Inverse Modeling for MEG/EEG data
It provides a comprehensive overview for researchers and clinicians working on MEG/EEG source localization, but is incremental as it surveys existing methods.
This paper reviews mathematical methods for reconstructing brain activity from MEG/EEG data, covering regularization and Bayesian inference techniques, and applies them to pre-surgical evaluation of epileptic patients.
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.