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

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...

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Bibliographic Details
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
ISSN:	2162-237X, 2162-2388, 2162-2388
Online Access:	Get full text
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