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Decoding Mental Workload in Virtual Environments: A fNIRS Study using an Immersive n-back Task.

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    Researchers can automatically detect user mental workload in virtual reality (VR) using functional near-infrared spectroscopy (fNIRS) brain activity. This brain-computer interface approach shows promise for adaptive VR experiences.

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    Area of Science:

    • Neuroscience
    • Human-Computer Interaction
    • Virtual Reality

    Background:

    • Virtual Reality (VR) offers immersive experiences for diverse applications.
    • Measuring cognitive load is crucial for optimizing user interaction in VR.

    Purpose of the Study:

    • To investigate the automatic classification of mental workload.
    • To analyze brain activity using functional near-infrared spectroscopy (fNIRS) within VR environments.

    Main Methods:

    • Implemented the n-back task within an immersive VR setting.
    • Collected brain activity data using fNIRS during the task.
    • Developed algorithms for person-dependent and adaptive workload classification.

    Main Results:

    • Successfully detected user mental workload from fNIRS signals in VR.
    • Demonstrated the feasibility of both person-dependent and adaptive classification models.
    • Workload classification was achieved even with physical interaction in VR.

    Conclusions:

    • fNIRS is a viable tool for monitoring mental workload in immersive VR.
    • Automatic workload detection enables adaptive VR systems.
    • This research paves the way for personalized VR training and rehabilitation.