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.

翻译：驾驶疲劳是导致交通事故的主要原因，对道路安全构成严重威胁。脑电图（EEG）为神经活动提供了直接测量手段，然而基于EEG的疲劳识别受限于强烈的非平稳性和不对称的神经动态。为应对这些挑战，我们提出了DeltaGateNet，一种新颖的框架，用于显式捕捉基于EEG的驾驶疲劳识别中的双向时序动态。我们的核心思想是引入一个双向Delta模块，该模块将一阶时序差分分解为正负分量，从而能够显式建模不对称的神经激活与抑制模式。此外，我们设计了一个门控时序卷积模块，通过深度时序卷积和残差学习来捕捉每个EEG通道的长期时序依赖关系，在保持通道特异性的同时增强了时序表示的鲁棒性。在公开的SEED-VIG和SADT驾驶疲劳数据集上，通过被试内和被试间两种评估设置进行的广泛实验表明，DeltaGateNet始终优于现有方法。在SEED-VIG上，DeltaGateNet取得了81.89%的被试内准确率和55.55%的被试间准确率。在平衡的SADT 2022数据集上，其被试内和被试间准确率分别达到96.81%和83.21%；而在不平衡的SADT 2952数据集上，则分别达到96.84%和84.49%。这些结果表明，显式建模双向时序动态能够在不同的被试和类别分布条件下产生鲁棒且可泛化的性能。