Support-Conditioned Neural Fields for EEG Spatial Super-Resolution under Variable Electrode Topologies

EEG spatial super-resolution must reconstruct dense scalp signals from electrode sets whose number and spatial arrangement may vary across recordings. Existing approaches either rely on prescribed spatial interpolation, assume predefined channel layouts, or achieve coordinate flexibility without explicitly adapting the reconstruction function to the temporal and spectral characteristics of the current recording. We propose ScalpINR, an amortized support-conditioned neural field that directly instantiates a recording-specific coordinate-to-signal function from an unordered set of observed EEG signals and electrode coordinates. The observed support generates recording-dependent temporal and spectral controls for a shared coordinate decoder, while a dual-support objective encourages compatible fields to be inferred from different electrode subsets of the same recording. Once conditioned on the available electrodes, ScalpINR can be queried at missing or training-unseen electrode coordinates without test-time optimization. Experiments on SEED, ESAA, and BCI2000 demonstrate strong performance under both random missing-channel reconstruction and strict held-out-electrode evaluation. On ESAA, ScalpINR reduces NMSE by 37.5% relative to the strongest baseline when all non-held-out electrodes are available as support.