Particle Filtering Applied to the EEG Inverse Problem with Dependent Noises

2015 ◽  
pp. 516-519
Author(s):  
Sergio Liberczuk ◽  
Bruno Cernuschi Frías
Keyword(s):  
Inverse Problem ◽  
Particle Filtering ◽  
Eeg Inverse Problem

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

scholarly journals Highly Automated Dipole EStimation (HADES)

2011 ◽  
Vol 2011 ◽  
pp. 1-11 ◽  
Author(s):  
C. Campi ◽  
A. Pascarella ◽  
A. Sorrentino ◽  
M. Piana
Keyword(s):  
Inverse Problem ◽  
User Interface ◽  
Graphical User Interface ◽  
Particle Filtering ◽  
Synthetic Data ◽  
Nonlinear Relationship ◽  
Data Set ◽  
Automatic Estimation ◽  
Dipole Estimation ◽  
Dipolar Model

Automatic estimation of current dipoles from biomagnetic data is still a problematic task. This is due not only to the ill-posedness of the inverse problem but also to two intrinsic difficulties introduced by the dipolar model: the unknown number of sources and the nonlinear relationship between the source locations and the data. Recently, we have developed a new Bayesian approach, particle filtering, based on dynamical tracking of the dipole constellation. Contrary to many dipole-based methods, particle filtering does not assume stationarity of the source configuration: the number of dipoles and their positions are estimated and updated dynamically during the course of the MEG sequence. We have now developed a Matlab-based graphical user interface, which allows nonexpert users to do automatic dipole estimation from MEG data with particle filtering. In the present paper, we describe the main features of the software and show the analysis of both a synthetic data set and an experimental dataset.

Sign in / Sign up

Export Citation Format

Share Document