Subject-specific mental workload classification using EEG and stochastic configuration network (SCN)

• DOI: 10.1016/j.bspc.2021.102711
• Publication Date: 2021-05-07T17:35:29Z
• Authors: Liping Pang; Liang Guo; Jie Zhang; Xiaoru Wanyan; Hongquan Qu; Xin Wang
• Keywords: 03 medical and health sciences; 0302 clinical medicine

Abstract Mental workload assessment of the operators in some safety-critical human-machine systems is an important research topic. In this paper, an experiment was designed to obtain the electroencephalogram (EEG) data under three levels of mental workload. The EEG data of multiple subjects were used for the mental workload classification based on the stochastic configuration network (SCN). The subject-specific classifiers (SSCs) were built by the individual EEG data. The results showed that the range of SSC test accuracy was between 56.5 % and 90.2 % with an average of 75.9 %. The SSC accuracy had a positive correlation with the operating accuracy (r = 0.852, p