Driver mental fatigue leads to thousands of traffic accidents. The increasing quality and availability of low-cost electroencephalogram (EEG) systems offer possibilities for practical fatigue monitoring. However, non-data-driven methods, designed for practical, complex situations, usually rely on handcrafted data statistics of EEG signals. To reduce human involvement, we introduce a data-driven methodology for online mental fatigue detection: self-weight ordinal regression (SWORE). Reaction time (RT), referring to the length of time people take to react to an emergency, is widely considered an objective behavioral measure for mental fatigue state. Since regression methods are sensitive to extreme RTs, we propose an indirect RT estimation based on preferences to explore the relationship between EEG and RT, which generalizes to any scenario when an objective fatigue indicator is available. In particular, SWORE evaluates the noisy EEG signals from multiple channels in terms of two states: shaking state and steady state. Modeling the shaking state can discriminate the reliable channels from the uninformative ones, while modeling the steady state can suppress the task-nonrelevant fluctuation within each channel. In addition, an online generalized Bayesian moment matching (online GBMM) algorithm is proposed to online-calibrate SWORE efficiently per participant. Experimental results with 40 participants show that SWORE can maximally achieve consistent with RT, demonstrating the feasibility and adaptability of our proposed framework in practical mental fatigue estimation.
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June 2021
May 13 2021
Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation
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Yuangang Pan,
Yuangang Pan
Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW 2007, Australia [email protected]
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Ivor W. Tsang,
Ivor W. Tsang
Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW 2007, Australia [email protected]
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Yueming Lyu,
Yueming Lyu
Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW 2007, Australia [email protected]
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Avinash K. Singh,
Avinash K. Singh
Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW 2007, Australia [email protected]
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Chin-Teng Lin
Chin-Teng Lin
Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW 2007, Australia [email protected]
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Yuangang Pan
Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW 2007, Australia [email protected]
Ivor W. Tsang
Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW 2007, Australia [email protected]
Yueming Lyu
Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW 2007, Australia [email protected]
Avinash K. Singh
Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW 2007, Australia [email protected]
Chin-Teng Lin
Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW 2007, Australia [email protected]
Received:
October 07 2020
Accepted:
December 31 2020
Online ISSN: 1530-888X
Print ISSN: 0899-7667
© 2021 Massachusetts Institute of Technology
2021
Massachusetts Institute of Technology
Neural Computation (2021) 33 (6): 1616–1655.
Article history
Received:
October 07 2020
Accepted:
December 31 2020
Citation
Yuangang Pan, Ivor W. Tsang, Yueming Lyu, Avinash K. Singh, Chin-Teng Lin; Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation. Neural Comput 2021; 33 (6): 1616–1655. doi: https://doi.org/10.1162/neco_a_01382
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