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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 5109530, 9 pages
Research Article

Comparison of Different Features and Classifiers for Driver Fatigue Detection Based on a Single EEG Channel

Jiangxi University of Technology, Nanchang 330098, China

Correspondence should be addressed to Jianfeng Hu; moc.liamtoh@112sseuguh

Received 11 November 2016; Revised 27 December 2016; Accepted 15 January 2017; Published 31 January 2017

Academic Editor: Ayman El-Baz

Copyright © 2017 Jianfeng Hu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Driver fatigue has become an important factor to traffic accidents worldwide, and effective detection of driver fatigue has major significance for public health. The purpose method employs entropy measures for feature extraction from a single electroencephalogram (EEG) channel. Four types of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE), and spectral entropy (PE), were deployed for the analysis of original EEG signal and compared by ten state-of-the-art classifiers. Results indicate that optimal performance of single channel is achieved using a combination of channel CP4, feature FE, and classifier Random Forest (RF). The highest accuracy can be up to 96.6%, which has been able to meet the needs of real applications. The best combination of channel + features + classifier is subject-specific. In this work, the accuracy of FE as the feature is far greater than the Acc of other features. The accuracy using classifier RF is the best, while that of classifier SVM with linear kernel is the worst. The impact of channel selection on the Acc is larger. The performance of various channels is very different.