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

You might also read

Related Articles

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

Sort by
Same author

A new method for optimal placement of tumor treating fields electrodes.

Neuro-oncology advances·2026
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

How Low-Frequency Neural Activity Structures Language in Time.

Neurobiology of language (Cambridge, Mass.)·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 journal

Correction: Komatsu et al. Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening. <i>Bioengineering</i> 2026, <i>13</i>, 100.

Bioengineering (Basel, Switzerland)·2026
Same journal

Comparison of CO<sub>2</sub> Laser and Microdebrider in the Surgical Treatment of Pediatric Recurrent Respiratory Papillomatosis: A Retrospective Analysis.

Bioengineering (Basel, Switzerland)·2026
Same journal

Toward More Translational Tumor Models: Breast dECM-Based 3D Systems Capture Native Microenvironmental Cues.

Bioengineering (Basel, Switzerland)·2026
Same journal

Postural Stability Changes During the 4 Phases of the Half Squat: Kinematics Profile of the Center of Pressure and Center of Mass in High-Performance Weightlifters-A Pilot Study.

Bioengineering (Basel, Switzerland)·2026
Same journal

Definite Implant Position as Novel Readout for Effectiveness of Ridge Preservation Indicates to Beneficial Effect of Combined Treatment with Platelet-Rich Fibrin (PRF) and Xenogenic Biomaterial in Bone Regeneration.

Bioengineering (Basel, Switzerland)·2026
Same journal

Trueness and Precision of Intraoral Scanners for 3D-Printed Orthodontic Models with Attachments: An In Vitro Comparative Study.

Bioengineering (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Jun 6, 2025

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.3K

Improving EEG Forward Modeling Using High-Resolution Five-Layer BEM-FMM Head Models: Effect on Source Reconstruction

Guillermo Nuñez Ponasso1, William A Wartman1, Ryan C McSweeney1

  • 1Department of Electrical and Computer Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA.

Bioengineering (Basel, Switzerland)
|November 27, 2024
PubMed
Summary
This summary is machine-generated.

More complex models improve electroencephalographic (EEG) source localization accuracy. Five-layer boundary element models (BEM) with adaptive mesh refinement offer superior precision over simpler three-layer BEM for brain imaging applications.

Keywords:
EEG dipole reconstructionEEG source analysisadaptative mesh refinement (AMR)boundary element method (BEM)brain imagingelectroencephalography (EEG)fast multipole method (FMM)

More Related Videos

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

11.6K
Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography
09:25

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Published on: July 26, 2019

6.8K

Related Experiment Videos

Last Updated: Jun 6, 2025

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.3K
Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

11.6K
Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography
09:25

Detecting Pre-Stimulus Source-Level Effects on Object Perception with Magnetoencephalography

Published on: July 26, 2019

6.8K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computational Biology

Background:

  • Electroencephalographic (EEG) source localization is crucial for understanding brain activity.
  • Current methods often rely on simplified head models, potentially limiting accuracy.
  • Advancements in computational power enable more complex modeling approaches.

Purpose of the Study:

  • To evaluate the impact of head model complexity on EEG source localization accuracy.
  • To compare a standard three-layer boundary element method (BEM) with a high-resolution five-layer BEM-FMM with adaptive mesh refinement (AMR).
  • To quantify localization errors across the grey matter.

Main Methods:

  • Generated noiseless 256-channel EEG data from 15 subjects.
  • Utilized four anatomically relevant dipole positions and three conductivity sets.
  • Compared a three-layer BEM inverse method with a five-layer BEM-FMM/AMR forward solver.
  • Mapped localization errors across 4000 dipole positions in the grey matter.

Main Results:

  • Average localization error for selected dipoles was ~5mm (±2mm) with ~12° (±7°) orientation error.
  • Average source localization error across the entire grey matter was ~9mm (±4mm).
  • Errors tended to be smaller in the occipital lobe.

Conclusions:

  • Three-layer BEM models show robustness in noiseless conditions but can yield substantial errors (10-20mm).
  • Higher-complexity models (five or more layers) are necessary for accurate EEG source reconstruction, especially with noisy data.
  • BEM-FMM with AMR provides an efficient and accurate approach for high-resolution modeling.