the Intelligent Cancer Care Laboratory, the Department of Urology, Roswell Park Comprehensive Cancer Center in Buffalo, NY 14263, USA.
the Department of Animal Biosciences, University of Guelph, Guelph, Ontario N1G 2W1, Canada.
Brain Res Bull. 2024 Aug;214:110992. doi: 10.1016/j.brainresbull.2024.110992. Epub 2024 May 31.
Electroencephalogram (EEG) represents an effective, non-invasive technology to study mental workload. However, volume conduction, a common EEG artifact, influences functional connectivity analysis of EEG data. EEG coherence has been used traditionally to investigate functional connectivity between brain areas associated with mental workload, while weighted Phase Lag Index (wPLI) is a measure that improves on coherence by reducing susceptibility to volume conduction, a common EEG artifact. The goal of this study was to compare two methods of functional connectivity analysis, wPLI and coherence, in the context of mental workload evaluation. The study involved model development for mental workload domains and comparing their performance using coherence-based features, wPLI-based features, and a combination of both. Generalized linear mixed-effects model (GLMM) with the least absolute shrinkage and selection operator (LASSO) feature selection method was used for model development. Results indicated that the model developed using a combination of both feature types demonstrated improved predictive performance across all mental workload domains, compared to models that used each feature type individually. The R values were 0.82 for perceived task complexity, 0.71 for distraction, 0.91 for mental demand, 0.85 for physical demand, 0.74 for situational stress, and 0.74 for temporal demand. Furthermore, task complexity and functional connectivity patterns in different brain areas were identified as significant contributors to perceived mental workload (p-value<0.05). Findings showed the potential of using EEG data for mental workload evaluation which suggests that combination of coherence and wPLI can improve the accuracy of mental workload domains prediction. Future research should aim to validate these results on larger, diverse datasets to confirm their generalizability and refine the predictive models.
脑电图（EEG）是一种有效的、非侵入性的技术，可用于研究精神工作负荷。然而，容积传导是一种常见的脑电图伪迹，会影响脑电图数据的功能连接分析。传统上，脑电图相干性被用于研究与精神工作负荷相关的脑区之间的功能连接，而加权相位滞后指数（wPLI）是一种通过减少对容积传导的敏感性来改进相干性的度量，容积传导是一种常见的脑电图伪迹。本研究的目的是比较两种功能连接分析方法，即 wPLI 和相干性，在精神工作负荷评估中的应用。该研究涉及精神工作负荷领域的模型开发，并使用基于相干性的特征、基于 wPLI 的特征以及两者的组合来比较它们的性能。使用广义线性混合效应模型（GLMM）和最小绝对收缩和选择算子（LASSO）特征选择方法进行模型开发。结果表明，与单独使用每种特征类型的模型相比，使用两种特征类型组合开发的模型在所有精神工作负荷领域都表现出了更好的预测性能。R 值分别为 0.82 用于感知任务复杂性，0.71 用于分散注意力，0.91 用于精神需求，0.85 用于体力需求，0.74 用于情境压力，0.74 用于时间需求。此外，不同脑区的任务复杂性和功能连接模式被确定为感知精神工作负荷的重要贡献者（p 值<0.05）。研究结果表明，使用 EEG 数据进行精神工作负荷评估具有潜力，这表明相干性和 wPLI 的组合可以提高精神工作负荷领域预测的准确性。未来的研究应该旨在在更大、更多样化的数据集上验证这些结果，以确认其普遍性并改进预测模型。
