基于多生理信号的脑力负荷评估。
Assessment of mental workload based on multi-physiological signals.
机构信息
SAMR Key Laboratory of Human Factors and Ergonomics, China National Institute of Standardization, Beijing, 100191, China.
Tsinghua University, Beijing, 100084, China.
出版信息
Technol Health Care. 2020;28(S1):67-80. doi: 10.3233/THC-209008.
BACKGROUND
Mental workload is one of the contributing factors to human errors in road accidents or other potentially adverse incidents.
OBJECTIVE
This research probes the effects of mental workload on the electroencephalographic (EEG) and electrocardiogram (ECG) of subjects in visual monitoring tasks, based on which a comprehensive evaluation model for mental workload is established effectively.
METHODS
Three degrees of mental workload were obtained by monitoring tasks with different levels of difficulty. 20 healthy subjects were selected to take part in the research.
RESULTS
The subjective scores showed a significant increase with the increase of task difficulty, meanwhile the reaction time (RT) increased and the accuracy decreased significantly, which proved the validity of three degrees of mental workload induced. For the EEG parameters, a significant decrease of θ energy was found in Frontal, Parietal and Occipital with the increase of level of mental workload, as well as a significant decrease of α energy in Frontal, Central and Occipital, meanwhile a significant increase of β energy occurred in Frontal and Occipital. There was a significant decrease of α/θ in Occipital, and significant increases of θ/β and (α+β)/θ in Frontal, Central and Occipital, meanwhile (α+θ)/β and WPE decreased significantly in Frontal and Occipital. Among the ECG parameters, it was shown that Mean RR, RMSSD, HF_norm and SampEn decreased significantly with the increase of task difficulty, while LF_norm and LF/HF showed significant increases. These EEG indictors in Occipital and ECG indictors were chosen and constituted a multidimensional original sample. Principal Component Analysis (PCA) was used to extract the principal elements and decreased the dimension of sample space in order to simplify the calculation, based on which an effective classification model with accuracy of 80% was achieved by support vector machine (SVM).
CONCLUSION
This study demonstrates that the proposed algorithm can be applied to mental workload monitoring.
背景
精神工作负荷是导致道路事故或其他潜在不良事件中人为失误的因素之一。
目的
本研究通过视觉监测任务探讨精神工作负荷对被试者脑电图（EEG）和心电图（ECG）的影响，在此基础上有效建立了精神工作负荷综合评价模型。
方法
通过监测不同难度水平的任务获得三种程度的精神工作负荷。选择 20 名健康受试者参与研究。
结果
主观评分随任务难度的增加而显著增加，同时反应时间（RT）显著增加，准确性显著降低，证明了三种程度的精神工作负荷的有效性。对于 EEG 参数，随着精神工作负荷水平的增加，额、顶和枕部的θ能量显著降低，同时额、中央和枕部的α能量显著降低，而额和枕部的β能量显著增加。枕部的α/θ显著降低，额、中央和枕部的θ/β和（α+β）/θ显著增加，同时额和枕部的（α+θ）/β和 WPE 显著降低。在 ECG 参数中，随着任务难度的增加，Mean RR、RMSSD、HF_norm 和 SampEn 显著降低，而 LF_norm 和 LF/HF 显著增加。选择枕部的这些 EEG 指标和 ECG 指标，并构成多维原始样本。主成分分析（PCA）用于提取主元素，并降低样本空间的维度，以简化计算，在此基础上，支持向量机（SVM）实现了准确率为 80%的有效分类模型。
结论
本研究表明，所提出的算法可应用于精神工作负荷监测。
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