Driver fatigue has caused numerous vehicle crashes and traffic injuries. Exploring the fatigue mechanism and detecting fatigue state are of great significance for preventing traffic accidents, and further lessening economic and societal loss. Due to the objectivity of EEG signals and the availability of EEG acquisition equipment, EEG-based fatigue detection task has raised great attention in recent years. Although there exist various methods for this task, the study of fatigue mechanism and detection of fatigue state still remain much to be explored. To investigate these problems, a multivariate weighted ordinal pattern transition (MWOPT) network is proposed in this paper. To be specific, a simulated driving experiment was first conducted to obtain the EEG signals of subjects in alert state and fatigue state respectively. Then the MWOPT network is constructed based on a novel Shannon entropy. To probe into the mechanism underlying fatigue behavior, the small-worldness index is extracted from the generated MWOPT network. Furthermore, the nodal degree index is input into a classifier to distinguish the fatigue state from alert state. The obtained high accuracy indicates the effectiveness of the proposed network for EEG-based fatigue detection. Besides, four nodes are found to play an important role in identifying fatigue state. These results suggest that the proposed method enables to analyze nonlinear multivariate time series and investigate the driving fatigue behavior.

中文翻译：

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用于从 EEG 信号中表征驾驶员疲劳行为的多元加权序数模式转换网络

驾驶员疲劳导致多起车祸和交通伤害。探索疲劳机理，检测疲劳状态，对于预防交通事故，进一步减少经济和社会损失具有重要意义。由于脑电信号的客观性和脑电采集设备的可用性，基于脑电的疲劳检测任务近年来备受关注。尽管有多种方法可以完成这项任务，但疲劳机理的研究和疲劳状态的检测仍有许多待探索。为了研究这些问题，本文提出了一种多元加权有序模式转换（MWOPT）网络。具体来说，首先进行了模拟驾驶实验，分别获得了处于警觉状态和疲劳状态的受试者的脑电信号。然后基于新的香农熵构建 MWOPT 网络。为了探究疲劳行为背后的机制，从生成的 MWOPT 网络中提取了小世界指数。此外，将节点度指数输入到分类器中，以区分疲劳状态和警戒状态。获得的高精度表明了所提出的基于脑电图的疲劳检测网络的有效性。此外，发现四个节点在识别疲劳状态方面发挥着重要作用。这些结果表明，所提出的方法能够分析非线性多元时间序列并研究驾驶疲劳行为。将节点度指标输入分类器，以区分疲劳状态和警戒状态。获得的高精度表明了所提出的基于脑电图的疲劳检测网络的有效性。此外，发现四个节点在识别疲劳状态方面发挥着重要作用。这些结果表明，所提出的方法能够分析非线性多元时间序列并研究驾驶疲劳行为。将节点度指标输入分类器，以区分疲劳状态和警戒状态。获得的高精度表明了所提出的基于脑电图的疲劳检测网络的有效性。此外，发现四个节点在识别疲劳状态方面发挥着重要作用。这些结果表明，所提出的方法能够分析非线性多元时间序列并研究驾驶疲劳行为。