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

Serial Position Effect01:03

Serial Position Effect

542
The serial position effect is a cognitive phenomenon where individuals are more likely to recall the first and last items in a list compared to those in the middle. This effect is divided into the primacy effect and the recency effect. The primacy effect is observed when the initial items in a list are remembered better. This occurs because these items are rehearsed more frequently or receive more elaborative processing, allowing them to be encoded into long-term memory more effectively. For...
542
Parallel Resonance01:23

Parallel Resonance

557
The parallel RLC circuit is an arrangement where the resistor (R), inductor (L), and capacitor (C) are all connected to the same nodes and, as a result, share the same voltage across them. The parallel RLC circuit is analyzed in terms of admittance (Y), which reflects the ease with which current can flow. The admittance is given by:
557
Parallel Processing01:20

Parallel Processing

713
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
713
Resistors In Parallel01:23

Resistors In Parallel

6.1K
Resistors are in parallel when one end of all the resistors are connected to a continuous wire of negligible resistance and the other end of all the resistors are also connected to one another through a continuous wire of negligible resistance. In the case of a parallel configuration, the potential drop across each resistor is the same. Current through each resistor can be found using Ohm’s law, I = V/R, where the voltage is constant across each resistor. The sum of the individual currents...
6.1K
Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

1.0K
Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
1.0K
Series and Parallel Capacitors01:14

Series and Parallel Capacitors

9.3K
Capacitors, fundamental components in electronic circuits, can be connected in series and/or parallel configurations. Each configuration has different impacts on the overall behavior of the circuit.
First, consider capacitors connected in series to a battery. In this configuration, the plate connected to the battery's positive terminal develops a positive charge, while the plate attached to the negative terminal becomes negatively charged. An equal magnitude of charge is induced on the...
9.3K

You might also read

Related Articles

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

Sort by
Same author

Numerical study of POD-Galerkin-DEIM reduced order modeling of cardiac monodomain formulation.

Biomedical physics & engineering express·2021
Same author

Model cortical association fields account for the time course and dependence on target complexity of human contour perception.

PLoS computational biology·2011
Same author

Finite difference neuroelectric modeling software.

Journal of neuroscience methods·2011
Same author

Orthogonal field calibration analysis for myocardial electrode arrays used in defibrillation studies.

IEEE transactions on bio-medical engineering·2009
Same author

Bayesian brain source imaging based on combined MEG/EEG and fMRI using MCMC.

NeuroImage·2008
Same author

Modeling direct effects of neural current on MRI.

Human brain mapping·2007
Same journal

Reduced mechanical strength correlates with decreased elastin content in aortic intima-media tissue: association with dissection in human ascending aortas.

Medical & biological engineering & computing·2026
Same journal

How plaque morphology and stenosis severity govern stent-artery interaction and deployment outcomes: a computational study.

Medical & biological engineering & computing·2026
Same journal

Investigating a relation between amyloid beta plaque burden and accumulated neurotoxicity caused by amyloid beta oligomers.

Medical & biological engineering & computing·2026
Same journal

A robot-assisted eye positioning method with high precision and repeatability for ocular particle therapy: mechanical and geometric assessment.

Medical & biological engineering & computing·2026
Same journal

Enhanced puncture event detection for teleoperated needle insertion robotic system.

Medical & biological engineering & computing·2026
Same journal

Energy-efficient real-time 4-stage sleep classification at 10-second resolution.

Medical & biological engineering & computing·2026
See all related articles

Related Experiment Video

Updated: Jan 31, 2026

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads
07:58

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads

Published on: July 25, 2025

820

Finite difference iterative solvers for electroencephalography: serial and parallel performance analysis.

Derek N Barnes1, John S George, Kwong T Ng

  • 1Klipsch School of Electrical and Computer Engineering, New Mexico State University, MSC 3-O, Las Cruces, NM 88003, USA.

Medical & Biological Engineering & Computing
|May 15, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a parallel finite difference method to accelerate electroencephalography (EEG) head modeling. The technique significantly reduces computation time for forward solvers, improving EEG study resolution and efficiency.

More Related Videos

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

7.1K
Analysis of Brain Mitochondria Using Serial Block-Face Scanning Electron Microscopy
07:47

Analysis of Brain Mitochondria Using Serial Block-Face Scanning Electron Microscopy

Published on: July 9, 2016

14.6K

Related Experiment Videos

Last Updated: Jan 31, 2026

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads
07:58

Automatic Laser-based Geometry Capture for Finite Element Analysis of Weld Beads

Published on: July 25, 2025

820
Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

7.1K
Analysis of Brain Mitochondria Using Serial Block-Face Scanning Electron Microscopy
07:47

Analysis of Brain Mitochondria Using Serial Block-Face Scanning Electron Microscopy

Published on: July 9, 2016

14.6K

Area of Science:

  • Computational neuroscience
  • Medical imaging analysis
  • High-performance computing

Background:

  • The resolution of electroencephalography (EEG) head models is currently limited by the computational speed of forward solvers.
  • Solving the Poisson equation governing electromagnetic fields in the head is a computationally intensive step in EEG analysis.

Purpose of the Study:

  • To present a parallel finite difference technique for accelerating the forward solver in EEG head modeling.
  • To compare the performance of various iterative solvers for realistic anisotropic and isotropic head models.
  • To identify the most efficient solver and preconditioning technique for improving EEG forward solutions.

Main Methods:

  • Implementation of a parallel finite difference technique, dividing the computational domain across multiple processors.
  • Detailed comparison of iterative matrix solvers, including the conjugate gradient (CG) method.
  • Application of geometric multigrid preconditioning to enhance solver performance.
  • Testing on realistic anisotropic and isotropic head models derived from MRI data.

Main Results:

  • The parallel finite difference technique significantly reduces the solution time for the Poisson equation in EEG head models.
  • The conjugate gradient method, preconditioned with a geometric multigrid technique, demonstrated superior performance.
  • A speedup of 508 was achieved on 32 processors for an anisotropic model (256x128x256 cells) compared to the serial CG solution.
  • Multigrid preconditioning and parallelization contributed speedups of 20.1 and 25.3, respectively.

Conclusions:

  • Parallel finite difference methods offer a viable approach to overcome the computational limitations of EEG forward solvers.
  • The combination of the conjugate gradient method with geometric multigrid preconditioning and parallelization provides substantial acceleration.
  • This advancement has the potential to improve the resolution and efficiency of EEG studies, enabling more complex analyses and faster results.