Support Sampling for Montage-Agnostic EEG Source Imaging

EEG source imaging (ESI) aims to reconstruct cortical activity from scalp measurements that mix signals generated by multiple sources. This represent a notable ill-posed inverse problem since distinct source configurations can produce similar EEG. Classical methods use a supplied leadfield and explicit assumptions to select a reconstruction, Bayesian methods use a leadfield to infer source uncertainty for each recording, and learned imagers are fast but often return a single estimate for a fixed electrode layout. We introduce a geometry-conditioned imager that estimates parcel waveforms and autoregressively samples sets of active parcels of variable size. By encoding EEG measurements and electrode coordinates in an internal representation, the same model processes different montages without a supplied leadfield, which it predicts directly from geometry. On held-out 256-channel synthetic data, our deterministic estimate improves on the previous state of the art, reducing median localization error by 13.4% to 9.7 mm and increasing AUPRC from 0.749 to 0.787. On real EEG with known intracranial stimulation sites, the full autoregressive model reduces median peak-to-site distance by 15.5% to 22.3 mm relative to the best-performing baseline and achieves a stimulated-parcel AUPRC of 0.090. Trained jointly on four electrode layouts, the imager also generalizes to unseen 21-, 76-, and 256-channel montages, achieving median localization errors of 10.4–16.5 mm compared with 10.6 mm across seen layouts.