Accurate Pediatric Head Models for EEG Source Localization

Biomedical Engineering Online, 2006

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.

IEEE Transactions on Biomedical Engineering, 2000

Electroencephalography and Clinical Neurophysiology, 1997

The paper describes finite element related procedures for inverse localization of multiple sources in realistically shaped head models. Dipole sources are modeled by placing proper monopole sources on neighboring nodes. Lead field operators are established for dipole sources. Two different strategies for the solution of inverse problems, namely combinatorial optimization techniques and regularization methods are discussed and applied to visually evoked potentials, for which exemplary results are shown. Most of the procedures described are fully automatic and require only proper input preparation. The overall work for the example presented (from EEG recording to visual inspection of lhe results) can be performed in roughly a week, most of which is waiting time for the computation of the lead field matrix or inverse calculations on a standard and affordable engineering workstation. © 1997 Elsevier Science Ireland Ltd.

Proceedings of The 13th International Conference on Biomagnetism, 2002

The inverse problem in the field of EEG and MEG requires the repeated simulation of the field distribution for a given dipolar source in the human brain using a volume-conduction model of the head. High resolution finite element head modeling allows the inclusion of tissue conductivity inhomogeneities and anisotropies. We will present new approaches for individually determining the direction-dependent conductivities of skull and brain white matter, based on non-invasive multimodal magnetic resonance imaging data, and for generating a high resolution realistically shaped anisotropic finite element model of the human head. Forward calculation and inverse localization errors indicate the necessity of the chosen complex forward model.

Physics in Medicine and Biology, 2001

In this work, a meshless (free Galerkin) method is used to define a realistic head conductor model to solve the EEG inverse problem. The aim is to find sources responsible for scalp potentials with precision and low computation time. In order to assess the proper method's performance, it was evaluated with simulated EEG signals, obtained from different source positions and constant electrode number. The meshless method integrates anatomical information obtained from segmented anatomical MRI; its performance was compared with that obtained considering an infinite homogenous conductor model. Head geometry and secondary current sources, contributing to the potential recorded at electrode sites, are taken into account in the meshless method, thus obtaining a realistic head conductor volume model; this results in source localization errors near 13 ± 5 mm on 200 cases of simulated EEG records.

Keyword: MATLAB Software Head modeling EEG Boundary Element Method BEM Realistic 4-layer head model MNI Inverse problem Source localization a b s t r a c t This paper introduces a Neuroelectromagnetic Forward Head Modeling Toolbox (NFT) running under MATLAB (The Mathworks, Inc.

IEEE Transactions on Magnetics, 2000

The electrical activity of a neurological epileptic disorder can be determined in a noninvasive way by analyzing the electroencephalogram (EEG). The EEG source localization procedure requires the solution of an inverse problem. We implemented the two-level inverse algorithm, which combines the computational efficiency of the semi-analytical three-shell spherical model with the accuracy of the anisotropic realistic head model. This paper mainly deals with the implementation and the validation of the numerical two-level inverse algorithm using an anisotropic head model. Results obtained from the EEG source localization are compared with measurements of depth electrodes. The results show the fast localization and noise robustness of the numerical procedure.

Brain Topography, 2013

Subject-specific four-layer boundary element method (BEM) electrical forward head models for four participants, generated from magnetic resonance (MR) head images using NFT (sccn.ucsd.edu/wiki/NFT), were used to simulate electroencephalographic (EEG) scalp potentials at 256 recorded electrode positions produced by single current dipoles of a 3-D grid in brain space. Locations of these dipoles were then estimated using gradient descent within five template head models fit to the electrode positions. These were: a spherical model, three-layer and four-layer boundary element method (BEM) head models based on the Montreal Neurological Institute (MNI) template head image, and these BEM models warped to the recorded electrode positions. Smallest localization errors (4.1 mm to 6.2 mm, medians) were obtained using the electrode-position warped four-layer BEM models, with largest localization errors (≈ 20 mm) for most basal brain locations. When we increased the brain-to-skull conductivity ratio assumed in the template model scalp projections from the simulated value (25:1) to a higher value (80:1) used in earlier studies, the estimated dipole locations moved outwards (12.4 mm, median). We also investigated the effects of errors in co-registering the electrode positions, of reducing electrode counts, and of adding a fifth, isotropic white matter layer to one individual head model. Results show that when individual subject MR head images are not available to construct subject-specific head models, accurate EEG source localization should employ a four-or five-layer BEM template head model incorporating an accurate skull conductivity estimate and warped to 64 or more accurately 3-D measured and co-registered electrode positions.

Studies in health technology and informatics, 2011

Understanding the milliscale (temporal and spatial) dynamics of the human brain activity requires high-resolution modeling of head electromagnetics and source localization of EEG data. We have developed an automated environment to construct individualized computational head models from image segmentation and to estimate conductivity parameters using electrical impedance tomography methods. Algorithms incorporating tissue inhomogeneity and impedance anisotropy in electromagnetics forward simulations have been developed and parallelized. The paper reports on the application of the environment in the processing of realistic head models, including conductivity inverse estimation and lead field generation for use in EEG source analysis.

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