An explainable machine learning framework for predicting driving states using electroencephalogram.

Journal: Medical engineering & physics
Published Date: May 9, 2025

Abstract

OBJECTIVES: Understanding drivers' cognitive load is essential for enhancing road safety, as cognitive demands fluctuate across different driving scenarios, potentially impacting performance, and safety, particularly for drivers with neurological disabilities. This study aims to predict driving states in healthy adult drivers using electroencephalogram (EEG) and machine learning (ML) models; and interpret the neural activity associated with each driving condition.

Authors

  • Iqram Hussain
    Department of Anesthesiology, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA.
  • Se-Jin Park
    Research Team for Health & Safety Convergence, Korea Research Institute of Standards and Science (KRISS), Daejeon 34113, Korea.
  • Akm Azad
    iThree Institute, University of Technology SydneyNSW2007Australia.
  • Salem Ali Alyami
    Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia; King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia.

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