Brain sources estimation based on EEG and computer simulation technology (CST)

Abstract EEG source estimation aims to provide precise information about the location of active brain source that corresponds to the measured signals. The accuracy of EEG forward model significantly influences the accuracy and performance of the inverse problem. In this research, we propose a new method to model head volume conductor and generate a leadfield matrix. The solution is based on employing electromagnetic simulation (CST electromagnetic software) to generate a leadfield matrix of a realistic head. The geometrical data consist of three compartments (Brain, Skull, and Scalp) obtained from real human MRI data. Finite Element Method (FEM) was used in the CST low frequency solver to generate the forward model. We were able to demonstrate the use of the electromagnetic simulation solvers in solving the EEG forward problem. The result has been validated by comparing the scalp voltage potential distribution obtained using CST with scalp potential calculated using FieldTrip (EEG/MEG open source). To further validate the proposed technique, an inverse solution was able to estimate the location of active dipoles within brain successfully based on the calculated leadfield matrix using CST.

[1]  B. W. van Dijk,et al.  Symmetry considerations in the quasi-static approximation of volume conductor theory , 1991 .

[2]  Maureen Clerc,et al.  Handbook of Neural Activity Measurement: EEG and MEG: forward modeling , 2012 .

[3]  Scott Makeig,et al.  Neuroelectromagnetic Forward Head Modeling Toolbox , 2010, Journal of Neuroscience Methods.

[4]  R. Sadleir,et al.  Modeling Skull Electrical Properties , 2007, Annals of Biomedical Engineering.

[5]  S. Makeig,et al.  Effects of Forward Model Errors on EEG Source Localization , 2013, Brain Topography.

[6]  Martin Luessi,et al.  MNE software for processing MEG and EEG data , 2014, NeuroImage.

[7]  Toshimitsu Musha,et al.  Electric Dipole Tracing in the Brain by Means of the Boundary Element Method and Its Accuracy , 1987, IEEE Transactions on Biomedical Engineering.

[8]  Shanan Zhu,et al.  Finite Element Modeling of Human Head from Medical Images , 2007, 2007 Joint Meeting of the 6th International Symposium on Noninvasive Functional Source Imaging of the Brain and Heart and the International Conference on Functional Biomedical Imaging.

[9]  Amr Mohamed,et al.  Ensemble Classifier for Epileptic Seizure Detection for Imperfect EEG Data , 2015, TheScientificWorldJournal.

[10]  Y. D'Asseler,et al.  A finite difference method with reciprocity used to incorporate anisotropy in electroencephalogram dipole source localization , 2005 .

[11]  J. P. Ary,et al.  Location of Sources of Evoked Scalp Potentials: Corrections for Skull and Scalp Thicknesses , 1981, IEEE Transactions on Biomedical Engineering.

[12]  Matti Hämäläinen,et al.  Handbook of Neural Activity Measurement: MEG and EEG: source estimation , 2012 .

[13]  G Curio,et al.  On the calculation of magnetic fields based on multipole modeling of focal biological current sources. , 1997, Biophysical journal.

[14]  Jens Haueisen,et al.  Changes in scalp potentials and spatial smoothing effects of inclusion of dura layer in human head models for EEG simulations , 2014, Front. Neuroeng..

[15]  Théodore Papadopoulo,et al.  OpenMEEG: opensource software for quasistatic bioelectromagnetics , 2010, Biomedical engineering online.

[16]  Zhuoxiang Ren,et al.  Finite element modeling of current dipoles using direct and subtraction methods for EEG forward problem , 2013 .

[17]  Carsten H. Wolters,et al.  Geometry-Adapted Hexahedral Meshes Improve Accuracy of Finite-Element-Method-Based EEG Source Analysis , 2007, IEEE Transactions on Biomedical Engineering.

[18]  Carsten H. Wolters,et al.  A full subtraction approach for finite element method based source analysis using constrained Delaunay tetrahedralisation , 2009, NeuroImage.

[19]  D. B. Heppner,et al.  Considerations of quasi-stationarity in electrophysiological systems. , 1967, The Bulletin of mathematical biophysics.

[20]  Thomas R. Knösche,et al.  A guideline for head volume conductor modeling in EEG and MEG , 2014, NeuroImage.

[21]  Bart Vanrumste,et al.  Review on solving the forward problem in EEG source analysis , 2007, Journal of NeuroEngineering and Rehabilitation.

[22]  Thomas R. Knösche,et al.  Influences of skull segmentation inaccuracies on EEG source analysis , 2012, NeuroImage.

[23]  K. Foster,et al.  Dielectric properties of brain tissue between 0.01 and 10 GHz. , 1979, Physics in medicine and biology.

[24]  Andreas Schulze-Bonhage,et al.  The role of blood vessels in high-resolution volume conductor head modeling of EEG , 2015, NeuroImage.

[25]  Jens Haueisen,et al.  Dipole models for the EEG and MEG , 2002, IEEE Transactions on Biomedical Engineering.

[26]  W. Drongelen,et al.  Estimation of in vivo human brain-to-skull conductivity ratio from simultaneous extra- and intra-cranial electrical potential recordings , 2005, Clinical Neurophysiology.

[27]  Thomas R. Knösche,et al.  Influence of the head model on EEG and MEG source connectivity analyses , 2015, NeuroImage.

[28]  E. Frank.  Electric Potential Produced by Two Point Current Sources in a Homogeneous Conducting Sphere , 1952 .

[29]  Z.J. Koles,et al.  A High-Resolution Anisotropic Finite-Volume Head Model for EEG Source Analysis , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[30]  Richard M. Leahy,et al.  Brainstorm: A User-Friendly Application for MEG/EEG Analysis , 2011, Comput. Intell. Neurosci..

[31]  Christophe Geuzaine,et al.  A finite-element reciprocity solution for EEG forward modeling with realistic individual head models , 2014, NeuroImage.

[32]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[33]  David R. Wozny,et al.  The electrical conductivity of human cerebrospinal fluid at body temperature , 1997, IEEE Transactions on Biomedical Engineering.

[34]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..