CANDLE: Cortical-geometry Aware Null-space Decomposition for Localized ESI

Under review

Overview of the CANDLE source imaging framework

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 · Decomposition

Range space + null space

CANDLE analytically reconstructs the observable range-space component and learns only the hidden component that EEG measurements cannot identify.

L† X_denoise+L_n g_γ(·)=Ŷ

01/ 03

Results

CANDLE is evaluated on unseen simulations and two empirical clinical settings, with no subject-specific tuning.

01 · Simulation

Simulated source activity

Subject-held-out evaluation across unseen cortical geometries shows more localized sources and more faithful temporal dynamics than representative ESI baselines.

37.3 mmHD95 ↓

0.48Dice ↑