CANDLE: Cortical-geometry Aware Null-space Decomposition for Localized ESI
Under review
Abstract
Electrophysiological source imaging estimates cortical activity from noninvasive EEG, but the problem is fundamentally ill-posed: source activity is much higher-dimensional than sensor observations.
We introduce CANDLE, a learning-based ESI model that preserves subject-specific geometric constraints while learning a prior over the unobservable null space.
Trained exclusively on a new whole-brain simulator, CANDLE generalizes without subject-specific tuning to simulated source estimation, intracranial stimulation localization, and epileptogenic zone estimation.
Method
From geometry-constrained decomposition to simulator-driven learning, explore the method one step at a time.
01/ 03
Results
CANDLE is evaluated on unseen simulations and two empirical clinical settings, with no subject-specific tuning.
01/ 04
BibTeX
Coming soon...