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EEG-based evaluation of mental fatigue using machine learning algorithms

: Liu, Yisi; Lan, Zirui; Khoo, Han Hua Glenn; Ho, K.; Li, King Ho Holden; Sourina, Olga; Müller-Wittig, Wolfgang K.


Sourin, A. ; Institute of Electrical and Electronics Engineers -IEEE-; European Association for Computer Graphics -EUROGRAPHICS-:
International Conference on Cyberworlds, CW 2018. Proceedings : Singapore, 3-5 October 2018
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-7315-7
ISBN: 978-1-5386-7316-4
International Conference on Cyberworlds (CW) <2018, Singapore>
Conference Paper
Fraunhofer Singapore ()
Lead Topic: Digitized Work; Research Line: Human computer interaction (HCI); Electroencephalography (EEG); Support vector machines (SVM); machine learning

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-of-the-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.