Reliable evaluation of functional connectivity and graph theory measures in source-level EEG: How many electrodes are enough?

Objective: Using EEG to characterise functional brain networks through graph theory has gained signifi-cant interest in clinical and basic research. However, the minimal requirements for reliable measures remain largely unaddressed. Here, we examined functional connectivity estimates and graph theory met-rics obtained from EEG with varying electrode densities.Methods: EEG was recorded with 128 electrodes in 33 participants. The high-density EEG data were sub-sequently subsampled into three sparser montages (64, 32, and 19 electrodes). Four inverse solutions, four measures of functional connectivity, and five graph theory metrics were tested.Results: The correlation between the results obtained with 128-electrode and the subsampled montages decreased as a function of the number of electrodes. As a result of decreased electrode density, the net-work metrics became skewed: mean network strength and clustering coefficient were overestimated, while characteristic path length was underestimated. Conclusions: Several graph theory metrics were altered when electrode density was reduced. Our results suggest that, for optimal balance between resource demand and result precision, a minimum of 64 elec-trodes should be utilised when graph theory metrics are used to characterise functional brain networks in source-reconstructed EEG data.Significance: Characterisation of functional brain networks derived from low-density EEG warrants care-ful consideration.(c) 2023 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).