Real-Time EEG-Based Driver Drowsiness Detection Based on Convolutional Neural Network With Gumbel-Softmax Trick

• Published in: IEEE sensors journal Vol. 25; no. 1; pp. 1860 - 1871
• Main Authors: Feng, Weibin, Wang, Xiaoping, Xie, Jialan, Liu, Wanqing, Qiao, Yinghao, Liu, Guangyuan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; Brain modeling; Channels; Deep learning; Driver drowsiness detection; Driver fatigue; Electroencephalography; electroencephalography (EEG); Feature extraction; Graphical user interface; graphical user interface (GUI); Gumbel-Softmax trick; Machine learning; Neural networks; Neurons; Real time; Real-time systems; Reviews; Sleepiness; Target tracking; Traffic accidents; Training; Vectors
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2024.3492176

Nowadays, severe traffic accidents attributed to driver drowsiness have become increasingly frequent, prompting a widespread concern among researchers in electroencephalography (EEG)-based driver drowsiness detection. However, due to the significant differences in EEG signals between participants, the prevalence of redundant information in multichannel EEG data, and the computational burden in combining channel selection with neural networks, achieving an accurate and efficient real-time driver drowsiness recognition remains challenging. To overcome these limitations, this article proposes a novel deep learning framework that utilizes a separable convolutional neural network (CNN) to mine the intricate spatiotemporal information in EEG signals, combined with the channel selection layer to jointly optimize EEG channels and network parameters. This layer employs an efficient embedded Gumbel-Softmax technique for discrete sampling and differentiable approximation. To prevent the introduction of duplicate channels, we impose penalties on the row sums of the selection matrix to encourage the selection neurons to learn distinct channels, enabling the neural network to train in an end-to-end manner. The proposed model achieves an average accuracy of 80.84% and an F1 score of 79.65% in cross-subject drowsiness identification for 11 subjects on the publicly available sustained-attention driving task dataset. Compared to the results of recent relevant works, our model exhibits superior performance, surpassing state-of-the-art (SOTA) deep learning methods by 1.47%. Furthermore, building upon the model's advantages, we have further actualized a real-time driver drowsiness detection graphical user interface (GUI), providing a practical reference for real-world applications.