Subject-specific mental workload classification using EEG and stochastic configuration network (SCN). (July 2021)

• Record Type: Journal Article
• Authors: Pang, Liping; Guo, Liang; Zhang, Jie; Wanyan, Xiaoru; Qu, Hongquan; Wang, Xin
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Mental workload -- Subject-specific classifier -- Subject-multiple classifier -- Stochastic configuration network -- Multiple subjects; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102711
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The electroencephalogram (EEG) data of 16 subjects are investigated for the mental workload classification by the Stochastic Configuration Network (SCN). 16 Subject-specified Classifiers (SSCs) of the mental workload are established. The relationships between the SSC accuracy and the operating performance are further analyzed. 15 Subject-multiple Classifiers (SMCs) of the mental workload are established. The SMC is further compared with the SSC in terms of classifier accuracy and modeling time. 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 < 0.01). For comparison, the subject-multiple classifiers (SMCs) were established with the EEG data of multiple subjects. The results showed that the SSCs had a lower time-consuming and higher prediction accuracy than the SMCs. But the SMCs might embody the trend of statistical performance for a large number of subjects. This study provided an effective modeling method for the classification of mental workload, and it would bring great convenience to the practical application in the future.