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

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 ↑