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Updated: Jul 17, 2025

Finite Element Modelling of a Cellular Electric Microenvironment

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Published on: May 18, 2021

An Adaptive H-Refinement Method for the Boundary Element Fast Multipole Method for Quasi-static Electromagnetic

William A Wartman¹, Konstantin Weise^2,3, Manas Rachh⁴

¹Electrical and Computer Engineering Department, Worcester Polytechnic Inst., Worcester, MA 01609 USA.

Biorxiv : the Preprint Server for Biology

|August 30, 2023

Summary

Adaptive mesh refinement (AMR) significantly improves accuracy in brain stimulation and neurophysiological models. This method reduces substantial electric field and potential errors in transcranial electrical stimulation (TES) and electroencephalography (EEG) modeling.

Keywords:

Adaptive mesh refinement Electroencephalography Numerical modeling Transcranial electrical stimulation Transcranial magnetic stimulation

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Area of Science:

Computational neuroscience
Biomedical engineering
Electrophysiology

Background:

Standard multi-compartment head models can exhibit significant errors in electric field and potential calculations for brain stimulation and neurophysiological recordings.
These errors impact the accuracy of modeling transcranial magnetic stimulation (TMS), transcranial electrical stimulation (TES), and electroencephalography (EEG) forward problems.

Conclusions:

AMR is essential for accurate modeling in TES and EEG, significantly reducing errors present in standard models.
The findings have critical implications for precise TES dosing prediction and EEG lead field accuracy.
The developed AMR approach is expected to be applicable and necessary for accurate electromagnetic simulations across various numerical modeling packages.