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A Study on Sensitive Bands of EEG Data under Different Mental Workloads

School of Information Science and Technology, North China University of Technology, Beijing 100144, China
School of Aeronautic Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
School of Aero-engine, Shenyang Aerospace University, Shenyang 110136, China
Department of Computer Science, Norwegian University of Science and Technology, Gjøvik 2802, Norway
Author to whom correspondence should be addressed.
Algorithms 2019, 12(7), 145;
Received: 29 May 2019 / Revised: 10 July 2019 / Accepted: 15 July 2019 / Published: 22 July 2019
(This article belongs to the Special Issue The Second Symposium on Machine Intelligence and Data Analytics)
PDF [8683 KB, uploaded 22 July 2019]


Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain–computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. In this paper, the sensitive bands of EEG data under different mental workloads are studied. By selecting the characteristics of EEG signals, the bands with the highest sensitivity to mental loads are selected. In this paper, EEG signals are measured in different load flight experiments. First, the EEG signals are preprocessed by independent component analysis (ICA) to remove the interference of electrooculogram (EOG) signals, and then the power spectral density and energy are calculated for feature extraction. Finally, the feature importance is selected based on Gini impurity. The classification accuracy of the support vector machines (SVM) classifier is verified by comparing the characteristics of the full band with the characteristics of the β band. The results show that the characteristics of the β band are the most sensitive in EEG data under different mental workloads. View Full-Text
Keywords: BCI; EEG; feature selection; EEG band; Gini impurity; SVM BCI; EEG; feature selection; EEG band; Gini impurity; SVM

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Qu, H.; Fan, Z.; Cao, S.; Pang, L.; Wang, H.; Zhang, J. A Study on Sensitive Bands of EEG Data under Different Mental Workloads. Algorithms 2019, 12, 145.

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