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EEG and eye-tracking based measures for enhanced training.

Walid Soussou1, Michael Rooksby, Charles Forty

  • 1Quantum Applied Science and Research (QUASAR), CA 92121, USA. walid@quasarusa.com

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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Summary

This study explored using EEG and eye-tracking to assess cognitive workload during X-ray screening training. Findings show these methods correlate with expertise, suggesting potential for adaptive training programs.

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Area of Science:

  • Cognitive Science
  • Human-Computer Interaction
  • Biomedical Engineering

Background:

  • Optimizing training efficiency is crucial in high-stakes fields like security screening.
  • Traditional training assessments may not fully capture cognitive load or expertise development.
  • Unobtrusive cognitive assessment offers a novel approach to training evaluation.

Purpose of the Study:

  • To determine the feasibility of using unobtrusive cognitive assessment methodologies.
  • To optimize the efficiency and expediency of training programs.
  • To demonstrate the correlation between cognitive workload, performance, and expertise.

Main Methods:

  • Utilized electroencephalography (EEG) and eye-tracking technologies for cognitive workload assessment.
  • Measured cognitive workload and performance during simulated baggage screening tasks.
  • Correlated EEG and eye-tracking metrics with subject expertise and error rates.

Main Results:

  • Significant correlations were found between cognitive workload metrics (EEG, eye-tracking) and subject expertise.
  • Cognitive workload measurements during simulated screening tasks correlated with error rates.
  • Demonstrated the link between objective cognitive measures and task performance.

Conclusions:

  • Cognitive monitoring using EEG and eye-tracking is feasible for assessing expertise.
  • These methodologies can provide insights into training effectiveness.
  • Adaptive training paradigms based on cognitive monitoring could enhance learning efficiency.