EEG-connectivity: A fundamental guide and checklist for optimal study design and evaluation

TL;DR

This paper provides a comprehensive guide and checklist for designing and evaluating EEG connectivity studies, emphasizing standardization to improve comparability and robustness of results across different methodologies.

Contribution

It introduces a novel checklist and detailed recommendations to standardize EEG connectivity research and address methodological heterogeneity.

Findings

Factors like referencing and artefact removal influence connectivity estimates

Standardization can improve comparability of EEG connectivity studies

Checklist aids quality assessment and methodological consistency

Abstract

Brain connectivity can be estimated through a wide number of analyses applied to electroencephalographic (EEG) data. However, substantial heterogeneity in the implementation of connectivity methods exist. Heterogeneity in conceptualization of connectivity measures, data collection, or data pre-processing may be associated with variability in robustness of measurement. While it is difficult to compare the results of studies using different EEG connectivity measures, standardization of processing and reporting may facilitate the task. We discuss how factors such as referencing, epoch length and number, controls for volume conduction, artefact removal, and statistical control of multiple comparisons influence the EEG connectivity estimate for connectivity measures, and what can be done to control for potential confounds associated with these factors. Based on the results reported in previous literature, this article presents recommendations and a novel checklist developed for quality assessment of EEG connectivity studies. This checklist and its recommendations are made in an effort to draw attention to factors that may influence connectivity estimates and factors that need to be improved in future research. Standardization of procedures and reporting in EEG connectivity may lead to EEG connectivity studies to be made more synthesisable and comparable despite variations in the methodology underlying connectivity estimates.