Skip to contentScholarGate
LibraryBookshelfDeskReview StudioAssistant
Sign in
On this page
IntuitionHow it worksWhen to use itStrengths & limitationsCommon pitfallsApplicationsFrequently asked🔒 Read the full methodSourcesRelated methods
Cite this pageSpotted an issue on this page? Report or suggest a fix →
Home›Neuroimaging›MEG Source Localization
Process / pipelineInverse problem solution

MEG Source Localization

Magnetoencephalography Source Localization · Also known as: MEG localization, magnetic source imaging, MSI

Magnetoencephalography (MEG) source localization is the inverse problem of estimating where in the brain neural currents originate from magnetic field measurements at the scalp. Introduced by David Cohen in 1972, MEG offers superior temporal resolution (milliseconds) and spatial specificity compared to EEG, as magnetic fields are less distorted by tissue conductivity, enabling researchers to pinpoint neural activity with high precision.

ScholarGate
  1. Process / pipeline
  2. v1
  3. 2 Sources
  4. PUBLISHED
Cite this page →
Tools & resources
Download slides
Learn & explore

Read the full method

Members only

Sign in with a free account to read this section.

Sign in

Method map

The neighbourhood of related methods — select a node to explore.

MEG Source Localization
Dynamic Causal ModelingeLORETAEvent-Related Potential…Spike Sorting

When to use it

MEG source localization is appropriate when high temporal and spatial resolution is required simultaneously, when sources are focal or regionally clustered, and when available resources permit (MEG systems are expensive). Use MEG for studying motor, sensory, and language systems where precise anatomical localization is critical. Avoid MEG when distributed sources are expected (use fMRI instead) or when cost constraints are severe.

Strengths & limitations

Strengths
  • Superior spatial resolution compared to EEG; magnetic fields bypass tissue distortion, yielding more accurate localization
  • Exceptional temporal resolution (milliseconds); tracks dynamic source changes in real time
  • Magnetic fields are reference-independent; no ambiguity from electrode placement (unlike EEG reference effects)
  • Works well for focal sources (e.g., motor cortex, primary sensory cortex); dipole fitting is accurate and interpretable
  • Combines advantages of EEG (temporal) and fMRI (spatial): superior to both on complementary dimensions
Limitations
  • Insensitive to currents aligned perpendicular to the scalp surface; radial sources (gyral crowns) often missed
  • Expensive and specialized equipment; MEG systems cost millions and require dedicated shielded facilities
  • Distributed source inversions are mathematically under-determined; multiple source configurations fit equally well
  • Limited sensitivity to deep subcortical sources; strong dependence on source-sensor distance

Frequently asked

Why can't MEG detect all neural sources?

MEG is insensitive to sources oriented radially (perpendicular to scalp). This affects gyral crowns, which generate radial currents. MEG primarily detects tangential (parallel to scalp) currents from sulcal walls. fMRI is complementary, detecting radial sources. Combining MEG and fMRI provides comprehensive source coverage.

What is the difference between dipole fitting and distributed inverse solutions?

Dipole fitting assumes a small number of focal sources (1–5) and estimates location, orientation, and strength. Distributed methods estimate current at many locations simultaneously. Dipole fitting is accurate for focal sources but biased for distributed activity. Distributed methods handle distributed sources but are poorly determined (many solutions fit equally well). Choose based on expected source geometry.

How accurate is MEG source localization?

For focal sources in well-characterized anatomy (motor cortex, visual cortex), localization error is typically 5–10 mm. Error increases for deep sources or distributed activity. Anatomical variability and model misspecification contribute substantially. Always report confidence intervals and validate with independent methods when possible.

Can MEG be used clinically?

Yes, for epilepsy surgery planning and presurgical brain mapping. MEG localizes seizure foci and eloquent cortex (motor, language) with precision comparable to or exceeding fMRI. Clinical MEG requires specialized training and interpretation. Insurance coverage varies; it is widely used but not universally available.

Sources

  1. Hauk, O., Friston, K. J., & Leff, A. (2019). Functional neuroimaging of language: understanding the complex relationships between localization and function. Journal of Neurolinguistics, 50, 236–250. link ↗
  2. Halgren, E., Marinkovic, K., & Chauvel, P. (2006). Generators of the late cognitive potentials in auditory and visual oddball tasks. Electroencephalography and Clinical Neurophysiology, 106(2), 156–164. link ↗

How to cite this page

ScholarGate. (2026, June 3). Magnetoencephalography Source Localization. ScholarGate. https://scholargate.app/en/neuroimaging/meg-source-localization

Related methods

Dynamic Causal ModelingeLORETAEvent-Related Potential Analysis

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

  • Dynamic Causal ModelingNeuroimaging↔ compare
  • eLORETANeuroimaging↔ compare
  • Event-Related Potential AnalysisNeuroimaging↔ compare
Compare side by side →

Referenced by

eLORETAEvent-Related Potential AnalysisSpike Sorting

Similar methods

eLORETAEvent-Related Potential AnalysisfNIRS AnalysisSpike SortingDynamic Causal ModelingGraph Brain Network AnalysisPhase-Locking ValueQuantitative Susceptibility Mapping

Related reference concepts

Cognitive NeuroscienceNeuroimaging of LanguageElectrophysiologyStructural and Functional NeuroimagingStereotactic Reference Frames and AtlasesFunctional and Stereotactic Neurosurgery

Spotted an issue on this page? Report or suggest a fix →

ScholarGate — MEG Source Localization (Magnetoencephalography Source Localization). Retrieved 2026-07-21 from https://scholargate.app/en/neuroimaging/meg-source-localization · Dataset: https://doi.org/10.5281/zenodo.20539026
Quick facts
Originator
David Cohen
Subfamily
Inverse problem solution
Year
1972
Type
MEG neuroimaging analysis pipeline
Related methods
Dynamic Causal ModelingeLORETAEvent-Related Potential Analysis
ScholarGate

A content-first reference library for research methods — what each one is, how it works, and where it comes from.

Open data (CC-BY)

Explore

  • Library
  • Search the library…
  • Browse by field
  • Fields
  • Journey
  • Compare
  • Which method?

Reference

  • Subjects
  • Atlas
  • Glossary
  • Methodology
  • Philosophy

Your tools

  • Bookshelf
  • Desk
  • Chat

Company

  • About
  • Pricing
  • Contact
  • Suggest a method

Entries are compiled from published sources for reference. Verifying the accuracy and suitability of any information for your own use remains your responsibility.

© 2026 ScholarGate · A research-method reference library
  • Privacy
  • Cookies
  • Terms
  • Delete account