RT Journal Article
SR Electronic
T1 Inverse Modeling for MEG/EEG Data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 135269
DO 10.1101/135269
A1 Alberto Sorrentino
A1 Michele Piana
YR 2017
UL http://biorxiv.org/content/early/2017/05/08/135269.abstract
AB 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.