Source space reduction for eLORETA

A Faes; A de Borman; M M Van Hulle

doi:10.1088/1741-2552/ac2bb6

Source space reduction for eLORETA

J Neural Eng. 2021 Nov 16;18(6). doi: 10.1088/1741-2552/ac2bb6.

Authors

A Faes¹, A de Borman¹, M M Van Hulle¹

Affiliation

¹ KU Leuven-University of Leuven, Department of Neurosciences, Laboratory for Neuro- & Psychophysiology, B-3000 Leuven, Belgium.

PMID: 34592724
DOI: 10.1088/1741-2552/ac2bb6

Abstract

Objective.We introduce Sparse exact low resolution electromagnetic tomography (eLORETA), a novel method for estimating a nonparametric solution to the source localization problem. Its goal is to generate a sparser solution compared to other source localization methods including eLORETA while benefitting from the latter's superior source localization accuracy.Approach.Sparse eLORETA starts by reducing the source space of the Lead Field Matrix using structured sparse Bayesian learning from which a Reduced Lead Field Matrix is constructed, which is used as input to eLORETA.Main results.With Sparse eLORETA, source sparsity can be traded against signal fidelity; the proposed optimum is shown to yield a much sparser solution than eLORETA's with only a slight loss in signal fidelity.Significance.When pursuing a data-driven approach, for cases where it is difficult to choose specific regions of interest, or when subsequently a connectivity analysis is performed, source space reduction could prove beneficial.

Keywords: eLORETA; source localization; sparsity constraint.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Bayes Theorem
Brain*
Electroencephalography* / methods
Tomography