RT Journal Article
SR Electronic
T1 SiamEEGNet: Siamese Neural Network-Based EEG Decoding for Drowsiness Detection
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2023.10.23.563513
DO 10.1101/2023.10.23.563513
A1 Chang, Li-Jen
A1 Chen, Hsi-An
A1 Chang, Chin
A1 Wei, Chun-Shu
YR 2023
UL http://biorxiv.org/content/early/2023/10/23/2023.10.23.563513.abstract
AB Recent advancements in deep-learning have significantly enhanced EEG-based drowsiness detection. However, most existing methods overlook the importance of relative changes in EEG signals compared to a baseline, a fundamental aspect in conventional EEG analysis including event-related potential and time-frequency spectrograms. We herein introduce SiamEEGNet, a Siamese neural network architecture designed to capture relative changes between EEG data from the baseline and a time window of interest. Our results demonstrate that SiamEEGNet is capable of robustly learning from high-variability data across multiple sessions/subjects and outperforms existing model architectures in cross-subject scenarios. Furthermore, the model’s interpretability associates with previous findings of drowsiness-related EEG correlates. The promising performance of SiamEEGNet highlights its potential for practical applications in EEG-based drowsiness detection. We have made the source codes available at http://github.com/CECNL/SiamEEGNet.Competing Interest StatementThe authors have declared no competing interest.