EEG source localization using a sparsity prior based on Brodmann areas

TL;DR

This paper introduces a novel EEG source localization method that uses Brodmann areas as functional zones with a sparsity prior, improving the uniqueness and biological plausibility of the solutions.

Contribution

The paper proposes a new approach dividing the cortex into Brodmann areas and applying a sparsity constraint, enhancing EEG source localization accuracy.

Findings

Potentially more consistent with brain activity sparsity

Improved localization accuracy over previous methods

Validated on realistic head models with multiple electrode setups

Abstract

Localizing the sources of electrical activity in the brain from Electroencephalographic (EEG) data is an important tool for non-invasive study of brain dynamics. Generally, the source localization process involves a high-dimensional inverse problem that has an infinite number of solutions and thus requires additional constraints to be considered to have a unique solution. In the context of EEG source localization, we propose a novel approach that is based on dividing the cerebral cortex of the brain into a finite number of Functional Zones which correspond to unitary functional areas in the brain. In this paper we investigate the use of Brodmanns areas as the Functional Zones. This approach allows us to apply a sparsity constraint to find a unique solution for the inverse EEG problem. Compared to previously published algorithms which use different sparsity constraints to solve this problem, the proposed method is potentially more consistent with the known sparsity profile of the human brain activity and thus may be able to ensure better localization. Numerical experiments are conducted on a realistic head model obtained from segmentation of MRI images of the head and includes four major compartments namely scalp, skull, cerebrospinal fluid (CSF) and brain with relative conductivity values. Three different electrode setups are tested in the numerical experiments.