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Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator
03:49

Evaluating Flight Performance and Eye Movement Patterns Using Virtual Reality Flight Simulator

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Cognitive Load Estimation in VR Flight Simulator.

P Archana Hebbar1, Sanjana Vinod2, Aumkar Kishore Shah3

  • 1CSIR-National Aerospace Laboratories Bengaluru, Karnataka, India.

Journal of Eye Movement Research
|September 5, 2024
PubMed
Summary

This study introduces a low-cost virtual reality flight simulator that estimates pilot cognitive load using eye-tracking and EEG signals. Findings show these physiological measures correlate strongly with perceived task difficulty, enhancing pilot training.

Keywords:
EEGHuman factorscognitive loadeye gazeflight simulatorocular parameterstask engagementvirtual reality

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Area of Science:

  • Human-Computer Interaction
  • Neuroscience
  • Aerospace Engineering

Background:

  • Pilot workload assessment is crucial for flight safety and training.
  • Traditional workload metrics can be subjective or intrusive.
  • Virtual reality (VR) offers a safe and cost-effective training environment.

Purpose of the Study:

  • To design and develop a low-cost VR flight simulator.
  • To integrate cognitive load estimation using ocular and EEG signals.
  • To evaluate the correlation between physiological workload measures and perceived task difficulty.

Main Methods:

  • Developed a VR flight simulator with a realistic battlefield scenario.
  • Utilized eye gaze tracking (pupil diameter, fixation, direction) and EEG (theta, alpha, beta bands).
  • Employed AI agents for interaction scenarios and analyzed physiological data against inceptor control metrics.

Main Results:

  • Real-time acquisition of ocular and EEG data during simulated flight tasks.
  • Cognitive load estimation based on pupil diameter, gaze patterns, and EEG indices (Task Load Index, Task Engagement Index).
  • Demonstrated a strong association between physiological workload metrics and pilot's perceived task difficulty.

Conclusions:

  • The developed VR system effectively estimates pilot cognitive load.
  • Ocular and EEG signals provide reliable physiological workload measures.
  • This approach enhances the evaluation of pilot-aircraft interaction in VR training.