BioMed Central Page 1 of 13 (page number not for citation purposes) BioMedical Engineering OnLine Open Access Research Influence of head models on EEG simulations and inverse source localizations Ceon Ramon*1, Paul H Schimpf2 and Jens Haueisen3 Address: 1Department of Electrical Engineering, University of Washington, Seattle, WA 98195, USA, 2School of Electrical Engineering and Computer Science, Washington State University, Spokane, WA 99202, USA and 3Biomagnetics Center, Department of Neurology, FriedrichSchiller University, Germany and Institute of Biomedical Engineering and Informatics, Technical University Ilmenau, Germany Email: Ceon Ramon* - ceon@u.washington.edu Paul H Schimpf - schimpf@wsu.edu Jens Haueisen - jens.haueisen@tu-ilmenau.de * Corresponding author Abstract Background: The structure of the anatomical surfaces, e.g., CSF and gray and white matter, could severely influence the flow of volume currents in a head model. This, in turn, will also influence the scalp potentials and the inverse source localizations. This was examined in detail with four different human head models. Methods: Four finite element head models constructed from segmented MR images of an adult male subject were used for this study. These models were: (1) Model 1: full model with eleven tissues that included detailed structure of the scalp, hard and soft skull bone, CSF, gray and white matter and other prominent tissues, (2) the Model 2 was derived from the Model 1 in which the conductivity of gray matter was set equal to the white matter, i.e., a ten tissue-type model, (3) the Model 3 was derived from the Model 1 in which the conductivities of gray matter and CSF were set equal to the white matter, i.e., a nine tissue-type model, (4) the Model 4 consisted of scalp, hard skull bone, CSF, gray and white matter, i.e., a five tissue-type model. How model complexity influences the EEG source localizations was also studied with the above four finite element models of the head. The lead fields and scalp potentials due to dipolar sources in the motor cortex were computed for all four models. The inverse source localizations were performed with an exhaustive search pattern in the motor cortex area. The inverse analysis was performed by adding uncorrelated Gaussian noise to the scalp potentials to achieve a signal to noise ratio (SNR) of -10 to 30 dB. The Model 1 was used as a reference model. Results: The reference model, as expected, performed the best. The Model 3, which did not have the CSF layer, performed the worst. The mean source localization errors (MLEs) of the Model 3 were larger than the Model 1 or 2. The scalp potentials were also most affected by the lack of CSF geometry in the Model 3. The MLEs for the Model 4 were also larger than the Model 1 and 2. The Model 4 and the Model 3 had similar MLEs in the SNR range of -10 dB to 0 dB. However, in the SNR range of 5 dB to 30 dB, the Model 4 has lower MLEs as compared with the Model 3. Discussion: These results indicate that the complexity of head models strongly influences the scalp potentials and the inverse source localizations. A more complex head model performs better in inverse source localizations as compared to a model with lesser tissue surfaces. The CSF layer plays an important role in modifying the scalp potentials and also influences the inverse source localizations. In summary, for best results one needs to have highly heterogeneous models of the head for accurate simulations of scalp potentials and for inverse source localizations. Published: 08 February 2006 BioMedical Engineering OnLine 2006, 5:10 doi:10.1186/1475-925X-5-10 Received: 07 October 2005 Accepted: 08 February 2006 This article is available from: http://www.biomedical-engineering-online.com/content/5/1/10 �� 2006 Ramon et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BioMedical Engineering OnLine 2006, 5:10 http://www.biomedical-engineering-online.com/content/5/1/10 Page 2 of 13 (page number not for citation purposes) Background Highly heterogeneous finite element method (FEM) mod- els of the head have recently become increasingly popular for EEG (electroencephalography) simulations and inverse reconstructions of the electrical sources in the cor- tex. How does the complexity of these models influence the forward and inverse simulations? We have examined this question with four different FEM models of the head varying in complexities from five to eleven tissue-types. In particular, we examined the effects of CSF, gray and white matter on the forward and inverse simulations for the sources located in the motor cortex area. Our results show that both the scalp potentials and the inverse source reconstruction are significantly influenced by the model complexity. Previous studies with boundary element method (BEM) models of the head have examined how volume currents affect the forward EEG simulations and also their effects on inverse source localizations [1,2]. It was found that a 3-compartment BEM model of the head performed better than a 3-shell spherical model of the head, particularly in basal brain areas, including the temporal lobe [1]. Recently, a five tissue-type FEM model of the head has also been used for MEG (magnetoencephalography) sim- ulations and source reconstructions [3]. That study com- pared the performance of a five tissue-type FEM model with a spherical head model and found that the five tis- sue-type FEM model performed better in accounting of the volume currents and in inverse source localization. These previous studies show that more complex head models account for volume currents more precisely as compared to simpler, e.g., spherical, head models. Thus, highly heterogeneous finite element models of the head have a potential to further improve the inverse source localizations. In related studies, a five tissue-type FEM model of the head has also been used for efficient compu- tations of the lead fields [4,5] and also for analyzing the effects of tissue conductivities on MEG forward and inverse simulations [6]. Methods Finite element models of the head were constructed from the segmented MRI (magnetic resonance imaging) slices Table 1: Head tissue resistivity and conductivity values. Tissue Resistivity (Ohm cm) Conductivity (Siemens/cm) Brain White Matter 700 1.428E-3 Brain Gray Matter 300 3.334E-3 Spinal Cord and Cerebellum 624 1.6026E-3 Cerebrospinal Fluid (CSF) 65 15.38E-3 Hard Bone 16000 6.25E-5 Soft Bone 2180 4.587E-4 Muscle 900 1.1112E-3 Fat 2500 4.0E-4 Eye 198 5.0505E-4 Scalp 230 4.3478E-3 Soft Tissue 576 1.7361E-3 (Left) A raw MRI slice, (right) segmented slice with ten major tissues identified in it Figure 1 (Left) A raw MRI slice, (right) segmented slice with ten major tissues identified in it. The soft tissue which is present in other slices is not included here.