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Title: EEG-based cross-subject driver drowsiness recognition with an interpretable convolutional neural network
Authors: Cui, Jian
Lan, Zirui
Sourina, Olga
Muller-Wittig, Wolfgang
Keywords: Science::Biological sciences::Human anatomy and physiology
Engineering::Computer science and engineering
Issue Date: 2022
Source: Cui, J., Lan, Z., Sourina, O. & Muller-Wittig, W. (2022). EEG-based cross-subject driver drowsiness recognition with an interpretable convolutional neural network. IEEE Transactions On Neural Networks and Learning Systems.
Journal: IEEE Transactions on Neural Networks and Learning Systems 
Abstract: 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 state recognition from EEG signals. However, existing work mostly treats deep learning models as black-box classifiers, while what have been learned by the models and to which extent they are affected by the noise in EEG data are still underexplored. In this article, we develop a novel convolutional neural network combined with an interpretation technique that allows sample-wise analysis of important features for classification. The network has a compact structure and takes advantage of separable convolutions to process the EEG signals in a spatial-temporal sequence. Results show that the model achieves an average accuracy of 78.35% on 11 subjects for leave-one-out cross-subject drowsiness recognition, which is higher than the conventional baseline methods of 53.40%-72.68% and state-of-the-art deep learning methods of 71.75%-75.19%. Interpretation results indicate the model has learned to recognize biologically meaningful features from EEG signals, e.g., alpha spindles, as strong indicators of drowsiness across different subjects. In addition, we also explore reasons behind some wrongly classified samples with the interpretation technique and discuss potential ways to improve the recognition accuracy. Our work illustrates a promising direction on using interpretable deep learning models to discover meaningful patterns related to different mental states from complex EEG signals.
ISSN: 2162-237X
DOI: 10.1109/TNNLS.2022.3147208
Research Centres: Fraunhofer Singapore 
Rights: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:Fraunhofer Singapore Journal Articles

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