TY - JOUR
T1 - Boundary element fast multipole method for modeling electrical brain stimulation with voltage and current electrodes
AU - Makarov, Sergey N.
AU - Golestanirad, Laleh
AU - Wartman, William A.
AU - Nguyen, Bach Thanh
AU - Noetscher, Gregory M.
AU - Ahveninen, Jyrki P.
AU - Fujimoto, Kyoko
AU - Weise, Konstantin
AU - Nummenmaa, Aapo R.
N1 - Publisher Copyright:
© 2021 IOP Publishing Ltd.
PY - 2021/8
Y1 - 2021/8
N2 - Objective. To formulate, validate, and apply an alternative to the finite element method (FEM) high-resolution modeling technique for electrical brain stimulation - the boundary element fast multipole method (BEM-FMM). To include practical electrode models for both surface and embedded electrodes. Approach. Integral equations of the boundary element method in terms of surface charge density are combined with a general-purpose fast multipole method and are expanded for voltage, shunt, current, and floating electrodes. The solution of coupled and properly weighted/preconditioned integral equations is accompanied by enforcing global conservation laws: charge conservation law and Kirchhoff's current law. Main results. A sub-percent accuracy is reported as compared to the analytical solutions and simple validation geometries. Comparison to FEM considering realistic head models resulted in relative differences of the electric field magnitude in the range of 3%-6% or less. Quantities that contain higher order spatial derivatives, such as the activating function, are determined with a higher accuracy and a faster speed as compared to the FEM. The method can be easily combined with existing head modeling pipelines such as headreco or mri2mesh. Significance. The BEM-FMM does not rely on a volumetric mesh and is therefore particularly suitable for modeling some mesoscale problems with submillimeter (and possibly finer) resolution with high accuracy at moderate computational cost. Utilizing Helmholtz reciprocity principle makes it possible to expand the method to a solution of EEG forward problems with a very large number of cortical dipoles.
AB - Objective. To formulate, validate, and apply an alternative to the finite element method (FEM) high-resolution modeling technique for electrical brain stimulation - the boundary element fast multipole method (BEM-FMM). To include practical electrode models for both surface and embedded electrodes. Approach. Integral equations of the boundary element method in terms of surface charge density are combined with a general-purpose fast multipole method and are expanded for voltage, shunt, current, and floating electrodes. The solution of coupled and properly weighted/preconditioned integral equations is accompanied by enforcing global conservation laws: charge conservation law and Kirchhoff's current law. Main results. A sub-percent accuracy is reported as compared to the analytical solutions and simple validation geometries. Comparison to FEM considering realistic head models resulted in relative differences of the electric field magnitude in the range of 3%-6% or less. Quantities that contain higher order spatial derivatives, such as the activating function, are determined with a higher accuracy and a faster speed as compared to the FEM. The method can be easily combined with existing head modeling pipelines such as headreco or mri2mesh. Significance. The BEM-FMM does not rely on a volumetric mesh and is therefore particularly suitable for modeling some mesoscale problems with submillimeter (and possibly finer) resolution with high accuracy at moderate computational cost. Utilizing Helmholtz reciprocity principle makes it possible to expand the method to a solution of EEG forward problems with a very large number of cortical dipoles.
KW - boundary element method
KW - deep brain stimulation
KW - electroencephalography
KW - fast multipole method
KW - intracortical microstimulation
KW - numerical modeling
KW - transcranial electrical stimulation
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U2 - 10.1088/1741-2552/ac17d7
DO - 10.1088/1741-2552/ac17d7
M3 - Article
C2 - 34311449
AN - SCOPUS:85114441424
SN - 1741-2560
VL - 18
JO - Journal of Neural Engineering
JF - Journal of Neural Engineering
IS - 4
M1 - 0460D4
ER -