Estimating sensor-space EEG connectivity: Identifying best performing methods for functional connectivity in simulated data

Objective: Functional brain connectivity (FC) can be estimated using electroencephalography (EEG). However, there is considerable variability across studies in the FC measures used and in data (pre-)processing methods, leading to difficulties comparing and amalgamating results between studies. Thus, standardisation of EEG (pre-)processing for the measurement and reporting of FC is needed. We aimed to assess differences in FC estimates produced by different settings across multiple EEG pre-processing steps, (including re-referencing and epoching) to validate a reliable methodological pipeline for assessing EEG-FC in simulated EEG data. Methods: We simulated EEG-FC data where the ‘ground truth’ of the connections is known and compared estimates of FC from this ground truth data across multiple FC measures and variations in multiple pre-processing steps. Results: Our results indicated that pre-processing steps that included segmenting the data into 40 or more epochs that were 6 s or more in length provided the most accurate estimation of the simulated FC. With regards to the data re-referencing, the Reference Electrode Standardization Technique or the common average re-referencing appeared best when used in conjunction with imaginary coherence and weighted phase lag index metrics. However, the magnitude-squared coherence FC measure performed best with the Current Source Density reference free techniques. Conclusions & Significance: Our paper provides an evidence-base for the influence of referencing, epoch length and number, controls for volume conduction, and different FC metrics on EEG-FC measurement. Using this evidence, we present an initial and promising account of the best performing (pre-)processing choices for robust EEG-FC assessment.