EEG and MEG: forward modeling

Abstract : The electro-encephalogram (EEG) represents potential differences recorded from the scalp as function of time. The generators of the EEG consist of time varying ionic currents generated in the brain by biochemical sources. These current sources also generate a small but measurable magnetic induction field, which can be recorded with magneto-encephalographic (MEG) equipment. When EEG and MEG are studied in the time or frequency domain, several rhythms can be discriminated that contain valuable information about the collective behaviour of the living human brain as a neural network. In this chapter EEG and MEG are discussed in the spatial domain. We consider that these signals are recorded from multiple sensors with known positions and study the spatial distribution of EEG and MEG (in the sequel abbreviated as MEEG) in relation to the spatial distribution of the underlying sources. More precisely, the mathematical problem is considered to predict the spatial distribution of MEEG, from several precisely defined assumptions on the current sources. This problem is commonly named "The Forward Problem". Solutions of the forward problem that are fast, accurate and practical are indispensable ingredients for the solution of the "Inverse Problem" or "Backward Problem", which is the problem to extract as much information as possible about the cerebral current sources, on the basis of MEEG data. In a living human brain there are many processes that are associated with ionic currents implying that there could be a large variety of mechanisms contributing to the MEEG. However, considering that MEEG are generally recorded in the frequency range from 1 to 200 Hz, in reality there is a generator mechanism that dominates the others, giving rise to the current dipole model. In section 2 the physiological basis of the current dipole model is presented together with a few other elementary biophysical assumptions. Forward models need to be both fast and accurate, which are competing demands. Different approaches have been presented in the literature: analytic models, Boundary Element Method (BEM), Finite Element Method (FEM) and others. These are reviewed in sections 3 to 6. Section 7 contains a general discussion and an outlook for further research.
Type de document :
Chapitre d'ouvrage
Brette, Romain and Destexhe, Alain. Handbook of Neural Activity Measurement, Cambridge University Press, pp.192-256, 2012, 978-0-521-51622-8
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Contributeur : Maureen Clerc <>
Soumis le : vendredi 28 septembre 2012 - 11:29:13
Dernière modification le : jeudi 11 janvier 2018 - 16:20:57

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  • HAL Id : hal-00736444, version 1

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Jan De Munck, Carsten Wolters, Maureen Clerc. EEG and MEG: forward modeling. Brette, Romain and Destexhe, Alain. Handbook of Neural Activity Measurement, Cambridge University Press, pp.192-256, 2012, 978-0-521-51622-8. 〈hal-00736444〉

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