EEG_DMNet: A Deep Multi-Scale Convolutional Neural Network for Electroencephalography-Based Driver Drowsiness Detection
Hanan Bin Obaidan, Muhammad Hussain, Reham Al-Majed
Abstract
Drowsy driving is one of the major causes of traffic accidents, injuries, and deaths on roads worldwide. One of the best physiological signals that are useful in detecting a driver’s drowsiness is electroencephalography (EEG), a kind of brain signal that directly measures neurophysiological activities in the brain and is widely utilized for brain–computer interfaces (BCIs). However, designing a drowsiness detection method using EEG signals is still challenging because of their non-stationary nature. Deep learning, specifically convolutional neural networks (CNNs), has recently shown promising results in driver’s drowsiness. However, state-of-the-art CNN-based methods extract features sequentially and discard multi-scale spectral-temporal features, which are important in tackling the non-stationarity of EEG signals. This paper proposes a deep multi-scale convolutional neural network (EEG_DMNet) for driver’s drowsiness detection that learns spectral-temporal features. It consists of two main modules. First, the multi-scale spectral-temporal features are extracted from EEG trials using 1D temporal convolutions. Second, the spatial feature representation module calculates spatial patterns from the extracted multi-scale features using 1D spatial convolutions. The experimental results on the public domain benchmark SEED-VIG EEG dataset showed that it learns discriminative features, resulting in an average accuracy of 97.03%, outperforming the state-of-the-art methods that used the same dataset. The findings demonstrate that the proposed method effectively and efficiently detects drivers’ drowsiness based on EEG and can be helpful for safe driving.