Driver drowsiness is identified as a critical factor in road accidents, necessitating robust detection systems to enhance road safety. This study proposes a driver drowsiness detection system, DrowzEE-G-Mamba, that combines Electroencephalography (EEG) with State Space Models (SSMs). EEG data, known for its sensitivity to alertness, is used to model driver state transitions between alert and drowsy. Compared to traditional methods, DrowzEE-G-Mamba achieves significantly improved detection rates and reduced false positives. Notably, it achieves a peak accuracy of 83.24% on the SEED-VIG dataset, surpassing existing techniques. The system maintains high accuracy across varying complexities, making it suitable for real-time applications with limited resources. This robustness is attributed to the combination of channel-split, channel-concatenation, and channel-shuffle operations within the architecture, optimizing information flow from EEG data. Additionally, the integration of convolutional layers and SSMs facilitates comprehensive analysis, capturing both local features and long-range dependencies in the EEG signals. These findings suggest the potential of DrowzEE-G-Mamba for enhancing road safety through accurate drowsiness detection. It also paves the way for developing powerful SSM-based AI algorithms in Brain-Computer Interface applications.

翻译：驾驶员疲劳被认定为导致道路事故的关键因素，因此需要可靠的检测系统以提升道路安全。本研究提出了一种驾驶员疲劳检测系统DrowzEE-G-Mamba，该系统将脑电图与状态空间模型相结合。脑电图数据因其对警觉性的敏感性而被用于建模驾驶员在警觉与疲劳状态之间的转换。与传统方法相比，DrowzEE-G-Mamba显著提高了检测率并降低了误报率。值得注意的是，该系统在SEED-VIG数据集上达到了83.24%的峰值准确率，超越了现有技术。该系统在不同复杂度下均保持高准确率，使其适用于资源有限的实时应用场景。这种鲁棒性得益于架构中通道分割、通道拼接和通道混洗操作的结合，优化了来自脑电图数据的信息流。此外，卷积层与状态空间模型的集成促进了综合分析，能够捕捉脑电信号中的局部特征和长程依赖关系。这些发现表明DrowzEE-G-Mamba有潜力通过精确的疲劳检测来提升道路安全。该研究也为在脑机接口应用中开发强大的基于状态空间模型的人工智能算法铺平了道路。