Mental Workload Classification Based on Visual and Operational EEG Signals

doi:10.13438/j.cnki.jdzk.2022.01.006

Abstract

Abstract: A classification method is proposed based on EEG independent component features for the mental workload classification of visual and operational task.First,the independent component analysis method is used to decompose the independent components of the EEG signals from the mixed EEG signals;then the energy features of the four different frequency bands of the EEG independent components are extracted;and the energy features are classified.The mental workload classification experiments were carried out based on EEG signal features and EEG independent component features respectively,and the classification accuracy was 60.52% and 86.14%,with the latter increased by 42.33%.

Key words: mental workload, independent component, support vector machine, EEG signal

QU Hongquan, LIU Yuzhe, PANG Liping, SHAN Yiping. Mental Workload Classification Based on Visual and Operational EEG Signals[J]. Journal of Jishou University(Natural Sciences Edition), 2022, 43(1): 31-37.

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