Abstract
Excessive mental workload will reduce work efficiency, but low mental workload will cause a waste of human resources. It is very significant to study the mental workload status of operators. The existing mental workload classification method is based on electroencephalogram (EEG) features, and its classification accuracy is often low because the channel signals recorded by the EEG electrodes are a group of mixed brain signals, which are similar to multi-source mixed speech signals. It is not wise to directly analyze the mixed signals in order to distinguish the feature of EEG signals. In this study, we propose a mental workload classification method based on EEG independent components (ICs) features, which borrows from the blind source separation (BSS) idea of mixed speech signals. This presented method uses independent component analysis (ICA) to obtain pure signals, i.e., ICs. The energy features of ICs are directly extracted for classifying the mental workload, since this method directly uses ICs energy features for feature extraction. Compared with the existing solution, the proposed method can obtain better classification results. The presented method might provide a way to realize a fast, accurate, and automatic mental workload classification.
Highlights
Mental workload, called psychological load, can be understood as the amount of brain activity in a unit of time, the occupation rate of brain resources, the psychological pressure, or information processing ability of a person at work [1]
Borrowing the idea of blind source separation (BSS), this paper presents a mental workload classification method based on EEG independent components (ICs) features
We find that the low classification is normal that the classification accuracy of different subjects is different, but the accuracies accuracy of individual is related their own mental states
Summary
Called psychological load, can be understood as the amount of brain activity in a unit of time, the occupation rate of brain resources, the psychological pressure, or information processing ability of a person at work [1]. Studies have shown that excessive mental workload can cause rapid fatigue, reduced flexibility, increased mistakes, and frustrated emotions, and results in errors in information acquisition analysis and decision errors [2]; and too low mental workload can cause waste of human resources and lead to a decline in job performance. It is of great significance to improve the accuracy of mental workload classification. Some studies have found that EEG signals were highly correlated with real-time mental workload status of operator processing information jobs as neurophysiological signals directly reflect brain activity [3].
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