An explainable machine learning framework for predicting driving states using electroencephalogram
This study demonstrates the potential of EEG-based features combined with interpretable machine learning for driver states prediction. The approach offers a foundation for personalized, responsive driving assistance systems at improving road safety and preventing disability by monitoring to drivers' cognitive states.
Authors
- AKM Azad (ORCID: https://orcid.org/0000-0002-5251-2214)
- Salem A. Alyami (ORCID: https://orcid.org/0000-0002-5507-9399)
- Iqram Hussain (ORCID: https://orcid.org/0000-0002-5183-7631)
- Se Jin Park (ORCID: https://orcid.org/0000-0002-1618-5906)
Institutions
- Cornell University (US)
- Imam Mohammad ibn Saud Islamic University (SA)
- Korea Research Institute of Standards and Science (KR)
Publication Details
- Journal
- Medical Engineering & Physics
- Published
- 2025-05-09
- DOI
- https://doi.org/10.1016/j.medengphy.2025.104355
- Citations
- 3
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 4.29
Funders
- King Salman Center for Disability Research