Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Electrostatic Boundary Conditions01:16

Electrostatic Boundary Conditions

1.2K
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.
The surface integral of an electric field is given by Gauss's law in integral form and is related to...
1.2K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Standardizing TMS Intensity Across Different Coils Using Individualized Electric Field Modeling.

Human brain mapping·2026
Same author

Cellular Mechanisms of Transcranial Magnetic Stimulation in Climbing Fibers and Purkinje Neurons in the Cerebellum.

bioRxiv : the preprint server for biology·2026
Same author

Muscle anisotropy influences the phrenic nerve activation threshold in non-invasive electrical stimulation.

Medical & biological engineering & computing·2026
Same author

Experimental Validation of Finite Element Models for Directional DBS: The Critical Role of Boundary Conditions on VTA Accuracy.

bioRxiv : the preprint server for biology·2026
Same author

Direct Reconstruction of DC Cortical Conductivity from Large-Scale Electron Microscopy Data.

bioRxiv : the preprint server for biology·2026
Same author

How much EEG is needed for deep learning with convolutional neural networks? Predicting the benefit from additional data.

Journal of neural engineering·2026
Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026
Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026
Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026
Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026
Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026
Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026
See all related articles

Related Experiment Video

Updated: Apr 11, 2026

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

9.5K

Charge Based Boundary Element Method with Residual Driven Adaptive Mesh Refinement for High Resolution Electrical

Derek A Drumm1, Gregory M Noetscher1, Hannes Oppermann2

  • 1Dept. of Electrical & Computer Engineering, Worcester Polytechnic Institute, Worcester, MA, USA.

Biorxiv : the Preprint Server for Biology
|April 10, 2026
PubMed
Summary
This summary is machine-generated.

Accurate modeling for transcranial electrical stimulation (TES), electroconvulsive therapy (ECT), and electroencephalography (EEG) is improved with adaptive mesh refinement. This method enhances numerical stability for charge-based boundary element methods in realistic head models.

More Related Videos

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.3K
Finite Element Modelling of a Cellular Electric Microenvironment
08:23

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

4.1K

Related Experiment Videos

Last Updated: Apr 11, 2026

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models
14:14

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Published on: August 12, 2018

9.5K
Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

2.3K
Finite Element Modelling of a Cellular Electric Microenvironment
08:23

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

4.1K

Area of Science:

  • Computational neuroscience
  • Biomedical engineering
  • Numerical methods

Background:

  • Accurate forward modeling for transcranial electrical stimulation (TES), electroconvulsive therapy (ECT), and electroencephalography (EEG) is crucial.
  • Numerical singularities near electrodes and tissue interfaces pose a challenge for existing methods.
  • The charge-based boundary element method (BEM) accelerated by the fast multiple method (BEM-FMM) is a common approach.

Purpose of the Study:

  • To present an adaptive mesh refinement (AMR) strategy for BEM-FMM that addresses singularities.
  • To develop a novel error estimator incorporating local and nonlocal contributions.
  • To evaluate the AMR strategy on spherical and subject-specific head models.

Main Methods:

  • Developed an AMR strategy for charge-based BEM-FMM.
  • Derived a new error estimator for single-layer potential operators.
  • Constructed a refinement criterion based on charge solution differences.
  • Validated on 5-layer sphere and SimNIBS/Sim4Life head models with different electrode formulations.

Main Results:

  • Achieved relative residual errors below 0.1% for SimNIBS and 1% for Sim4Life models.
  • Demonstrated numerical stability for TES and EEG forward solutions.
  • Showed effective handling of electrode and interface singularities.

Conclusions:

  • The proposed residual-based AMR strategy significantly improves the accuracy and stability of BEM-FMM for TES and EEG forward modeling.
  • This method is effective even with complex, subject-specific head geometries.
  • Enables more reliable computational models for brain stimulation and recording techniques.