  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.
