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Optimizing Mental Workload by Functional Near-Infrared Spectroscopy Based Dynamic Difficulty Adjustment.

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    Summary
    This summary is machine-generated.

    Cognitive training gains can be lost to mental fatigue. A new neurofeedback training (NFT) system using functional near-infrared spectroscopy (fNIRS) and dynamic difficulty adjustment (DDA) helped participants avoid fatigue during cognitive tasks.

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    Area of Science:

    • Neuroscience
    • Cognitive Psychology
    • Biomedical Engineering

    Background:

    • Cognitive training benefits can be negated by mental fatigue.
    • Mental fatigue assessment often relies on subjective measures.
    • Objective monitoring of fatigue during cognitive tasks is needed.

    Purpose of the Study:

    • To design and implement a functional near-infrared spectroscopy (fNIRS) - dynamic difficulty adjustment (DDA) system.
    • To investigate the system's ability to mitigate mental fatigue during cognitive training.
    • To compare neurofeedback training (NFT) with fixed difficulty training (FDT).

    Main Methods:

    • A system integrating fNIRS with DDA was developed.
    • Twenty-five healthy volunteers participated in two training sessions: FDT and NFT.
    • Workload was assessed using the National Aeronautics and Space Administration Task Load Index (NASA-TLX).

    Main Results:

    • NFT subjects showed sustained oxygenation levels, unlike FDT subjects.
    • NFT participants reported higher NASA-TLX scores, particularly in mental demand and frustration.
    • The fNIRS-DDA system appeared to help participants avoid mental fatigue.

    Conclusions:

    • The fNIRS-DDA system shows promise in preventing mental fatigue during cognitive tasks.
    • Objective fNIRS data, combined with subjective workload, can indicate fatigue.
    • Future research will explore the system's potential in preventing Alzheimer's disease progression.