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Multimodal Deep Learning for Driver Monitoring: Integrating EEG and Vision for Robust Drowsiness Detection and Safety Enhancement
Road accidents remain a major global concern, with driver drowsiness and delayed reaction times recognized as key contributing factors. This thesis advances driver-monitoring research by developing multimodal approaches that integrate electroencephalography (EEG) and vision data to predict reaction
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