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Influence of Skull Modeling Approaches on EEG Source Localization

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Abstract

Electroencephalographic source localization (ESL) relies on an accurate model representing the human head for the computation of the forward solution. In this head model, the skull is of utmost importance due to its complex geometry and low conductivity compared to the other tissues inside the head. We investigated the influence of using different skull modeling approaches on ESL. These approaches, consisting in skull conductivity and geometry modeling simplifications, make use of X-ray computed tomography (CT) and magnetic resonance (MR) images to generate seven different head models. A head model with an accurately segmented skull from CT images, including spongy and compact bone compartments as well as some air-filled cavities, was used as the reference model. EEG simulations were performed for a configuration of 32 and 128 electrodes, and for both noiseless and noisy data. The results show that skull geometry simplifications have a larger effect on ESL than those of the conductivity modeling. This suggests that accurate skull modeling is important in order to achieve reliable results for ESL that are useful in a clinical environment. We recommend the following guidelines to be taken into account for skull modeling in the generation of subject-specific head models: (i) If CT images are available, i.e., if the geometry of the skull and its different tissue types can be accurately segmented, the conductivity should be modeled as isotropic heterogeneous. The spongy bone might be segmented as an erosion of the compact bone; (ii) when only MR images are available, the skull base should be represented as accurately as possible and the conductivity can be modeled as isotropic heterogeneous, segmenting the spongy bone directly from the MR image; (iii) a large number of EEG electrodes should be used to obtain high spatial sampling, which reduces the localization errors at realistic noise levels.

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Acknowledgments

We would like to acknowledge the Radiology and Neurology Department of the Ghent University Hospital for providing the patient data used in this study. Victoria Montes-Restrepo was funded by iMinds (Interdisciplinary Institute for Technology). This work is funded by a research grant of the scientific research council of Flanders (FWO-Vlaanderen). The computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by Ghent University, the Hercules Foundation and the Flemish Government—Department EWI.

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Authors and Affiliations

  1. Faculty of Engineering and Architecture, Medical Image and Signal Processing (MEDISIP), iMinds, Ghent University, De Pintelaan 185, 9000, Ghent, Belgium

    Victoria Montes-Restrepo, Pieter van Mierlo, Gregor Strobbe & Stefaan Vandenberghe

  2. Faculty of Medicine and Health Sciences, Molecular Imaging Center Antwerp (MICA), Antwerp University, Universiteitsplein 1, 2610, Wilrijk, Belgium

    Steven Staelens

  3. Faculty of Industrial Engineering, Reliability in Mechatronics and ICT, KU Leuven (Campus Ostend), Zeedijk 101, 8400, Ostend, Belgium

    Hans Hallez

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  1. Victoria Montes-Restrepo
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Correspondence to Victoria Montes-Restrepo.

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Montes-Restrepo, V., van Mierlo, P., Strobbe, G. et al. Influence of Skull Modeling Approaches on EEG Source Localization. Brain Topogr 27, 95–111 (2014). https://doi.org/10.1007/s10548-013-0313-y

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