%0 Journal Article
%T Realistic and Spherical Head Modeling for EEG Forward Problem Solution: A Comparative Cortex-Based Analysis
%A Federica Vatta
%A Fabio Meneghini
%A Fabrizio Esposito
%A Stefano Mininel
%A Francesco Di Salle
%J Computational Intelligence and Neuroscience
%D 2010
%I Hindawi Publishing Corporation
%R 10.1155/2010/972060
%X The accuracy of forward models for electroencephalography (EEG) partly depends on head tissues geometry and strongly affects the reliability of the source reconstruction process, but it is not yet clear which brain regions are more sensitive to the choice of different model geometry. In this paper we compare different spherical and realistic head modeling techniques in estimating EEG forward solutions from current dipole sources distributed on a standard cortical space reconstructed from Montreal Neurological Institute (MNI) MRI data. Computer simulations are presented for three different four-shell head models, two with realistic geometry, either surface-based (BEM) or volume-based (FDM), and the corresponding sensor-fitted spherical-shaped model. Point Spread Function (PSF) and Lead Field (LF) cross-correlation analyses were performed for 26 symmetric dipole sources to quantitatively assess models' accuracy in EEG source reconstruction. Realistic geometry turns out to be a relevant factor of improvement, particularly important when considering sources placed in the temporal or in the occipital cortex. 1. Introduction Localization of neural brain sources is important in several areas of research of basic neuroscience, such as cortical organization and integration [1], and in some areas of clinical neuroscience such as preoperative planning [2] and epilepsy [3]. Localization of neural brain sources based on electroencephalography (EEG) uses scalp potential data to infer the location of underlying neural activity [1]. This procedure entails with (i) modeling the brain electrical activity, (ii) modeling the head volume conduction process for linking the neural electrical activity to EEG recordings, and (iii) reconstructing the brain electrical activity from recorded EEG data (measured scalp potentials). The first two modeling steps serve to solve the so-called EEG forward problem, which describes the distribution of electric potentials for given source locations, orientations, and signals; the following step is the inverse of the previous ones, thereby it is commonly referred to as the EEG inverse problem solution. A model of brain electrical activity (in short ˇ°source modelˇ±) is composed of bioelectric units distributed within the entire brain volume or over specific brain surfaces or confined to a few brain locations. A single source unit is often modeled as a current dipole, which well approximates the synchronized synaptic currents at a columnar level [4]. When confined to the cerebral cortex, the orientation of the current dipoles can be either free
%U http://www.hindawi.com/journals/cin/2010/972060/