Abstract:
This research focuses on utilizing EEG brainwave data for the crucial task of detecting driver drowsiness a significant concern for road safety. We carefully curated the "Sleepy Driver EEG Brainwave Data"set, excluding less reliable metrics. Employing an ensemble approach, our robust classification model integrates Logistic Regression, K-Nearest Neighbors, Decision Tree, and Random Forest algorithms. The ensemble significantly improved prediction accuracy during real tests. The model demonstrated effectiveness in discerning between awake and asleep states, with rigorous hyper-parameter tuning identifying the optimal Random-Forest classifier. This study highlights the potential of EEG signal analysis and machine learning in establishing a dependable system for driver drowsiness detection. Beyond promising a substantial impact on road safety, our findings advocate for life-saving interventions and encourage safer driving practices, contributing to enhanced public well-being. © 2023 IEEE.
