Inverse Modeling for MEG/EEG Data

Chapter
Part of the Springer INdAM Series book series (SINDAMS, volume 24)

Abstract

We provide an overview of the state-of-the-art for mathematical methods that are used to reconstruct brain activity from neurophysiological data. After a brief introduction on the mathematics of the forward problem, we discuss standard and recently proposed regularization methods, as well as Monte Carlo techniques for Bayesian inference. We classify the inverse methods based on the underlying source model, and discuss advantages and disadvantages. Finally we describe an application to the pre-surgical evaluation of epileptic patients.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2017

Authors and Affiliations

  1. 1.Dipartimento di MatematicaUniversità degli Studi di GenovaGenovaItaly
  2. 2.CNR–SPINGenovaItaly

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