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 general modeling framework for complex living systems. A comment on "a decade of thermostatted kinetic theory models for complex active matter living systems" by C. Bianca.

Physics of life reviews·2026
Same author

Reflections on Family Medicine's First Year of Program Signals and Other New ERAS Features.

Family medicine·2026
Same author

Analytical insights from a model of opinion formation based on persuasive argument theory.

Physical review. E·2025
Same author

Associations Between Mental Health and Social Needs Among Black Patients in Primary Care Settings.

Journal of primary care & community health·2025
Same author

Multiaxial fatigue life assessment of dental implants.

Heliyon·2024
Same author

High resolution finite difference schemes for a size structured coagulation-fragmentation model in the space of radon measures.

Mathematical biosciences and engineering : MBE·2023
Same journal

Learning under constraints: a theoretical framework for comparing resource-constrained learning in biological and artificial systems.

Frontiers in computational neuroscience·2026
Same journal

MsGCN: a multi-stream graph convolutional network for multiband PLV graph fusion in EEG-based biometric identification.

Frontiers in computational neuroscience·2026
Same journal

AI-driven neuroanalytic modeling for mental health: multichannel CNN-based autism spectrum disorder detection via facial pattern analysis.

Frontiers in computational neuroscience·2026
Same journal

Modeling multiscale neural dynamics for EEG-based emotion recognition using an attentive wavelet-transformer framework.

Frontiers in computational neuroscience·2026
Same journal

New directions for complex systems in contemporary neuroscience: a morphodynamic and emergent function approach.

Frontiers in computational neuroscience·2026
Same journal

NMDA receptor kinetics drive distinct routes to chaotic firing in pyramidal neurons.

Frontiers in computational neuroscience·2026
See all related articles

Related Experiment Video

Updated: Jun 7, 2026

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

Sensitivity analysis for the EEG forward problem.

Maria Inés Troparevsky1, Diana Rubio, Nicolas Saintier

  • 1Departamento de Matemática, Facultad de Ingeniería, Universidad de Buenos Aires Buenos Aires, Argentina.

Frontiers in Computational Neuroscience
|October 27, 2010
PubMed
Summary
This summary is machine-generated.

This study applies traditional and generalized sensitivity functions to analyze electroencephalography models. The methods help identify key parameters in brain imaging by measuring output variations.

Keywords:
forward problem of electroencephalographygeneralized sensitivitysensitivitytraditional sensitivity

More Related Videos

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

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

Related Experiment Videos

Last Updated: Jun 7, 2026

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

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

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

Area of Science:

  • Computational Neuroscience
  • Biomedical Engineering
  • Mathematical Modeling

Background:

  • Sensitivity analysis is crucial for identifying system parameters by measuring output variations.
  • Traditional Sensitivity Functions (TSF) and Generalized Sensitivity Functions (GSF) are established methods.
  • These functions aid in determining optimal time instants for parameter estimation in dynamical systems.

Purpose of the Study:

  • To apply TSF and GSF for analyzing the sensitivity of the 3D Poisson-type equation in electroencephalography (EEG).
  • To investigate the sensitivity of EEG forward problems with respect to tissue conductivity.
  • To compare the performance of TSF and GSF in a simplified head model.

Main Methods:

  • Established differential and integral equations for TSF concerning tissue conductivity in a nested homogeneous head model.
  • Computed GSF for the same simplified head model.
  • Performed numerical experiments to compare TSF and GSF results.

Main Results:

  • Successfully applied TSF and GSF to a 3D Poisson-type equation relevant to EEG.
  • Derived specific differential and integral equations for TSF in the context of EEG.
  • Numerical experiments provided comparative insights into the application of both sensitivity functions.

Conclusions:

  • TSF and GSF are effective tools for analyzing the sensitivity of EEG forward problems.
  • The study provides a framework for understanding parameter sensitivity in simplified head models.
  • This analysis can inform more accurate parameter estimation in EEG-based brain imaging.