Application of EEG-based Passive Mental Fatigue Detection Model to an Active Fatigue Task

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Application of EEG-based Passive Mental Fatigue Detection Model to an Active Fatigue Task
Title:
Application of EEG-based Passive Mental Fatigue Detection Model to an Active Fatigue Task
Journal Title:
2023 11th International IEEE/EMBS Conference on Neural Engineering (NER)
Keywords:
Publication Date:
19 May 2023
Citation:
Guo, L., Fang, W., Wang, C., Zhang, Z., & Ang, K. K. (2023). Application of EEG-based Passive Mental Fatigue Detection Model to an Active Fatigue Task. 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER). https://doi.org/10.1109/ner52421.2023.10123739
Abstract:
Mental fatigue results in feelings of tiredness and decreases task performance and efficiency. It can be caused by cognitive underload (passive fatigue) or overload (active fatigue). Although fatigue detection models have been developed using electroencephalogram (EEG) recordings, it is unclear whether their predictions (derived from one task) are applicable to different tasks. We investigated if an EEG-based fatigue model previously trained on a passive fatigue-inducing driving task, can predict fatigue during an active fatigue-inducing task. We collected EEG while subjects performed a target-hitting task as a non-fatigued baseline, followed by mental arithmetic tasks of increasing complexity and mental load. Overall, the fatigue score from the passive fatigue model was significantly correlated with self-reported fatigue and sleepiness, which increased over the course of the experiment. The fatigue score was higher during simple mental arithmetic than the target-hitting task, demonstrating that the model can successfully differentiate non-fatigued and passive/low mental load situations. However, the fatigue score was higher during the simple (low mental load) than the complex (high mental load) mental arithmetic task, showing that the model cannot accurately detect active fatigue. Future fatigue models should be trained on a variety of both active and passive fatigue-inducing tasks to be more generalizable.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - Strategic Programme Funds - Brain Body Initiative
Grant Reference no. : C211817001
Description:
© 2023 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.
ISSN:
1948-3554
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