Reduced Conductivity Dependence Method for Increase of Dipole Localization Accuracy in the EEG Inverse Problem

2011 ◽  
Vol 58 (5) ◽  
pp. 1430-1440 ◽  
Author(s):  
B R Yitembe ◽  
G Crevecoeur ◽  
R Van Keer ◽  
L Dupré
Keyword(s):  
Inverse Problem ◽  
Dipole Localization ◽  
Localization Accuracy ◽  
Eeg Inverse Problem

scholarly journals The Neuroelectromagnetic Inverse Problem and the Zero Dipole Localization Error

2009 ◽  
Vol 2009 ◽  
pp. 1-11 ◽  
Author(s):  
Rolando Grave de Peralta ◽  
Olaf Hauk ◽  
Sara L. Gonzalez
Keyword(s):  
Inverse Problem ◽  
Unique Solution ◽  
A Priori ◽  
Inverse Solution ◽  
Localization Error ◽  
A Priori Information ◽  
Dipole Localization ◽  
Inverse Solutions ◽  
Priori Information ◽  
Additional Constraints

A tomography of neural sources could be constructed from EEG/MEG recordings once the neuroelectromagnetic inverse problem (NIP) is solved. Unfortunately the NIP lacks a unique solution and therefore additional constraints are needed to achieve uniqueness. Researchers are then confronted with the dilemma of choosing one solution on the basis of the advantages publicized by their authors. This study aims to help researchers to better guide their choices by clarifying what is hidden behind inverse solutions oversold by their apparently optimal properties to localize single sources. Here, we introduce an inverse solution (ANA) attaining perfect localization of single sources to illustrate how spurious sources emerge and destroy the reconstruction of simultaneously active sources. Although ANA is probably the simplest and robust alternative for data generated by a single dominant source plus noise, the main contribution of this manuscript is to show that zero localization error of single sources is a trivial and largely uninformative property unable to predict the performance of an inverse solution in presence of simultaneously active sources. We recommend as the most logical strategy for solving the NIP the incorporation of sound additional a priori information about neural generators that supplements the information contained in the data.

General bounds for electrode mislocation on the EEG inverse problem

2011 ◽  
Vol 103 (1) ◽  
pp. 1-9 ◽  
Author(s):  
L. Beltrachini ◽  
N. von Ellenrieder ◽  
C.H. Muravchik
Keyword(s):  
Inverse Problem ◽  
Eeg Inverse Problem

Assessing the relevance of fMRI-based prior in the EEG inverse problem: a bayesian model comparison approach

2005 ◽  
Vol 53 (9) ◽  
pp. 3461-3472 ◽  
Author(s):  
J. Daunizeau ◽  
C. Grova ◽  
J. Mattout ◽  
G. Marrelec ◽  
D. Clonda ◽  
...  
Keyword(s):  
Inverse Problem ◽  
Bayesian Model ◽  
Model Comparison ◽  
Bayesian Model Comparison ◽  
Comparison Approach ◽  
Eeg Inverse Problem
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