EEG-Based Cross-Subject Driver Drowsiness Recognition With an Interpretable Convolutional Neural Network

• Published in: IEEE transaction on neural networks and learning systems Vol. 34; no. 10; pp. 7921 - 7933
• Main Authors: Cui, Jian, Lan, Zirui, Sourina, Olga, Muller-Wittig, Wolfgang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; Automobiles; Brain modeling; Convolutional neural network (CNN); Convolutional neural networks; Deep learning; depthwise separable convolution; driver drowsiness recognition; Driver fatigue; Drowsiness; EEG; Electroencephalography; electroencephalography (EEG); interpretable deep learning; interpretation techniques; Machine learning; Monitoring; Neural networks; Recognition; Sleepiness; Teaching methods; Vehicles

In the context of electroencephalogram (EEG)-based driver drowsiness recognition, it is still challenging to design a calibration-free system, since EEG signals vary significantly among different subjects and recording sessions. Many efforts have been made to use deep learning methods for mental sta...