An EEG-Based Fatigue Detection and Mitigation System (2024)

https://doi.org/10.1142/s0129065716500180 · Повний текст

Видання: International Journal of Neural Systems, 2016, №04, с.1650018

Видавець: World Scientific Pub Co Pte Lt

Автори:

Kuan-Chih Huang
Teng-Yi Huang
Chun-Hsiang Chuang
Jung-Tai King
Yu-Kai Wang
Chin-Teng Lin
Tzyy-Ping Jung

Анотація

Research has indicated that fatigue is a critical factor in cognitive lapses because it negatively affects an individual’s internal state, which is then manifested physiologically. This study explores neurophysiological changes, measured by electroencephalogram (EEG), due to fatigue. This study further demonstrates the feasibility of an online closed-loop EEG-based fatigue detection and mitigation system that detects physiological change and can thereby prevent fatigue-related cognitive lapses. More importantly, this work compares the efficacy of fatigue detection and mitigation between the EEG-based and a nonEEG-based random method. Twelve healthy subjects participated in a sustained-attention driving experiment. Each participant’s EEG signal was monitored continuously and a warning was delivered in real-time to participants once the EEG signature of fatigue was detected. Study results indicate suppression of the alpha- and theta-power of an occipital component and improved behavioral performance following a warning signal; these findings are in line with those in previous studies. However, study results also showed reduced warning efficacy (i.e. increased response times (RTs) to lane deviations) accompanied by increased alpha-power due to the fluctuation of warnings over time. Furthermore, a comparison of EEG-based and nonEEG-based random approaches clearly demonstrated the necessity of adaptive fatigue-mitigation systems, based on a subject’s cognitive level, to deliver warnings. Analytical results clearly demonstrate and validate the efficacy of this online closed-loop EEG-based fatigue detection and mitigation mechanism to identify cognitive lapses that may lead to catastrophic incidents in countless operational environments.

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Публікації, які цитують цю публікацію

The evaluation of cEEGrids for fatigue detection in aviation

Carmen van Klaren, Anneloes Maij, Laurie Marsman, Alwin van Drongelen

https://doi.org/10.1093/sleepadvances/zpae009

2024, Sleep Advances, №1

Цитувань Crossref:0

Towards a new approach to detect sleepiness: Validation of the objective sleepiness scale under simulated driving conditions

C. Giot, M. Hay, C. Chesneau, E. Pigeon, T. Bonargent, M. Beaufils, N. Chastan, J. Perrier, F. Pasquier, S. Polvent, D. Davenne, J. Taillard, N. Bessot

https://doi.org/10.1016/j.trf.2022.08.007 ·

2022, Transportation Research Part F: Traffic Psychology and Behaviour, с.109-119

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Цитувань Crossref:6

Brain Network Changes in Fatigued Drivers: A Longitudinal Study in a Real-World Environment Based on the Effective Connectivity Analysis and Actigraphy Data