# Mental Workload Classification Method Based on EEG Independent Component Features
###### 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. classification of mental workload; independent component analysis (ICA); support vector machine (SVM); EEG +
Footnote †: journal: Medical  
the effects of delta, theta, alpha, and gamma bands on the mental workload. Beta band is sensitive to changes in mental workload [9]. It is generally believed that psychological stress, active thinking, and attention cause EEG activity to move to higher frequency bands and suppress alpha wave activity [10]. The power spectrum energy of alpha and theta wave has a negative correlation with task difficulty and mental workload [11; 12; 13]. Gamma band correlates with cognitive task execution [14]. In order to seek an effective assessment of the mental workload status, researchers have made many beneficial attempts. The methods of evaluating mental workload include the subjective scale method represented by National Aeronautics and Space Administration-Task Load Index (NASA-TLX) scale [15]. Analysis of variance by SPSS-repeated measurements showed that the brain load factors were the main factor affecting the NASA-TLX scores [16]. In recent years, machine learning has developed rapidly. More and more researchers have focused their attention on the use of machine learning algorithms [17] to explore the correspondence between EEG and mental workload levels and establish mental workload classifiers. [PERSON] et al. [18] designed an experiment to obtain the physiological signal data of the subjects. They directly extracted the features of the physiological signals and classified the mental workload by using linear discriminant analysis (LDA), k-nearest neighbor (KNN) algorithm, and a support vector machine (SVM). [PERSON] [19] analyzed the EEG signal to establish the EEG signal feature space and constructed the mental workload status recognition model for occupant information processing tasks based on the particle swarm optimization (PSO) algorithm and SVM. [PERSON] et al. [20] extracted the frequency domain features of the collected signals and normalized them. Then they used an SVM to evaluate the cross-task brain load level in the abnormal detection process. The existing mental workload classification methods are based on the mixed EEG signals. These types of methods can achieve an assessment of the mental workload state to a certain extent, but they also have limitations. Because the channel signals, which recorded by the EEG electrodes, are a group of mixed brain signals, they 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, borrowing from the BSS idea of mixed speech signals [21], we propose a mental workload classification method based on the EEG ICs features for vision and operational tasks.