A Robust eLORETA Technique for Localization of Brain Sources in the Presence of Forward Model Uncertainties
A. Noroozi, M. Ravan, B. Razavi, R. S. Fisher, Y. Law, and M. S. Hasan

TL;DR
This paper introduces ReLORETA, a robust extension of eLORETA, designed to accurately localize brain sources despite uncertainties in the forward model, improving clinical applications like epilepsy source localization.
Contribution
The paper proposes ReLORETA, an iterative method that estimates and compensates for forward model uncertainties, enhancing localization robustness over traditional eLORETA.
Findings
ReLORETA outperforms eLORETA in simulated and real data scenarios.
ReLORETA maintains accuracy across various noise levels.
ReLORETA shows promise for clinical brain source localization.
Abstract
In this paper, we present a robust version of the well-known exact low-resolution electromagnetic tomography (eLORETA) technique, named ReLORETA, to localize brain sources in the presence of different forward model uncertainties. Methods: We first assume that the true lead field matrix is a transformation of the existing lead field matrix distorted by uncertainties and propose an iterative approach to estimate this transformation accurately. Major sources of the forward model uncertainties, including differences in geometry, conductivity, and source space resolution between the real and simulated head models, and misaligned electrode positions, are then simulated to test the proposed method. Results: ReLORETA and eLORETA are applied to simulated focal sources in different regions of the brain and the presence of various noise levels as well as real data from a patient with focal…
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