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6 - EEG and MEG: forward modeling

Published online by Cambridge University Press:  05 October 2012

By
Jan C. de Munck ,
Carsten H. Wolters  and
Maureen Clerc
Edited by
Romain Brette  and
Alain Destexhe
Jan C. de Munck
Affiliation:
VU University Medical Centre, The Netherlands
Carsten H. Wolters
Affiliation:
University of Münster, Germany
Maureen Clerc
Affiliation:
INRIA Sophia Antipolis Méditerranée, France
Romain Brette
Affiliation:
Ecole Normale Supérieure, Paris
Alain Destexhe
Affiliation:
Centre National de la Recherche Scientifique (CNRS), Paris
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Summary

Introduction

The electroencephalogram (EEG) represents potential differences recorded from the scalp as function of time (Niedermayer and Lopes da Silva, 1987). 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 magnetoencephalographic (MEG) equipment (Hämäläinen et al., 1993). When EEG and MEG are studied in the time or frequency domain, several rhythms can be discriminated that contain valuable information about the collective behavior 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, we consider the mathematical problem of predicting the spatial distribution of MEEG, from several physiological 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 of extracting as much information as possible about the cerebral current sources, on the basis of MEEG data. Both the forward and the inverse problems are formulated within the framework of a certain mathematical model, wherein the underlying physiological assumptions are precisely formulated.

Type
Chapter
Information
Handbook of Neural Activity Measurement , pp. 192 - 256
Publisher: Cambridge University Press
Print publication year: 2012

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