Mental workload evaluation using weighted phase lag index and coherence features extracted from EEG data.

Autor: Shafiei SB; the Intelligent Cancer Care Laboratory, the Department of Urology, Roswell Park Comprehensive Cancer Center in Buffalo, NY 14263, USA. Electronic address: Somayeh.BesharatShafiei@RoswellPark.org., Shadpour S; the Department of Animal Biosciences, University of Guelph, Guelph, Ontario N1G 2W1, Canada., Shafqat A; the Intelligent Cancer Care Laboratory, the Department of Urology, Roswell Park Comprehensive Cancer Center in Buffalo, NY 14263, USA.
Jazyk: angličtina
Zdroj: Brain research bulletin [Brain Res Bull] 2024 Aug; Vol. 214, pp. 110992. Date of Electronic Publication: 2024 May 31.
DOI: 10.1016/j.brainresbull.2024.110992
Abstrakt: 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 2 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.
Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest.
(Copyright © 2024. Published by Elsevier Inc.)
Databáze: MEDLINE
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