@inproceedings{f50777d36e2e4f0fb4b88fbabd0465e6,
title = "The Experiment Study on Evaluation Method of Mental Fatigue in Cognitive Task Based on Physiological Signals",
abstract = "TloadDback task experiment with personalized stimulus presentation time was designed to induce mental fatigue. The effect of mental fatigue induction was verified by analyzing the subjective score, behavioral performance data and physiological signal data (The EEG data, ECG data and eye movement data were collected in the experiment). Deep learning method was used to explore the best combination of physiological signals for mental fatigue evaluation according to different combinations of EEG, ECG and eye movement data. The results showed that the combination of EEG and eye movement signals had the highest accuracy of 92.61\%.",
keywords = "Cognition, ECG signal, EEG signal, Evaluation, Eye movement, Mental fatigue",
author = "Zhongqi Liu and Ze Li and Qianxiang Zhou",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 25th International Conference on Human-Computer Interaction , HCII 2023 ; Conference date: 23-07-2023 Through 28-07-2023",
year = "2023",
doi = "10.1007/978-3-031-35989-7\_46",
language = "英语",
isbn = "9783031359880",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "358--365",
editor = "Constantine Stephanidis and Margherita Antona and Stavroula Ntoa and Gavriel Salvendy",
booktitle = "HCI International 2023 Posters - 25th International Conference on Human-Computer Interaction, HCII 2023, Proceedings, Part I",
address = "德国",
}