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A Robust eLORETA Technique for Localization of Brain Sources in the Presence of Forward Model Uncertainties

NOROOZI FAKHABI, Amin, Ravan, Maryam, Razavi, B, LAW, Ying-Ying and HASAN, Mohammad (2022) A Robust eLORETA Technique for Localization of Brain Sources in the Presence of Forward Model Uncertainties. IEEE Transactions on Biomedical Engineering. ISSN 1558-2531 (In Press)

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Abstract or description

Objective: 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 epilepsy. The results show that ReLORETA is considerably more robust and accurate than eLORETA in all cases. Conclusion: Having successfully dealt with the forward model uncertainties, ReLORETA proved to be a promising method for real-world clinical applications. Significance: eLORETA is one of the localization techniques that could be used to study brain activity for medical applications such as determining the epileptogenic zone in patients with medically refractory epilepsy. However, the major limitation of eLORETA is sensitivity to the uncertainties in the forward model. Since this problem can substantially undermine its performance in real-world applications where the exact lead field matrix is unknown, developing a more robust method capable of dealing with these uncertainties is of significant interest.

Item Type: Article
Faculty: School of Digital, Technologies and Arts > Esports
Depositing User: Mohammad HASAN
Date Deposited: 02 Sep 2022 09:40
Last Modified: 03 Sep 2022 04:30
URI: https://eprints.staffs.ac.uk/id/eprint/7433

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