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http://dx.doi.org/10.14400/JDC.2021.19.10.295

Deep Learning Model for Mental Fatigue Discrimination System based on EEG  

Seo, Ssang-Hee (School of Computer Science and Engineering, Kyungnam University)
Publication Information
Journal of Digital Convergence / v.19, no.10, 2021 , pp. 295-301 More about this Journal
Abstract
Individual mental fatigue not only reduces cognitive ability and work performance, but also becomes a major factor in large and small accidents occurring in daily life. In this paper, a CNN model for EEG-based mental fatigue discrimination was proposed. To this end, EEG in the resting state and task state were collected and applied to the proposed CNN model, and then the model performance was analyzed. All subjects who participated in the experiment were right-handed male students attending university, with and average age of 25.5 years. Spectral analysis was performed on the measured EEG in each state, and the performance of the CNN model was compared and analyzed using the raw EEG, absolute power, and relative power as input data of the CNN model. As a result, the relative power of the occipital lobe position in the alpha band showed the best performance. The model accuracy is 85.6% for training data, 78.5% for validation, and 95.7% for test data. The proposed model can be applied to the development of an automated system for mental fatigue detection.
Keywords
Mental fatigue; EEG; Spectrum analysis; Deep learning; CNN;
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