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Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

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

Somayeh B Shafiei¹, Saeed Shadpour², Ambreen Shafqat¹

¹the Intelligent Cancer Care Laboratory, the Department of Urology, Roswell Park Comprehensive Cancer Center in Buffalo, NY 14263, USA.

Brain Research Bulletin

|June 2, 2024

Summary

Combining EEG coherence and weighted Phase Lag Index (wPLI) improves mental workload prediction. This approach enhances accuracy in evaluating cognitive load across various domains, outperforming individual methods.

Keywords:

Coherence Robot-assisted surgery Surgical simulator Weighted phase lag index

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Area of Science:

Neuroscience
Cognitive Science
Biomedical Engineering

Background:

Electroencephalogram (EEG) is a non-invasive tool for studying mental workload.
Volume conduction is a significant artifact in EEG data, affecting functional connectivity analysis.
Traditional EEG coherence is susceptible to volume conduction, while weighted Phase Lag Index (wPLI) offers improved robustness.

Purpose of the Study:

To compare the efficacy of wPLI and coherence in analyzing functional connectivity for mental workload assessment.
To develop and evaluate predictive models for various mental workload domains using these connectivity measures.

Main Methods:

Functional connectivity was analyzed using both EEG coherence and wPLI.
Generalized linear mixed-effects models (GLMM) with LASSO feature selection were employed for model development.
Models were compared based on predictive performance using coherence-based, wPLI-based, and combined features.

Main Results:

A combined model using both coherence and wPLI features showed superior predictive performance across all mental workload domains (R² values ranging from 0.71 to 0.91).
Task complexity and specific brain functional connectivity patterns were significant predictors of perceived mental workload (p<0.05).
The combined approach significantly improved the accuracy of predicting various mental workload dimensions.

Conclusions:

The integration of EEG coherence and wPLI enhances the accuracy of mental workload prediction.
This combined method holds promise for more reliable EEG-based mental workload evaluation.
Future research should focus on validating these findings in larger, diverse populations to ensure generalizability.