基于脑电功率谱密度的作业人员脑力负荷评估方法

The accurate recognition of mental workload levels is of great significance to reduce human accidents caused by operators with invalid mental workload. This paper focuses on the objective mental workload assessment of operators in human-machine system. An aviation situational experiment based on MATB-Ⅱ was carried out at three levels of mental workload. Sixteen subjects were asked to fill in the NASA-Task Load Index (NASA-TLX) scale and the Electroencephalogram (EEG) results during the experiment were recorded. By analyzing the collected subjective and physiological data, a subject-specified mental workload assessment method was proposed using the Power Spectral Density (PSD) of EEG and the Support Vector Machine (SVM). The results show that the subjective mental workload scores increase significantly (p<0.001) with the increase of designed mental workload levels, indicating that the experimental design successfully induces different mental workload scenarios. Based on the rationality of the experimental design, the subject-specified mental workload assessment models are established, and the parameters of these models are optimized by grid search and then unified as the penalty parameter of 3 000 and the kernel function parameter of 0.000 1. The test accuracy reaches 0.966 5±0.029 8, and the area under Macro-Averaging receiver operating Characteristic curve (Macro-AUC) reaches 0.991 0±0.011 4. Thus, the models provide a new approach for the objective and accurate assessment of mental workload, providing a basis for the real-time discrimination of mental workload.