NeuroX-EEG: Scalp Field Inpainting for Zero-Shot EEG Super-Resolution

EEG electrodes sparsely sample a smooth spatial field on the scalp surface. Existing learning-based super-resolution methods typically reconstruct a fixed set of target channels repeatedly supervised during training, leaving their ability to recover unseen electrode locations unclear. We introduce NeuroX-EEG for zero-shot reconstruction at locations excluded from both conditioning and supervision, a protocol that tests whether reconstruction generalizes over scalp position. Unlike dataset-level zero-shot transfer, this protocol holds out electrode sites within the same recordings. NeuroX-EEG performs diffusion-based inpainting on a 2D scalp grid, where convolution exploits the local smoothness implied by volume conduction, and incorporates pretrained EEG foundation-model representations to supply broader temporal, spectral, and cross-channel structure. Across three datasets spanning 62- and 19-channel montages and multiple observation densities, NeuroX-EEG achieves the lowest NMSE among the evaluated methods under our zero-shot protocol in six of seven dataset–scale settings. Region-wise zero-shot evaluation shows the clearest gains for band-shaped temporal and parietal holdouts, and the reconstructed signals yield higher mean downstream brain–computer interface (BCI) decoding performance than low-density input under the evaluated protocol.