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A BEM-FMM TMS Coil Designer Using MATLAB Platform.
Brain Stimul
January 2025
Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA, 01609; Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129; Department of Mathematics, Worcester Polytechnic Institute, Worcester, MA, USA, 01609.
An adaptive h-refinement method for the boundary element fast multipole method for quasi-static electromagnetic modeling.
Phys Med Biol
February 2024
Electrical and Computer Engineering Department, Worcester Polytechnic Inst., Worcester, MA 01609 United States of America.
In our recent work pertinent to modeling of brain stimulation and neurophysiological recordings, substantial modeling errors in the computed electric field and potential have sometimes been observed for standard multi-compartment head models. The goal of this study is to quantify those errors and, further, eliminate them through an adaptive mesh refinement (AMR) algorithm. The study concentrates on transcranial magnetic stimulation (TMS), transcranial electrical stimulation (TES), and electroencephalography (EEG) forward problems.
View Article and Find Full Text PDFA fast direct solver for surface-based whole-head modeling of transcranial magnetic stimulation.
Sci Rep
October 2023
Athinoula A. Martinos Ctr. for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA.
When modeling transcranial magnetic stimulation (TMS) in the brain, a fast and accurate electric field solver can support interactive neuronavigation tasks as well as comprehensive biophysical modeling. We formulate, test, and disseminate a direct (i.e.
View Article and Find Full Text PDFAn Adaptive H-Refinement Method for the Boundary Element Fast Multipole Method for Quasi-static Electromagnetic Modeling.
bioRxiv
August 2023
Electrical and Computer Engineering Department, Worcester Polytechnic Inst., Worcester, MA 01609 USA.
Objective: In our recent work pertinent to modeling of brain stimulation and neurophysiological recordings, substantial modeling errors in the computed electric field and potential have sometimes been observed for standard multi-compartment head models. The goal of this study is to quantify those errors and, further, eliminate them through an adaptive mesh refinement (AMR) algorithm. The study concentrates on transcranial magnetic stimulation (TMS), transcranial electrical stimulation (TES), and electroencephalography (EEG) forward problems.
View Article and Find Full Text PDFA fast direct solver for surface-based whole-head modeling of transcranial magnetic stimulation.
Res Sq
July 2023
Athinoula A. Martinos Ctr. for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129 USA.
Background: When modeling transcranial magnetic stimulation (TMS) in the brain, a fast and accurate electric field solver can support interactive neuronavigation tasks as well as comprehensive biophysical modeling.
Objective: We formulate, test, and disseminate a direct ( non-iterative) TMS solver that can accurately determine global TMS fields for any coil type everywhere in a high-resolution MRI-based surface model with ~200,000 or more arbitrarily selected observation points within approximately 5 sec, with the solution time itself of 3 sec.
Method: The solver is based on the boundary element fast multipole method (BEM-FMM), which incorporates the latest mathematical advancement in the theory of fast multipole methods - an FMM-based LU decomposition.
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