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http://dx.doi.org/10.22156/CS4SMB.2021.11.10.010

Derivation of EEG Spectrum-based Feature Parameters for Mental Fatigue Determination  

Seo, Ssang-Hee (School of Computer Science and Engineering, Kyungnam University)
Publication Information
Journal of Convergence for Information Technology / v.11, no.10, 2021 , pp. 10-19 More about this Journal
Abstract
In this paper, we tried to derive characteristic parameters that reflect mental fatigue through EEG measurement and analysis. For this purpose, mental fatigue was induced through a resting state with eyes closed and performing subtraction operations in mental arithmetic for 30 minutes. Five subjects participated in the experiment, and all subjects were right-handed male students in university, with an average age of 25.5 years. Spectral analysis was performed on the EEG collected at the beginning and the end of the experiment to derive feature parameters reflecting mental fatigue. As a result of the analysis, the absolute power of the alpha band in the occipital lobe and the temporal lobe increased as the mental fatigue increased, while the relative power decreased. Also, the difference in power between resting state and task state showed that the relative power was larger than the absolute power. These results indicate that alpha relative power in the occipital lobe and temporal lobe is a feature parameter reflecting mental fatigue. The results of this study can be utilized as feature parameters for the development of an automated system for mental fatigue determination such as fatigue and drowsiness while driving.
Keywords
Mental fatigue; EEG; Spectrum analysis; Feature parameter; Resting state; Task state;
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