Encyclopedia of Computational Neuroscience

Living Edition
| Editors: Dieter Jaeger, Ranu Jung

Forward and Inverse Problems of MEG/EEG

  • Sylvain Baillet
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4614-7320-6_529-1

Definition

The quantitative analysis of scalp MEG and EEG data continues to generate a very diverse body of research based on the characterization of time-resolved brain activity. Some questions however require a more direct assessment of the anatomical substrate of cerebral dynamics. In many cases, modeling the neural generators of scalp MEG/EEG data is the method of choice. From a methodological standpoint, MEG/EEG source modeling is an “inverse problem”: a ubiquitous concept in many fields, from medical imaging to geophysics (Tarantola 2004). It builds a framework that helps conceptualize and formalize the fact that, in experimental sciences, models are confronted with observations to test a set of hypotheses and/or to estimate some parameters that were originally unknown. Parameters are quantities that can be changed without fundamentally invalidating the theoretical model. Predicting observations from a model with a given set of parameters is called forwardmodeling. The...

Keywords

Boundary Element Method Inverse Modeling Head Model Equivalent Current Dipole Neural Source 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Further Reading

  1. Stenroos M, Hunold A, Haueisen J (2014) Comparison of three-shell and simplified volume conductor models in magnetoencephalography. NeuroimageGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Sylvain Baillet
    • 1
  1. 1.McConnell Brain Imaging Centre, Montreal Neurological InstituteMcGill UniversityMontrealCanada