Mental fatigue in drivers is one of the leading causes that give rise to traffic accidents. Electroencephalography (EEG)-based driving fatigue studies showed promising performance in fatigue monitoring. However, complex methodologies are not suitable for practical implementation. In our simulation-based setup that retained the constraints of real driving, we took a step closer to fatigue estimation in a practical scenario. We adopted a preprocessing pipeline with low computational complexity, which can be easily and practically implemented in real time. Moreover, regression-based continuous fatigue estimation was achieved using power spectral features in conjunction with time as the fatigue label. We sought to compare three regression models and three time windows to demonstrate their effects on the performance of fatigue estimation. Dynamic time warping was proposed as a new measure for evaluating the performance of fatigue estimation. The results derived from the validation of the proposed framework on 19 subjects showed that our proposed framework was promising toward practical implementation. Fatigue estimation by the support vector regression with radial basis function kernel and 5-s window length achieved the best performance. We also provided a comprehensive analysis on the spatial distribution of channels and frequency bands mostly contributing to fatigue estimation, which can inform the feature and channel reduction for real-time fatigue monitoring in practical driving. After reducing the number of electrodes by 75%, the proposed framework retained comparable performance in fatigue estimation. This paper demonstrates the feasibility and adaptability of our proposed framework in practical implementation of mental fatigue estimation.

中文翻译：

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基于回归的连续驾驶疲劳估计：走向实际实施

驾驶员精神疲劳是引发交通事故的主要原因之一。基于脑电图 (EEG) 的驾驶疲劳研究显示出在疲劳监测方面的良好性能。然而，复杂的方法不适合实际实施。在我们保留实际驾驶约束的基于模拟的设置中，我们更接近于实际场景中的疲劳估计。我们采用了计算复杂度低的预处理流水线，可以轻松实用地实时实现。此外，使用功率谱特征结合时间作为疲劳标签，实现了基于回归的连续疲劳估计。我们试图比较三个回归模型和三个时间窗口，以证明它们对疲劳估计性能的影响。动态时间规整被提出作为评估疲劳估计性能的新措施。从对 19 个主题的提议框架的验证得出的结果表明，我们提议的框架有望实现实际实施。使用径向基函数核和 5 s 窗口长度的支持向量回归进行疲劳估计取得了最佳性能。我们还对最有助于疲劳估计的信道和频段的空间分布进行了综合分析，可以为实际驾驶中的实时疲劳监测提供特征和信道减少信息。在将电极数量减少 75% 后，所提出的框架在疲劳估计方面保持了可比的性能。