Electroencephalographic Connectivity: A Fundamental Guide and Checklist for Optimal Study Design and Evaluation

Brain connectivity can be estimated through many analyses applied to electroencephalography (EEG) data. However, substantial heterogeneity in the implementation of connectivity methods exists. Heterogeneity in conceptualization of connectivity measures, data collection, or data preprocessing 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, artifact 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 being made more synthesizable and comparable despite variations in the methodology underlying connectivity estimates.

Automated summary

Brain connectivity can be estimated through analysis of EEG data, but there is substantial heterogeneity in the implementation of connectivity methods. Standardization of processing and reporting can improve comparability and synthesizability of EEG connectivity studies. This article discusses factors influencing connectivity estimates and provides recommendations and a checklist for quality assessment.