ArticleBrain research bulletin2024
Mental workload evaluation using weighted phase lag index and coherence features extracted from EEG data.
Article in Brain research bulletin, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Prefrontal hemodynamic alterations during resting state and stroop interference in cognitively healthy older adults with high plasma pTau-217 levels.Neuroimage. Reports · 2026Article
- Multimodal deep learning for objective skill assessment in robot-assisted vesico-urethral anastomosis.Journal of robotic surgery · 2026Article
- When robots reshape teams: neurodynamic insights into taskwork and teamwork in search and rescue.Frontiers in neuroergonomics · 2026Article
- Functional brain network analysis in patients with upper-limb spasticity after stroke.Frontiers in human neuroscience · 2025Article
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Authors and funding
3 authors.
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Abstract
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
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