Fast EEG/MEG BEM-based forward problem solution for high-resolution head models
William A. Wartman,
Guillermo Nuñez Ponasso,
Zhen Qi,
Jens Haueisen,
Burkhard Maess,
Thomas R. Knösche,
Konstantin Weise,
Gregory M. Noetscher,
Tommi Raij,
Sergey N. Makaroff
Affiliations
William A. Wartman
Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA; Corresponding author.
Guillermo Nuñez Ponasso
Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
Zhen Qi
Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
Jens Haueisen
Technische Universität Ilmenau, Institute of Biomedical Engineering and Informatics, Ilmenau, Germany
Burkhard Maess
Methods and Development Group ‘Brain Networks’, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
Thomas R. Knösche
Methods and Development Group ‘Brain Networks’, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
Konstantin Weise
Methods and Development Group ‘Brain Networks’, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Leipzig University of Applied Sciences (HTWK), Institute for Electrical Power Engineering, Leipzig, Germany
Gregory M. Noetscher
Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
Tommi Raij
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
Sergey N. Makaroff
Dept. of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA
A fast BEM (boundary element method) based approach is developed to solve an EEG/MEG forward problem for a modern high-resolution head model. The method utilizes a charge-based BEM accelerated by the fast multipole method (BEM-FMM) with an adaptive mesh pre-refinement method (called b-refinement) close to the singular dipole source(s). No costly matrix-filling or direct solution steps typical for the standard BEM are required; the method generates on-skin voltages as well as MEG magnetic fields for high-resolution head models within 90 s after initial model assembly using a regular workstation. The forward method is validated by comparison against an analytical solution on a spherical shell model as well as comparison against a full h-refinement method on realistic 1M facet human head models, both of which yield agreement to within 5 % for the EEG skin potential and MEG magnetic fields. The method is further applied to an EEG source localization (inverse) problem for real human data, and a reasonable source dipole distribution is found.
