A mixed discretized surface-volume integral equation for solving EEG forward problems with inhomogeneous and anisotropic head models

This work presents a new integral formulation to solve the EEG forward problem for potentially inhomogeneous and anisotropic head conductivity profiles. The formulation has been obtained from a surface/volume variational expression derived from Green's third identity and then solved in terms of both surface and volume unknowns. These unknowns are expanded with suitably chosen basis functions which systematically enforce transmission conditions. Finally, by leveraging on a mixed discretization, the equation is tested within the framework of a Petrov-Galerkin's scheme. Numerical results show the high level of accuracy of the proposed method, which compares very favourably with those obtained with existing, finite element, schemes.

[1]  I F Gorodnitsky,et al.  Neuromagnetic source imaging with FOCUSS: a recursive weighted minimum norm algorithm. , 1995, Electroencephalography and clinical neurophysiology.

[2]  Olaf Steinbach,et al.  Numerical Approximation Methods for Elliptic Boundary Value Problems: Finite and Boundary Elements , 2007 .

[3]  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.

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

[5]  Rajendra Mitharwal,et al.  A novel volume integral equation for solving the Electroencephalography forward problem , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[6]  J. Ebersole,et al.  Localization of Temporal Lobe Foci by Ictal EEG Patterns , 1996, Epilepsia.

[7]  Sampsa Pursiainen,et al.  Raviart–Thomas-type sources adapted to applied EEG and MEG: implementation and results , 2012 .

[8]  G. A. Miller,et al.  Comparison of different cortical connectivity estimators for high‐resolution EEG recordings , 2007, Human brain mapping.

[9]  D. Wilton,et al.  A tetrahedral modeling method for electromagnetic scattering by arbitrarily shaped inhomogeneous dielectric bodies , 1984 .

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

[11]  Z. Zhang,et al.  A fast method to compute surface potentials generated by dipoles within multilayer anisotropic spheres. , 1995, Physics in medicine and biology.

[12]  R. Pascual-Marqui Review of methods for solving the EEG inverse problem , 1999 .

[13]  W. Freeman,et al.  Fine temporal resolution of analytic phase reveals episodic synchronization by state transitions in gamma EEGs. , 2002, Journal of neurophysiology.

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