How Much Does Electrode Density Matter? A Multilevel Evaluation from EEG Signals to Cortical Networks

Abstract

Background High-density electroencephalography (EEG) improves spatial sampling but entails greater acquisition cost, preparation time, participant burden, and computational demand. However, the extent to which electrode reduction affects successive levels of EEG analysis from global signal properties to cortical network inference remains insufficiently understood. Methods We systematically subsampled two independent resting-state EEG datasets comprising 120 children recorded with 128 channels and 98 adults recorded with 256 channels. Density-dependent effects were assessed across 128-, 256-, 64-, 32-, and 19-channel configurations using a multilevel framework encompassing signal representation and quality, global and regional spectral power, EEG microstates, scalp-level effective connectivity, traveling waves, and source-level cortical hub topology. Results Sensitivity to electrode reduction increased with the spatial specificity of the outcome. Global relative spectral power and explained variance were comparatively stable across configurations. In contrast, signal-to-noise ratio, entropy, reconstruction error, regional spectral distributions, traveling-wave estimates, effective connectivity, and microstate dynamics became progressively more density-sensitive. Although canonical microstate classes remained broadly identifiable at lower densities, their global explained variance, temporal parameters, and transition structure were altered. Source-level hub identity and centrality ranking showed the greatest vulnerability, with markedly reduced cross-density agreement at 32 and 19 channels. Across both datasets, several spatially dependent measures showed a transition around 64 channels, whereas estimates obtained with 128 and 256 channels were generally more similar. Conclusions Electrode density affects EEG outcomes according to the spatial scale of the intended inference. Sparse montages may preserve broad global summaries but are less reliable for regional, spatiotemporal, and source-network analyses. Under the resting-state conditions examined here, approximately 64 channels represented a pragmatic transition for several spatially dependent measures. This value should be interpreted as context-specific guidance rather than a universal threshold. Highlights Electrode-density effects increase with the spatial specificity of the EEG measure. Global spectral power and explained variance remain comparatively robust at low density. Microstate classes persist at low density, but temporal dynamics and syntax are distorted. Connectivity, traveling waves, and cortical hub inference are more sensitive to sparse sampling. Across two independent datasets, many spatially dependent measures deteriorate below approximately 64 channels.