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 Experiment Videos

Models of source currents in the brain

R J Ilmoniemi1

  • 1Low Temperature Laboratory, Helsinki University of Technology, Espoo, Finland.

Brain Topography
|January 1, 1993
PubMed
Summary
This summary is machine-generated.

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

The effect of EEG lead configuration on early TMS-EEG artifacts.

bioRxiv : the preprint server for biology·2026
Same author

Methods for analysis of brain connectivity: An IFCN-sponsored review.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology·2019
Same author

Bilateral changes in excitability of sensorimotor cortices during unilateral movement: combined electroencephalographic and transcranial magnetic stimulation study.

Neuroscience·2008
Same author

Prefrontal transcranial magnetic stimulation produces intensity-dependent EEG responses in humans.

NeuroImage·2005
Same author

EEG minimum-norm estimation compared with MEG dipole fitting in the localization of somatosensory sources at S1.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology·2004
Same author

Mismatch negativity indexes auditory temporal resolution: evidence from event-related potential (ERP) and event-related field (ERF) recordings.

Brain research. Cognitive brain research·2003

Electroencephalography (EEG) and magnetoencephalography (MEG) measure brain activity. Accurately localizing brain sources requires advanced estimation theory and prior information to solve the complex inverse problem.

Area of Science:

  • Neuroscience
  • Biophysics
  • Signal Processing

Background:

  • Electroencephalography (EEG) and magnetoencephalography (MEG) measure brain activity via weighted integrals of neural currents.
  • These techniques exhibit differential sensitivities to various brain sources, with EEG lead field determination being more complex than MEG.
  • Accurate source localization is crucial for understanding brain function.

Purpose of the Study:

  • To explore the methodologies for accurately determining brain source locations, directions, and amplitudes using EEG and MEG data.
  • To address the limitations of the dipole model when multiple sources are active.
  • To investigate the application of estimation theory for solving the inverse problem in neuroimaging.

Main Methods:

  • Analysis of signal weighting and source sensitivities for EEG and MEG.

Related Experiment Videos

  • Application of the dipole model for single-source localization.
  • Development and evaluation of multiple-dipole models for complex source configurations.
  • Incorporation of estimation theory and a priori information to solve the inverse problem.

    • Main Results:

    • The dipole model provides accurate results only for single, localized brain sources.
    • Violations of the single-source assumption lead to erroneous localization outcomes.
    • Multiple-dipole models offer improved solutions when several sources are active.
    • Estimation theory, combined with prior information, yields optimal inverse problem solutions.

    Conclusions:

    • Accurate brain source localization using EEG and MEG necessitates advanced inverse problem-solving techniques.
    • The minimum-norm solution is derived under minimal prior information.
    • Supplementary information significantly enhances the resolution and accuracy of source localization.
    • Integrating experimental data with a priori knowledge is key to robust neuroimaging analysis.