Driver Drowsiness Detection Using Support Vector Machine (SVM) Model from Brain Alpha Wave and GUI Implementation

Driver drowsiness is a major cause of road accidents, especially for long-distance drivers, making monitoring and intervention essential. This paper proposes a novel method for detecting driver drowsiness using a Support Vector Machine (SVM) model and a graphical user interface (GUI). We use electroencephalogram (EEG) signals to measure brain alpha waves indicating drowsiness, extracting features to train an SVM classifier to distinguish between drowsy and alert states. The GUI displays the driver’s drowsiness level and provides visual feedback. Evaluated on 12 drivers, our method achieves 99% accuracy, outperforming existing methods, and can be applied to real-time detection systems.