CANDLE: Cortical Null-Space Decomposition for Noninvasive Brain Source Imaging

Electrophysiological source imaging (ESI) aims to estimate cortical source activity from noninvasive electrophysiological measurements such as EEG. However, ESI is fundamentally ill-posed because source activity is substantially higher-dimensional than sensor observations, resulting in non-unique solutions. Conventional methods resolve this ambiguity using hand-designed constraints, while recent learning-based approaches instead learn data-driven source priors. However, the latter often struggle to generalize across individual cortical geometries. To this end, we propose CANDLE, a learning-based ESI model that estimates source activity on subject-specific cortical geometries. CANDLE learns a prior over the null space induced by the T1-derived source-to-sensor mapping, restricting learning to unobservable source components while preserving geometric constraints. To train CANDLE, we developed a whole-brain simulator spanning over 1,100 subject-specific cortical geometries with source configurations derived from over 26,000 statistical brain maps. Trained exclusively on simulated data, CANDLE outperformed prior ESI methods on simulated source estimation and generalized to two empirical tasks: (i) intracranial stimulation-site localization and (ii) epileptogenic zone estimation from presurgical interictal EEG.