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

Real-time electric-field neuronavigation on realistic head models for conventional and multi-locus TMS.

Brain stimulation·2026
Same author

A common framework for semantic memory and semantic composition.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Separating feedforward and feedback dynamics using time-frequency resolved connectivity: A hybrid model of left ventral occipitotemporal cortex in word reading.

Imaging neuroscience (Cambridge, Mass.)·2025
Same author

Feasibility Study of Supplementary Motor Area to Primary Motor Cortex Facilitation using Multi-locus Transcranial Magnetic Stimulation<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Linear and nonlinear multidimensional functional connectivity methods reveal similar networks for semantic processing in EEG/MEG data.

Frontiers in human neuroscience·2025
Same author

Word-selective EEG/MEG responses in the English language obtained with fast periodic visual stimulation (FPVS).

Imaging neuroscience (Cambridge, Mass.)·2025

Related Experiment Video

Updated: May 11, 2026

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

Minimum-norm cortical source estimation in layered head models is robust against skull conductivity error.

Matti Stenroos1, Olaf Hauk2

  • 1MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, Cambridge CB2 7EF, UK; Department of Biomedical Engineering and Computational Science, Aalto University, P.O. Box 12200, FI-00076 Aalto, Finland.

Neuroimage
|May 4, 2013
PubMed
Summary

Uncertainty in skull conductivity has minimal impact on electroencephalography (EEG) and magnetoencephalography (MEG) source estimation using minimum-norm (MN) methods. Researchers can confidently apply MN estimation to EEG and MEG+EEG data despite skull conductivity variations.

Keywords:
ElectroencephalographyInverse problemMagnetoencephalographyMinimum-norm estimationSkull conductivity

More Related Videos

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

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

Related Experiment Videos

Last Updated: May 11, 2026

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

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

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

Area of Science:

  • Neuroscience
  • Biophysics
  • Biomedical Engineering

Background:

  • The conductivity of the human skull significantly influences electroencephalography (EEG) signals.
  • Previous studies using dipole models suggested skull conductivity errors detrimentally affect EEG source estimation.
  • Dipole models are limited and their findings may not generalize to other source estimation techniques.

Purpose of the Study:

  • To investigate the sensitivity of EEG and combined MEG+EEG source estimation to skull conductivity errors.
  • To evaluate the impact of skull conductivity variations on distributed source models using minimum-norm (MN) estimation.

Main Methods:

  • A realistic three-layer anatomical head model was constructed using segmented cortical surfaces.
  • Forward models were generated using the Galerkin boundary element method with varied skull conductivity.
  • Lead-field topographies and MN spatial filter vectors were analyzed for morphological and amplitude changes.
  • Localization performance and spatial spread of MN estimators were assessed using resolution metrics.

Main Results:

  • Minimum-norm (MN) estimators demonstrated robustness against skull conductivity errors.
  • Variations in skull conductivity moderately affected lead-field and spatial filter amplitudes but had minimal impact on their morphologies.
  • Localization performance of EEG and MEG+EEG MN estimators was minimally impacted by conductivity errors.
  • Spatial spread of the estimates showed slight variations.

Conclusions:

  • Uncertainty in skull conductivity should not deter the application of minimum-norm estimation for EEG and combined MEG+EEG data.
  • Results contrast with dipole model findings, highlighting the importance of using diverse source estimation methods for evaluating imaging modalities.