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Workload estimator using EEG and eye-tracking.

Ivan Tashev, Christine Beauchene, R Michael Winters

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
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    This study introduces a novel workload estimator using electroencephalography and eye-tracking for virtual reality flight simulators. The system achieves high correlation with objective flight data, enabling adaptive training.

    Area of Science:

    • Human-Computer Interaction
    • Neuroscience
    • Aerospace Engineering

    Background:

    • Workload estimation is crucial for adaptive training systems in complex environments like flight simulators.
    • Existing methods often lack person- and session-independence, limiting their real-world applicability.
    • Objective evaluation of workload is challenging, necessitating advanced estimation techniques.

    Purpose of the Study:

    • To develop and evaluate a person- and session-independent workload estimator for virtual reality flight simulators.
    • To integrate biological signals (electroencephalography, eye-tracking) into an adaptive training system.
    • To utilize objective flight log data for training and validating the workload estimator.

    Main Methods:

    • Utilized electroencephalographic (EEG) and eye-tracking data as biological signals.

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  • Developed a regression neural network trained on objective workload labels derived from flight logs.
  • Implemented a person- and session-independent estimation approach.
  • Evaluated estimator performance using correlation with objective labels.

    • Main Results:

    • The best-performing workload estimator achieved a correlation of 0.84 with objective flight log labels.
    • Demonstrated the effectiveness of using biological signals for workload estimation in a VR flight simulator.
    • The developed estimator is designed to be person- and session-independent.

    Conclusions:

    • The proposed workload estimator, based on biological signals and objective labels, shows significant promise for adaptive training systems.
    • This approach offers a robust and generalizable method for assessing pilot workload in virtual reality environments.
    • The findings support the integration of such estimators into future flight training paradigms.