Bidirectional Temporal Dynamics Modeling for EEG-based Driving Fatigue Recognition

• URL: http://arxiv.org/abs/2602.14071v1
• Date: Sun, 15 Feb 2026 09:42:25 GMT
• Title: Bidirectional Temporal Dynamics Modeling for EEG-based Driving Fatigue Recognition
• Authors: YipTin Po, Jianming Wang, Yutao Miao, Jiayan Zhang, Yunxu Zhao, Xiaomin Ouyang, Zhihong Li, Nevin L. Zhang,
• Abstract summary: Driving fatigue is a major contributor to traffic accidents and poses a serious threat to road safety.<n>EEG-based fatigue recognition is hindered by strong non-stationarity and asymmetric neural dynamics.<n>We propose DeltaGateNet, a novel framework that explicitly captures Bidirectional temporal dynamics for EEG-based driving fatigue recognition.
• Score: 8.553069379167221
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Driving fatigue is a major contributor to traffic accidents and poses a serious threat to road safety. Electroencephalography (EEG) provides a direct measurement of neural activity, yet EEG-based fatigue recognition is hindered by strong non-stationarity and asymmetric neural dynamics. To address these challenges, we propose DeltaGateNet, a novel framework that explicitly captures Bidirectional temporal dynamics for EEG-based driving fatigue recognition. Our key idea is to introduce a Bidirectional Delta module that decomposes first-order temporal differences into positive and negative components, enabling explicit modeling of asymmetric neural activation and suppression patterns. Furthermore, we design a Gated Temporal Convolution module to capture long-term temporal dependencies for each EEG channel using depthwise temporal convolutions and residual learning, preserving channel-wise specificity while enhancing temporal representation robustness. Extensive experiments conducted under both intra-subject and inter-subject evaluation settings on the public SEED-VIG and SADT driving fatigue datasets demonstrate that DeltaGateNet consistently outperforms existing methods. On SEED-VIG, DeltaGateNet achieves an intra-subject accuracy of 81.89% and an inter-subject accuracy of 55.55%. On the balanced SADT 2022 dataset, it attains intra-subject and inter-subject accuracies of 96.81% and 83.21%, respectively, while on the unbalanced SADT 2952 dataset, it achieves 96.84% intra-subject and 84.49% inter-subject accuracy. These results indicate that explicitly modeling Bidirectional temporal dynamics yields robust and generalizable performance under varying subject and class-distribution conditions.

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