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Localizing the sources of electrical activity in the brain fromElectroencephalographic (EEG) data is an important tool for non-invasive studyof brain dynamics. Generally, the source localization process involves ahigh-dimensional inverse problem that has an infinite number of solutions andthus requires additional constraints to be considered to have a uniquesolution. In the context of EEG source localization, we propose a novelapproach that is based on dividing the cerebral cortex of the brain into afinite number of Functional Zones which correspond to unitary functional areasin the brain. In this paper we investigate the use of Brodmanns areas as theFunctional Zones. This approach allows us to apply a sparsity constraint tofind a unique solution for the inverse EEG problem. Compared to previouslypublished algorithms which use different sparsity constraints to solve thisproblem, the proposed method is potentially more consistent with the knownsparsity profile of the human brain activity and thus may be able to ensurebetter localization. Numerical experiments are conducted on a realistic headmodel obtained from segmentation of MRI images of the head and includes fourmajor compartments namely scalp, skull, cerebrospinal fluid (CSF) and brainwith relative conductivity values. Three different electrode setups are testedin the numerical experiments.

EEG source localization using a sparsity prior based on Brodmann areas