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Using machine learning methods and EEG to discriminate aircraft pilot cognitive workload during flight.

Hamed Taheri Gorji1, Nicholas Wilson2, Jessica VanBree3

  • 1Biomedical Engineering Program, University of North Dakota, Grand Forks, ND, USA. hamed.taherigorji@und.edu.

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Summary

This study used electroencephalogram (EEG) data to accurately measure pilot cognitive workload during flight. Machine learning models identified workload states, improving aviation safety and aircraft system design.

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Area of Science:

  • Aviation Psychology
  • Neuroscience
  • Machine Learning

Background:

  • Pilot cognitive workload fluctuates during flight operations, potentially increasing error risk.
  • Understanding and quantifying cognitive workload is crucial for aviation safety.
  • Existing methods for assessing pilot workload may not fully capture dynamic changes.

Purpose of the Study:

  • To investigate the use of electroencephalogram (EEG) for real-time assessment of pilot cognitive workload.
  • To develop and validate a machine learning model capable of discriminating between low, medium, and high cognitive workload states.
  • To explore the potential of EEG-based workload monitoring to enhance aircraft system design and flight automation.

Main Methods:

  • Collected EEG data from ten collegiate aviation students during live-flight operations in a single-engine aircraft.
  • Analyzed EEG signals using power spectral density (PSD) and log energy entropy across delta, theta, alpha, and beta sub-bands.
  • Employed recursive feature elimination (RFE) and LassoCV for feature selection, followed by a stacking ensemble machine learning algorithm (SVM, Random Forest, Logistic Regression) with hyperparameter optimization and cross-validation.

Main Results:

  • Identified 15 key features indicative of pilot cognitive workload states.
  • The RFE-selected features achieved high performance in the stacking ensemble model: 91.67% accuracy, 93.89% precision, 91.67% recall, 91.22% F-score, and 0.93 ROC-AUC.
  • Demonstrated the effectiveness of combining PSD and log energy entropy with machine learning for workload discrimination.

Conclusions:

  • EEG, analyzed with advanced machine learning techniques, shows significant potential for accurately discriminating pilot cognitive workload levels.
  • The findings support the integration of EEG-based workload monitoring into aircraft systems to improve automation and enhance flight safety.
  • This research provides a foundation for developing intelligent systems that adapt to pilot cognitive states, mitigating risks associated with workload fluctuations.