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

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, t...