On the handling of brain tissue anisotropy in the forward EEG problem with a conformingly discretized surface integral method

This work presents a mixed discretized integral formulation for the EEG forward problem that can handle piecewise homogeneous anisotropic conductivities. Given that, in the presence of anisotropic conductivity profiles, an harmonic function for one profile is not necessarily harmonic for a different profile, standard methods to obtain surface integral equations cannot be used. For this reason, we have adopted here an indirect method strategy that allows to overcome this issue. In addition, the equation is discretized by using a mixed and conforming approach that is specifically designed to abide by the mapping properties of all integral operators involved. This results in a further enhancement of the accuracy, especially when dipolar sources are used to model the brain activity near a layer boundary. Numerical results confirms the accuracy of the approach and shows the applicability to real case scenario.

[1]  D. Tucker,et al.  EEG source localization: Sensor density and head surface coverage , 2015, Journal of Neuroscience Methods.

[2]  Snorre H. Christiansen,et al.  A dual finite element complex on the barycentric refinement , 2005, Math. Comput..

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

[4]  Scott Makeig,et al.  Simultaneous head tissue conductivity and EEG source location estimation , 2016, NeuroImage.

[5]  B.N. Cuffin,et al.  EEG localization accuracy improvements using realistically shaped head models , 1996, IEEE Transactions on Biomedical Engineering.

[6]  Théodore Papadopoulo,et al.  Handling white-matter anisotropy in BEM for the EEG forward problem , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[7]  M. Gavaret,et al.  High-resolution EEG (HR-EEG) and magnetoencephalography (MEG) , 2015, Neurophysiologie Clinique/Clinical Neurophysiology.

[8]  D. A. Driscoll,et al.  Current Distribution in the Brain From Surface Electrodes , 1968, Anesthesia and analgesia.

[9]  Francesco P. Andriulli,et al.  Mixed discretization formulations for the direct EEG problem , 2014, The 8th European Conference on Antennas and Propagation (EuCAP 2014).

[10]  F. Andriulli,et al.  On an indirect boundary element method for the anisotropic EEG forward problem , 2015, 2015 9th European Conference on Antennas and Propagation (EuCAP).

[11]  Olivier D. Faugeras,et al.  A common formalism for the Integral formulations of the forward EEG problem , 2005, IEEE Transactions on Medical Imaging.

[12]  J. Sarvas Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem. , 1987, Physics in medicine and biology.

[13]  Thom F. Oostendorp,et al.  The conductivity of the human skull: results of in vivo and in vitro measurements , 2000, IEEE Transactions on Biomedical Engineering.

[14]  C. Schwab,et al.  Boundary Element Methods , 2010 .

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

[16]  J. Fermaglich Electric Fields of the Brain: The Neurophysics of EEG , 1982 .

[17]  Jens Haueisen,et al.  Influence of anisotropic electrical conductivity in white matter tissue on the EEG/MEG forward and inverse solution. A high-resolution whole head simulation study , 2010, NeuroImage.

[18]  Sylvain Baillet,et al.  Influence of skull anisotropy for the forward and inverse problem in EEG: Simulation studies using FEM on realistic head models , 1998, Human brain mapping.

[19]  Zhongying Chen,et al.  The Petrov--Galerkin and Iterated Petrov--Galerkin Methods for Second-Kind Integral Equations , 1998 .