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A Novel Mutual Information Based Feature Set for Drivers' Mental Workload Evaluation Using Machine Learning.

Mir Riyanul Islam1, Shaibal Barua1, Mobyen Uddin Ahmed1

  • 1School of Innovation, Design and Engineering, Mälardalen University, Högskoleplan 1, 722 20 Västerås, Sweden.

Brain Sciences
|August 23, 2020
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Summary
This summary is machine-generated.

This study introduces a new method using mutual information to fuse electroencephalography and vehicle signals for evaluating driver mental workload. This approach enhances the usability and accuracy of monitoring driver cognitive states in vehicles.

Keywords:
electroencephalographyfeature extractionmachine learningmental workloadmutual informationvehicular signal

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Area of Science:

  • Neuroscience and Cognitive Science
  • Automotive Engineering
  • Signal Processing

Background:

  • Electroencephalography (EEG) offers objective mental workload assessment but lacks in-vehicle usability.
  • Current methods for monitoring in-vehicle driver mental workload are limited.
  • There is a need for reliable and practical methods to assess cognitive load in drivers.

Purpose of the Study:

  • To develop a novel feature set for in-vehicle driver mental workload evaluation.
  • To fuse electroencephalography (EEG) and vehicular signals using mutual information.
  • To assess the reliability and performance of the developed features in predicting and classifying mental workload.

Main Methods:

  • Acquired EEG and vehicular signals during a real driving experiment.
  • Constructed a feature set based on mutual information between EEG and vehicular signals.
  • Employed machine learning models for mental workload score prediction and classification tasks.
  • Compared the performance of the fused features against EEG-only features.

Main Results:

  • The proposed mutual information-based feature set achieved a lowest mean absolute error of 0.09 for mental workload score prediction.
  • The highest accuracy achieved for classifying mental workload was 94%.
  • The novel features demonstrated superior performance compared to EEG-only features.

Conclusions:

  • The developed mutual information-based features are effective for classifying and monitoring in-vehicle driver mental workload.
  • The proposed methodology offers a practical solution for assessing driver cognitive states.
  • This approach has the potential to improve driver safety and performance monitoring.