Automated classification system for drowsiness detection using convolutional neural network and electroencephalogram

Abstract

Detecting drowsiness in drivers while driving is extremely important to avoid possible accidents and reduce the fatality rate due to drivers sleeping at the wheel. A real-time alert generation when the driver might possibly go into sleepy state is essential to safeguard any unwarranted incidents. Wearable sensors to monitor vehicle movement and camera-based systems to monitor driver behaviour are commonly used to detect driver drowsiness. Due to the fact that electroencephalogram (EEG) signals have the ability to monitor the mood of humans and are easily obtainable, many different EEG-based drowsiness detection systems have been proposed to date. In this study, a novel deep learning architecture based on a convolutional neural network (CNN) is proposed for automated drowsiness detection using a single-channel EEG signal. To improve the generalization performance of the proposed method, subject-wise, cross-subject-wise, and combined-subjects-wise validations have been employed. The whole of the work is carried over pre-recorded sleep state EEG data obtained from benchmarked dataset. The experimental results show a superior detection capability compared to the existing state–of–the–art drowsiness detection methods using single-channel EEG signals.