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Electrostatic Boundary Conditions01:16

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Consider an external electric field propagating through a homogeneous medium. When the electric field crosses the surface boundary of the medium, it undergoes a discontinuity. The electric field can be resolved into normal and tangential components. The amount by which the field changes at any boundary is given by the difference between the field components above and below the surface boundary.
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Finite Element Modelling of a Cellular Electric Microenvironment
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A software toolkit for TMS electric-field modeling with boundary element fast multipole method: an efficient MATLAB

Sergey N Makarov1,2,3, William A Wartman1, Mohammad Daneshzand2

  • 1Electrical & Computer Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609 United States of America.

Journal of Neural Engineering
|April 3, 2020
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Summary

A new transcranial magnetic stimulation (TMS) modeling toolkit uses an improved charge-based boundary element fast multipole method (BEM-FMM) for faster, accurate simulations. This open-source software aids in TMS targeting and hardware development.

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Biomedical Engineering

Background:

  • Transcranial magnetic stimulation (TMS) is a key non-invasive brain stimulation technique.
  • Accurate modeling of electric fields is crucial for optimizing TMS targeting and dosing.
  • Current modeling methods, like finite element method (FEM), have limitations in accuracy and resolution.

Purpose of the Study:

  • To present and disseminate a novel TMS modeling software toolkit.
  • To introduce algorithmic improvements to the charge-based boundary element fast multipole method (BEM-FMM).
  • To apply the toolkit to realistic TMS modeling scenarios using high-resolution human head models.

Main Methods:

  • Employed the charge-based boundary element fast multipole method (BEM-FMM) as an alternative to traditional FEM.
  • Implemented novel improvements to the BEM-FMM algorithm for enhanced performance.
  • Utilized a high-resolution human head model with detailed cortical geometry and an accurate coil model.

Main Results:

  • Achieved a threefold increase in computational speed with the improved BEM-FMM algorithm.
  • Maintained high solution accuracy comparable to previous methods.
  • Released the MATLAB®-based computational code, coil/head model repositories, and documentation as an open-source toolkit.

Conclusions:

  • The developed TMS modeling toolkit offers accurate and fast simulations.
  • The open-source nature facilitates its adoption for research and development.
  • This toolkit can significantly improve individualized TMS targeting, dosing, and hardware design.