量α/(δ+θ+α+β)相对能量Hjorth活动性δ波绝对能量Hjorth复杂性α波绝对能量
    Renyi熵β波绝对能量Shannon熵α/(δ+θ+α+β)相对能量Teager平均能量(α+θ)/β相对能量
    α/β相对能量θ波绝对能量θ/β相对能量Hjorth复杂性对数能量熵Tsallis熵
    β/(δ+θ+α+β)相对能量Hjorth活动性δ波绝对能量θ/β相对能量α/β相对能量θ/β相对能量
    δ波绝对能量Teager平均能量δ/(δ+θ+α+β)相对能量(α+θ)/(α+β)相对能量β/(δ+θ+α+β)相对能量α/(δ+θ+α+β)相对能量
    Teager平均能量δ波绝对能量(α+θ)/(α+β)相对能量Teager平均能量Shannon熵δ波绝对能量
    Hjorth活动性θ/β相对能量Hjorth移动性对数能量熵Hjorth移动性Hjorth活动性
    Renyi熵Shannon熵β波绝对能量Hjorth移动性β波绝对能量α/β相对能量
    β波绝对能量Hjorth移动性Renyi熵α/β相对能量(α+θ)/β相对能量Renyi熵
    (α+θ)/(α+β)相对能量α/β相对能量Teager平均能量θ波绝对能量θ/β相对能量Teager平均能量
    下载: 导出CSV

    表  4  敏感导联回归模型的AUC值

    Table  4.   AUC value of sensitive lead regression model

    敏感导联无疲劳感轻度疲劳中度疲劳较重疲劳严重疲劳
    FZ0.79510.70560.78540.70560.7469
    O10.74580.68910.71210.79050.7854
    F40.79560.72360.75410.71250.7965
    O20.75210.75320.69570.75580.7236
    OZ0.80230.74560.75540.74150.7451
    F30.76850.67850.70280.73070.8218
    下载: 导出CSV

    表  5  脑力疲劳融合检测模型的比较

    Table  5.   Comparison of fusion detection models for mental fatigue

    模型LogisticANNRF
    SVM0.4456↓0.3895↑0.0245↑
    Logistic0.2453↑0.0187↑
    ANN0.0236↑
    RF
    注:↑指横向单元格中的方法优于纵向单元格中的方法, ↓指横向单元格中的方法差于纵向单元格中的方法。
    下载: 导出CSV

    表  6  脑力疲劳检测模型的分类结果

    Table  6.   Classification results of mental fatigue detection model

    检测模型精确率/%召回率/%F1/%
    无疲劳感87.7120.8233.65
    轻度疲劳81.0431.2045.05
    中度疲劳82.7129.8643.88
    较重疲劳86.0819.6431.98
    严重疲劳88.7318.2930.33
    平均值85.2523.9636.98
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-04-23
  • 录用日期:  2021-07-18
  • 网络出版日期:  2023-01-16
  • 刊出日期:  2021-07-24

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