Paper Info
Title
Better conditioning the MEG/EEG inverse problem: the multivariate source prelocalization approach
Abstract
The recently proposed multivariate source prelocalization (MSP) proved efficient and robust for restricting the solution space of the highly under-determined MEG/EEG inverse problem through the estimation of a probability-like coefficient of activation for each cortical area. It makes MSP a good candidate for initializing conventional inverse methods in order to improve electromagnetic source reconstruction. In this paper, we evaluate the benefit of MSP as a preprocessing step for a classical iterative weighted minimum norm algorithm, the FOCUSS approach, whose results highly rely upon its initialization. By using Monte Carlo simulations, we demonstrate the usefulness of such a coupling. Moreover, we quantify the benefits due on the one hand, to the reduction of the solution space and, on the other hand, to the explicit use as functional prior knowledge, of the activation probabilities inferred by MSP.
Year
DOI
Venue
2004
10.1109/ISBI.2004.1398799
ISBI
Keywords
Field
DocType
Monte Carlo methods,electroencephalography,inverse problems,iterative methods,magnetoencephalography,probability,EEG inverse problem,FOCUSS approach,MEG inverse problem,Monte Carlo simulation,activation probability,electromagnetic source reconstruction,iterative weighted minimum norm algorithm,multivariate source prelocalization approach,probability-like coefficient estimation
Monte Carlo method,Pattern recognition,Iterative method,Computer science,Multivariate statistics,Robustness (computer science),Preprocessor,Artificial intelligence,Inverse problem,Initialization,Magnetoencephalography
Conference
ISBN
Citations 
PageRank 
0-7803-8388-5
0
0.34
References 
Authors
1
4
Name
Order
Citations
PageRank
Jérémie Mattout178448.61
Jean Daunizeau2140671.53
M. Pelegrini-Issac3385.81
L Garnero415619.86