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Title: EEG-based evaluation of mental fatigue using machine learning algorithms
Authors: Liu, Yisi
Lan, Zirui
Khoo, Glenn Han Hua
Li, Holden King Ho
Sourina, Olga
Mueller-Wittig, Wolfgang
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Liu, Y., Lan, Z., Khoo, G. H. H., Li, H. K. H., Sourina, O., & Mueller-Wittig, W. (2018). EEG-based evaluation of mental fatigue using machine learning algorithms. Proceedings of the International Conference on Cyberworlds, 276-279. doi:10.1109/CW.2018.00056
Abstract: When people are exhausted both physically and mentally from overexertion, they experience fatigue. Fatigue can lead to a decrease in motivation and vigilance which may result in certain accidents or injuries. It is crucial to monitor fatigue in workplace for safety reasons and well-being of the workers. In this paper, Electroencephalogram (EEG)-based evaluation of mental fatigue is investigated using the state-ofthe-art machine learning algorithms. An experiment lasted around 2 hours and 30 minutes was designed and carried out to induce four levels of fatigue and collect EEG data from seven subjects. The results show that for subject-dependent 4-level fatigue recognition, the best average accuracy of 93.45% was achieved by using 6 statistical features with a linear SVM classifier. With subject-independent approach, the best average accuracy of 39.80% for 4 levels was achieved by using fractal dimension, 6 statistical features and a linear discriminant analysis classifier. The EEG-based fatigue recognition has the potential to be used in workplace such as cranes to monitor the fatigue of operators who are often subjected to long working hours with heavy workloads.
ISBN: 9781538673157
DOI: 10.1109/CW.2018.00056
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:Fraunhofer Singapore Conference Papers

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