Drowsiness detection using EEG and ECG signals

Abstract

Numerous studies show that driver drowsiness is one of the significant contributors which lead to fatal accidents. Regard to these problems; many hybrid measure detections is proposed using the physiological, behavioural as well as vehicle based. Nevertheless, the proposed model that associates behavioural-based and vehicle-based measure bounce to have a less significant impact on predicting drowsiness as the prediction is based on sensory located closed to the driver. Furthermore, finding drowsiness cannot rely on one single measure of signals. Therefore, this project aimed to produce a hybrid measure detection using multimodal bio signals as it is a gold standard and precisely in evaluating the human body signals. Utilizing the ULg multimodality drowsiness database (called DROZY) database, the electroencephalogram (EEG) and electrocardiogram (ECG) signals have been extracted to determine the drowsiness. k-nearest neighbor (KNN) produces better accuracy than support vector machine (SVM) on both datasets.