Abstract
Localizing the sources of electrical activity in the brain from electroencephalographic (EEG) data is an important tool for noninvasive 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 this article, we propose a novel method for EEG source localization. The proposed method 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. To specify the sparsity profile of human brain activity more concisely, the proposed approach considers grouping of the electrical current dipoles inside each of the functional zones. In this article, we investigate the use of Brodmann's areas as the functional zones while sparse Bayesian learning is used to perform sparse approximation. 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. The results demonstrate that the proposed approach is quite promising in solving the EEG source localization problem. In a noiseless environment with 71 electrodes, the proposed method was found to accurately locate up to 6 simultaneously active sources with accuracy >70%.