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Neuroimage. 2014 Jun;93 Pt 1:11-22. doi: 10.1016/j.neuroimage.2014.02.022. Epub 2014 Feb 26.

Bayesian model selection of template forward models for EEG source reconstruction.

Author information

1
Ghent University - iMinds, Department of Electronics and Information Systems, MEDISIP, De Pintelaan 185, Building BB Floor 5, 9000, Ghent, Belgium. Electronic address: gregor.strobbe@ugent.be.
2
Ghent University - iMinds, Department of Electronics and Information Systems, MEDISIP, De Pintelaan 185, Building BB Floor 5, 9000, Ghent, Belgium. Electronic address: pieter.vanmierlo@ugent.be.
3
University of Oldenburg, Methods in Neurocognitive Psychology, Department of Psychology, 26111 Oldenburg, Germany; University of Oldenburg, Research Center Neurosensory Science, 26111 Oldenburg, Germany; University of Oldenburg, Cluster of Excellence Hearing4all, 26111 Oldenburg, Germany. Electronic address: maarten.de.vos@uni-oldenburg.de.
4
KU Leuven, Department of Electrical Engineering-ESAT, SCD-SISTA, Kasteelpark Arenberg 10, Bus 2446, 3001 Heverlee, Belgium; IMinds Future Health Department, Leuven, Belgium. Electronic address: bogdan.mijovic@esat.kuleuven.be.
5
Catholic University College of Bruges-Ostend, Faculty of Engineering Technology, Electronics/ICT, Zeedijk 101, 8400, Ostend, Belgium. Electronic address: hans.hallez@kuleuven.be.
6
KU Leuven, Department of Electrical Engineering-ESAT, SCD-SISTA, Kasteelpark Arenberg 10, Bus 2446, 3001 Heverlee, Belgium; IMinds Future Health Department, Leuven, Belgium. Electronic address: sabine.vanhuffel@esat.kuleuven.be.
7
Universidad de Antioquia UdeA, SISTEMIC, Department of Electronic Engineering, Engineering Faculty, Medellín, Colombia. Electronic address: josedavid@udea.edu.co.
8
Ghent University - iMinds, Department of Electronics and Information Systems, MEDISIP, De Pintelaan 185, Building BB Floor 5, 9000, Ghent, Belgium. Electronic address: stefaan.vandenberghe@ugent.be.

Abstract

Several EEG source reconstruction techniques have been proposed to identify the generating neuronal sources of electrical activity measured on the scalp. The solution of these techniques depends directly on the accuracy of the forward model that is inverted. Recently, a parametric empirical Bayesian (PEB) framework for distributed source reconstruction in EEG/MEG was introduced and implemented in the Statistical Parametric Mapping (SPM) software. The framework allows us to compare different forward modeling approaches, using real data, instead of using more traditional simulated data from an assumed true forward model. In the absence of a subject specific MR image, a 3-layered boundary element method (BEM) template head model is currently used including a scalp, skull and brain compartment. In this study, we introduced volumetric template head models based on the finite difference method (FDM). We constructed a FDM head model equivalent to the BEM model and an extended FDM model including CSF. These models were compared within the context of three different types of source priors related to the type of inversion used in the PEB framework: independent and identically distributed (IID) sources, equivalent to classical minimum norm approaches, coherence (COH) priors similar to methods such as LORETA, and multiple sparse priors (MSP). The resulting models were compared based on ERP data of 20 subjects using Bayesian model selection for group studies. The reconstructed activity was also compared with the findings of previous studies using functional magnetic resonance imaging. We found very strong evidence in favor of the extended FDM head model with CSF and assuming MSP. These results suggest that the use of realistic volumetric forward models can improve PEB EEG source reconstruction.

KEYWORDS:

Bayesian model selection; Electroencephalography; Finite difference reciprocity method; Forward model; Head model; Parametric empirical Bayes

[Indexed for MEDLINE]

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