Mental fatigue deteriorates ability to perform daily activities − known as time-on-task (TOT) effect and becomes a common complaint in contemporary society. However, an applicable technique for fatigue detection/prediction is hindered due to substantial inter-subject differences in behavioural impairment and brain activity. Here, we developed a fully cross-validated, data-driven analysis framework incorporating multivariate regression model to explore the feasibility of utilizing functional connectivity (FC) to predict the fatigue-related behavioural impairment at individual level. EEG was recorded from 40 healthy adults as they performed a 30-min high-demanding sustained attention task. FC were constructed in different frequency bands using three widely-adopted methods (including coherence, phase log index (PLI), and partial directed coherence (PDC)) and contrasted between the most vigilant and fatigued states. The differences of individual FC ( diff (FC)) were considered as features; whereas the TOT slop across the course of task and the differences of reaction time ( $\Delta $ RT) between the most vigilant and fatigued states were chosen to represent behavioural impairments. Behaviourally, we found substantial inter-subject differences of impairments. Furthermore, we achieved significantly high accuracies for individualized prediction of behavioural impairments using diff (PDC). The identified top diff (PDC) features contributing to the individualized predictions were found mainly in theta and alpha bands. Further interrogation of diff (PDC) features revealed distinct patterns between the TOT slop and $\Delta $ RT prediction models, highlighting the complex neural mechanisms of mental fatigue. Overall, the current findings extended conventional brain-behavioural correlation analysis to individualized prediction of fatigue-related behavioural impairments, thereby moving a step forward towards development of applicable techniques for quantitative fatigue monitoring in real-world scenarios.

中文翻译：

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脑电图功能连接性预测精神疲劳期间的个人行为障碍。

精神疲劳会降低执行日常活动的能力-所谓的任务时间（TOT）效应，并成为当代社会的普遍抱怨。然而，由于行为障碍和大脑活动之间的实质性个体差异，阻碍了疲劳检测/预测的适用技术。在这里，我们开发了一个完全交叉验证的，数据驱动的分析框架，其中包含多元回归模型，以探讨利用功能连通性（FC）预测个体水平上与疲劳相关的行为障碍的可行性。从40名健康的成年人中记录了他们的脑电图，他们执行了30分钟的高要求持续关注任务。使用三种广泛采用的方法（包括相干性，相位对数索引（PLI），和部分定向相干性（PDC）），并在最警惕和最疲劳的状态之间进行对比。各个FC（ 差异（FC））被视为特征；而TOT在整个任务过程中以及反应时间的差异上都是倾斜的（ $ \ Delta $ 在最警觉状态和疲劳状态之间选择RT（RT）来代表行为障碍。行为上，我们发现受试者之间存在重大的损伤差异。此外，我们使用以下方法对行为障碍的个性化预测取得了很高的准确性差异 （PDC）。确定的顶部差异 （PDC）有助于个体化预测的特征主要出现在theta和alpha波段。进一步审问差异 （PDC）功能揭示了TOT斜率和 $ \ Delta $ RT预测模型，强调了精神疲劳的复杂神经机制。总体而言，当前的发现将传统的脑行为相关分析扩展到了与疲劳相关的行为障碍的个体化预测，从而朝着开发适用于现实情况中定量疲劳监测的技术迈出了一步。