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Handb Clin Neurol. 2019;160:85-101. doi: 10.1016/B978-0-444-64032-1.00006-0.

EEG source localization.

Author information

1
Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland; Center for Biomedical Imaging (CIBM) Lausanne-Geneva, Geneva, Switzerland. Electronic address: christoph.michel@unige.ch.
2
Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States.

Abstract

Since the discovery of electroencephalography (EEG), when it was hoped that EEG would offer "a window into the brain," researchers and clinicians have attempted to localize the neuronal activity in the brain that generates the scalp potentials measured noninvasively with EEG. Early explorations in the 1950s using electric field theory to infer the location and orientation of the current dipole in the brain from the scalp potential distribution triggered considerable efforts to quantitatively deduce these sources. Initially, dipole fitting, or dipole localization, was the method of choice and many studies used this approach in experimental and clinical studies with remarkable success. Later on, new methods were proposed that attempted to overcome the problem of having to fix the number of sources a priori; these methods are known as distributed source imaging techniques. The introduction and increasing availability of magnetic resonance imaging, allowing detailed realistic anatomy of the brain and head to be incorporated in source localization methods, has drastically increased the precision of such approaches. Today, source localization of EEG (and magnetoencephalography, or MEG) has reached a level of consistency and precision that allows these methods to be placed in the family of brain imaging techniques. The particular advantage that they have over other imaging methods is their high temporal resolution, which allows the origin of activity to be distinguished from its propagation and information flow in large-scale brain networks to be examined. This chapter gives an overview of these methods and illustrates them with several examples, thereby focusing on EEG source imaging in epilepsy and presurgical planning, as clinical applications with remarkable maturation.

KEYWORDS:

Connectivity; EEG source imaging; Epilepsy; Forward problem; Inverse problem; Surgical planning

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