  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.
