背景:个体内变异性(IIV),衡量个人表现的方差,已被证明是神经功能的大脑反应指标。然而,精神疲劳如何调节IIV尚不清楚.因此,在单次试验水平上开发稳健的精神疲劳检测方法具有挑战性.
方法:基于长持续时间侧翼任务EEG数据集,我们从反应时间(RT)和事件相关电位(ERPs)的试验-试验潜伏期变化方面探讨了精神疲劳对IIV的调节.具体来说,使用残基迭代分解(RIDE)对潜伏期变化进行量化,以重建潜伏期校正的ERP。我们通过时间主成分分析(PCA)将重建的ERP与原始ERP进行了比较。此外,我们开发了单试验分类管道来检测精神疲劳水平的变化.
结果:我们发现与警戒状态相比,疲劳状态下的RT指标中的IIV增加。相同的ERP序列(N1,P2,N2,P3a,P3b,和慢波,或SW)使用PCA从原始和重建的ERP中分离,而由于IIV,发现原始和重建的ERP在分离的ERP的解释方差中存在差异。特别是,在RIDE后,在疲劳状态下检测到比警戒状态更强的N2。单次试验疲劳检测管道的可接受精度为73.3%。
方法:IIV与衰老和脑部疾病有关,作为一个延伸,我们的发现表明IIV是一种有效的精神疲劳指标.
结论:这项研究揭示了精神疲劳在行为和神经水平上对IIV的显着调节,并建立了强大的精神疲劳检测管道。
BACKGROUND: Intra-individual variability (IIV), a measure of variance within an individual\'s performance, has been demonstrated as metrics of brain responses for neural functionality. However, how mental fatigue modulates IIV remains unclear. Consequently, the development of robust mental fatigue detection methods at the single-
trial level is challenging.
METHODS: Based on a long-duration flanker task EEG dataset, the modulations of mental fatigue on IIV were explored in terms of response time (RT) and
trial-to-
trial latency variations of event-related potentials (ERPs). Specifically, latency variations were quantified using residue iteration decomposition (RIDE) to reconstruct latency-corrected ERPs. We compared reconstructed ERPs with raw ERPs by means of temporal principal component analysis (PCA). Furthermore, a single-
trial classification pipeline was developed to detect the changes of mental fatigue levels.
RESULTS: We found an increased IIV in the RT metric in the fatigue state compared to the alert state. The same sequence of ERPs (N1, P2, N2, P3a, P3b, and slow wave, or SW) was separated from both raw and reconstructed ERPs using PCA, whereas differences between raw and reconstructed ERPs in explained variances for separated ERPs were found owing to IIV. Particularly, a stronger N2 was detected in the fatigue than alert state after RIDE. The single-
trial fatigue detection pipeline yielded an acceptable accuracy of 73.3%.
METHODS: The IIV has been linked to aging and brain disorders, and as an extension, our finding demonstrates IIV as an efficient indicator of mental fatigue.
CONCLUSIONS: This
study reveals significant modulations of mental fatigue on IIV at the behavioral and neural levels and establishes a robust mental fatigue detection pipeline.