arXiv：cs.LG· Shuntaro Suzuki, Yuiga Wada, Komei Sugiura·· 4 小时前AI 评分39
CANDLE：基于皮层零空间分解的无创脑源成像模型
CANDLE: Cortical Null-Space Decomposition for Noninvasive Brain Source Imaging
AI 导读
研究提出 CANDLE，一种基于学习的无创脑源成像（ESI）模型，通过在由 T1 加权 MRI 推导的源到传感器映射的零空间上学习先验，在受试者个体化皮层几何上估计源活动。
正文
Abstract:Electrophysiological source imaging (ESI) aims to estimate cortical source activity from noninvasive electrophysiological measurements such as electroencephalogram (EEG). However, ESI is fundamentally ill-posed because source activity is substantially higher-dimensional than sensor observations, resulting in non-unique solutions. Recent learning-based approaches address this ambiguity by learning data-driven source priors, yet they often struggle to generalize across subject-specific cortical geometries. To address this, 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 source-to-sensor mapping derived from T1-weighted MRI, restricting learning to unobservable source components while preserving geometric constraints. To train CANDLE, we develop 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 activity estimation and generalized to two empirical tasks: (i) intracranial stimulation localization from simultaneously recorded scalp EEG and (ii) epileptogenic zone estimation from presurgical interictal EEG. Our project page is available at this https URL}{this https URL.
| Subjects: | Machine Learning (cs.LG); Signal Processing (eess.SP); Neurons and Cognition (q-bio.NC) |
| Cite as: | arXiv:2610.07824 [cs.LG] |
| (or arXiv:2610.07824v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07824 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shuntaro Suzuki [view email]
[v1]
Tue, 6 Oct 2026 06:19:18 UTC (6,117 KB)
来源：arXiv：cs.LG · arxiv.org