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ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Perception Science
Volume 18 - 2024 |
doi: 10.3389/fnins.2024.1508747
This article is part of the Research Topic Current Research and Future Development of Neuropsychology View all 7 articles
ID3RSNet: Cross-Subject Driver Drowsiness Detection from Raw Single-Channel EEG with an Interpretable Residual Shrinkage Network
Provisionally accepted- 1 Chongqing University of Posts and Telecommunications, Chongqing, Chongqing, China
- 2 Southwest University, Chongqing, China
- 3 Lingnan University, Tuen Mun, Hong Kong, SAR China
Accurate monitoring of drowsy driving through Electroencephalography (EEG) can effectively reduce traffic accidents. Developing a calibration-free drowsiness detection system with single-channel EEG alone is very challenging due to the non-stationarity of EEG signals, the heterogeneity among different individuals and the relatively parsimonious compared to muti-channel EEG. Although deep learning-based approaches can effectively decode EEG signals, most deep learning models lack interpretability due to their black-box nature. To address these issues, we propose a novel interpretable residual shrinkage network, namely ID3RSNet, for cross-subject driver drowsiness detection using single-channel EEG signals. First, a base feature extractor is employed to extract the essential features of EEG frequencies; to enhance the discriminative feature learning ability, the residual shrinkage building unit with attention mechanism is adopted to perform adaptive feature recalibration and soft threshold desonising inside the residual network is further applied to achieve automatic feature extraction. In addition, a fully connected layer with weight freezing is utilized to effectively suppress the negative influence of neurons on the model classification. With the global average pooling (GAP) layer incorporated in the residual shrinkage network structure, we introduce an EEG-based Class Activation Map (ECAM) interpretable method to enable visualization analysis of sample-wise learned patterns to effectively explain the model decision. Extensive experimental results demonstrate that the proposed method achieves the superior classification performance, and has found neurophysiologically reliable evidence of classification.
Keywords: Single-channel EEG, Drowsiness detection, residual shrinkage network, Attention, Interpretability
Received: 09 Oct 2024; Accepted: 24 Dec 2024.
Copyright: © 2024 Feng, Guo and Kwong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhongyuan Guo, Southwest University, Chongqing, China
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