Mental Workload Classification Method Based on EEG Independent Component Features

被引:28
作者
Qu, Hongquan [1 ]
Shan, Yiping [1 ]
Liu, Yuzhe [1 ]
Pang, Liping [2 ]
Fan, Zhanli [1 ]
Zhang, Jie [2 ]
Wanyan, Xiaoru [2 ]
机构
[1] North China Univ Technol, Sch Informat Sci & Technol, Beijing 100144, Peoples R China
[2] Beihang Univ, Sch Aeronaut Sci & Engn, Beijing 100191, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2020年 / 10卷 / 09期
基金
中国国家自然科学基金;
关键词
classification of mental workload; independent component analysis (ICA); support vector machine (SVM); EEG; NEAR-INFRARED SPECTROSCOPY; SIGNALS;
D O I
10.3390/app10093036
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
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.
引用
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页数:15
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