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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 5109530, 9 pages
https://doi.org/10.1155/2017/5109530
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.

Citations to this Article [18 citations]

The following is the list of published articles that have cited the current article.

  • Jianfeng Hu, Feiqiang Liu, and Ping Wang, “EEG-Based Multiple Entropy Analysis for Assessing Driver Fatigue,” 2019 5th International Conference on Transportation Information and Safety (ICTIS), pp. 1290–1294, . View at Publisher · View at Google Scholar
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  • Jianfeng Hu, “An approach to EEG-based gender recognition using entropy measurement methods,” Knowledge-Based Systems, vol. 140, pp. 134–141, 2018. View at Publisher · View at Google Scholar
  • Jianliang Min, Junli Xu, and Jianfeng Hu, “Real-time eye tracking for the assessment of driver fatigue,” Healthcare Technology Letters, vol. 5, no. 2, pp. 54–58, 2018. View at Publisher · View at Google Scholar
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  • Yuliang Ma, Bin Chen, Rihui Li, Chushan Wang, Jun Wang, Qingshan She, Zhizeng Luo, and Yingchun Zhang, “Driving Fatigue Detection from EEG Using a Modified PCANet Method,” Computational Intelligence and Neuroscience, vol. 2019, pp. 1–9, 2019. View at Publisher · View at Google Scholar
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