Hybrid EEG Signal Processing Framework for Driver Drowsiness Detection Using QWT, EMD, and Bayesian Optimized SVM

Drowsiness among drivers poses an urgent safety problem in road transportation because it creates serious risks to both drivers and their passengers. Carrying out prolonged driving operations creates a mind state between full wakefulness and sleep. The research presents a new supervised learning approach to evaluate sleep stages which helps detect driver sleepiness and trigger prompt alerts. A deep feature extraction system based on QWT with time-frequency scalogram and EMD artifact removal generates features from EEG signals. The system uses evaluation of decomposition levels' energy measurements to build Energy Vectors which lead to the processing of each EEG signal into feature vectors. Data classification in this research occurs through implementation of the BO-SVM classifier which features Bayesian optimization. The BO-SVM classifier delivers exceptional accuracy results because it reaches 99.17% maximum accuracy beyond typical SVM classifier performance levels. This paper combines signal processing methods with machine learning techniques which result in advanced EEG data-based sleep stage diagnosis to enhance driver safety.