EEG-based cross-subject mental fatigue recognition
Mental fatigue is common at work places, and it can lead to decreased attention, vigilance and cognitive performance, which is dangerous in the situations such as driving, vessel maneuvering, etc. By directly measuring the neurophysiological activities happening in the brain, electroencephalography...
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sg-ntu-dr.10356-1459722021-01-23T20:11:22Z EEG-based cross-subject mental fatigue recognition Liu, Yisi Lan, Zirui Cui, Jian Sourina, Olga Müller-Wittig, Wolfgang 2019 International Conference on Cyberworlds (CW) Fraunhofer Singapore Engineering::Electrical and electronic engineering Machine Learning Fatigue Mental fatigue is common at work places, and it can lead to decreased attention, vigilance and cognitive performance, which is dangerous in the situations such as driving, vessel maneuvering, etc. By directly measuring the neurophysiological activities happening in the brain, electroencephalography (EEG) signal can be used as a good indicator of mental fatigue. A classic EEG-based brain state recognition system requires labeled data from the user to calibrate the classifier each time before the use. For fatigue recognition, we argue that it is not practical to do so since the induction of fatigue state is usually long and weary. It is desired that the system can be calibrated using readily available fatigue data, and be applied to a new user with adequate recognition accuracy. In this paper, we explore performance of cross-subject fatigue recognition algorithms using the recently published EEG dataset labeled with two levels of fatigue. We evaluate three categories of classification method: classic classifier such as logistic regression, transfer learning-enabled classifier using transfer component analysis, and deep-learning based classifier such as EEGNet. Our results show that transfer learning-enabled classifier can outperform the other two for cross-subject fatigue recognition on a consistent basis. Specifically, transfer component analysis (TCA) improves the cross-subject recognition accuracy to 72.70 % that is higher than using just logistic regression (LR) by 9.08 % and EEGNet by 8.72 - 12.86 %. National Research Foundation (NRF) Accepted version This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its International Research Centres in Singapore Funding Initiative. 2021-01-19T03:01:51Z 2021-01-19T03:01:51Z 2019 Conference Paper Liu, Y., Lan, Z., Cui, J., Sourina, O., & Müller-Wittig, W. (2019). EEG-based cross-subject mental fatigue recognition. Proceedings of the International Conference on Cyberworlds, 247-252. doi:10.1109/CW.2019.00048 9781728122977 https://hdl.handle.net/10356/145972 10.1109/CW.2019.00048 2-s2.0-85077115360 247 252 en © 2019 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: https://doi.org/10.1109/CW.2019.00048 application/pdf |
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Engineering::Electrical and electronic engineering Machine Learning Fatigue Liu, Yisi Lan, Zirui Cui, Jian Sourina, Olga Müller-Wittig, Wolfgang EEG-based cross-subject mental fatigue recognition |
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Mental fatigue is common at work places, and it can lead to decreased attention, vigilance and cognitive performance, which is dangerous in the situations such as driving, vessel maneuvering, etc. By directly measuring the neurophysiological activities happening in the brain, electroencephalography (EEG) signal can be used as a good indicator of mental fatigue. A classic EEG-based brain state recognition system requires labeled data from the user to calibrate the classifier each time before the use. For fatigue recognition, we argue that it is not practical to do so since the induction of fatigue state is usually long and weary. It is desired that the system can be calibrated using readily available fatigue data, and be applied to a new user with adequate recognition accuracy. In this paper, we explore performance of cross-subject fatigue recognition algorithms using the recently published EEG dataset labeled with two levels of fatigue. We evaluate three categories of classification method: classic classifier such as logistic regression, transfer learning-enabled classifier using transfer component analysis, and deep-learning based classifier such as EEGNet. Our results show that transfer learning-enabled classifier can outperform the other two for cross-subject fatigue recognition on a consistent basis. Specifically, transfer component analysis (TCA) improves the cross-subject recognition accuracy to 72.70 % that is higher than using just logistic regression (LR) by 9.08 % and EEGNet by 8.72 - 12.86 %. |
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2019 International Conference on Cyberworlds (CW) |
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2019 International Conference on Cyberworlds (CW) Liu, Yisi Lan, Zirui Cui, Jian Sourina, Olga Müller-Wittig, Wolfgang |
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Conference or Workshop Item |
author |
Liu, Yisi Lan, Zirui Cui, Jian Sourina, Olga Müller-Wittig, Wolfgang |
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Liu, Yisi |
title |
EEG-based cross-subject mental fatigue recognition |
title_short |
EEG-based cross-subject mental fatigue recognition |
title_full |
EEG-based cross-subject mental fatigue recognition |
title_fullStr |
EEG-based cross-subject mental fatigue recognition |
title_full_unstemmed |
EEG-based cross-subject mental fatigue recognition |
title_sort |
eeg-based cross-subject mental fatigue recognition |
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2021 |
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https://hdl.handle.net/10356/145972 |
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1690658448174219264 |