| 基于EEG信号特征的脑力疲劳快速检测方法 |
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| 引用本文： | 张朋,周前祥,于洪强,王川.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. |
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| 作者姓名： | 张朋 周前祥 于洪强 王川 |
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| 作者单位： | 1.北京航空航天大学 生物与医学工程学院，北京 100191 |
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| 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) |
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| 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δ、θ、α和β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%。
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| 关 键 词： | 脑力疲劳 脑电波 脑电特征 随机森林 检测模型 |
| 收稿时间： | 2021-04-23 |
Fast detection method of mental fatigue based on EEG signal characteristics |
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| Affiliation: | 1.School of Biological Science and Medical Engineering，Beihang University，Beijing 100191，China2.Astronaut Research and Training Center，Beijing 100194，China3.Naval Medical Center of PLA，Shanghai 200433，China |
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| Abstract: | During the flight in space station, astronauts are prone to mental fatigue, which is the main factor that affects the efficiency of operations and causes errors. For this reason, studying rapid detection methods for human mental fatigue will help ensure the safety of on-orbit operations. The characteristic changes of the electroencephalogram (EEG) can reflect the fatigue state of the brain. Still, the existing EEG method requires multiple lead signals when analyzing mental fatigue, which seriously limits its practical application in the space station environment. This study successfully induced various mental fatigue states in 45 subjects through a foundation experiment using 36 hours of sleep deprivation. Aiming at the non-stationarity of EEG signals, the designed 8-layer db4 wavelet transform structure effectively decomposes δ, θ, α, and β brain rhythm waves. First, screen out the mental fatigue sensitivity characteristics using analysis of variance (ANOVA) and Logistic regression. Secondly, according to the number of sensitive features of mental fatigue, the sharp leads of mental fatigue were further screened out. Finally, the characteristics of 6 keen leaders were used to construct random forest regression models. Finally, the weighted fusion of the regression models at 6 leads to a rapid detection model of mental fatigue, with an average accuracy rate of up to 85.25%. |
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