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.

A Review of EEG Signal Features and Their Application in Driver Drowsiness Detection Systems

“…The other three papers used CNN as a classification method. The highest accuracy achieved was 94% and the model used only raw data, without any pre-computed EEG signal features [ 173 ].…”

“…The reported accuracies for these deep learning models are in line with the accuracies of other models but, as we stated earlier, a direct comparison of the accuracies may lead to the wrong conclusions. Balam et al [ 173 ] provided a proper comparison of different approaches. The authors used a publicly available dataset, so they were able to provide a fair comparison of different approaches.…”

“…Reporting of validation with improper methodology can create overexpectation of the model performance, bad generalization on the unseen subjects, and can lead other researchers in the wrong direction. This effect is visible through the examples of papers that use validation on the unseen subjects, but also report about validation on the unseen parts of the signal in order to be comparable with existing research [173]. The fourth inclusion constraint defined in Section 1 was used to eliminate the papers that have a low probability of achieving good generalization due to a low number of participants.…”

“…Balam et al [ 173 ] used a convolutional neural network (CNN) for the classification based on the raw EEG signal from the Cz-Oz channel. They used data from the Sleep-EDF Expanded Database and their ground truth for drowsiness was the S1 sleep stage.…”

A Hybrid Model Utilizing Principal Component Analysis and Artificial Neural Networks for Driving Drowsiness Detection

“…In previous studies, electroencephalogram (EEG) and electrocardiogram (ECG) signals have been the most commonly used physiological signals for drowsiness detection. EEG, which is called the "golden standard" of drowsiness detection, can intuitively and effectively reflect the electrical activity information of the brain; thus, it has a wide range of applications in assessing the alertness of the brain [10][11][12]. ECG can be used to calculate heart rate variability (HRV), which refers to the tiny variations in successive heartbeat intervals.…”