Literature DB >> 15759571

Model selection in spatio-temporal electromagnetic source analysis.

Lourens J Waldorp1, Hilde M Huizenga, Arye Nehorai, Raoul P P P Grasman, Peter C M Molenaar.   

Abstract

Several methods [model selection procedures (MSPs)] to determine the number of sources in electroencephalogram (EEG) and magnetoencphalogram (MEG) data have previously been investigated in an instantaneous analysis. In this paper, these MSPs are extended to a spatio-temporal analysis if possible. It is seen that the residual variance (RV) tends to overestimate the number of sources. The Akaike information criterion (AIC) and the Wald test on amplitudes (WA) and the Wald test on locations (WL) have the highest probabilities of selecting the correct number of sources. The WA has the advantage that it offers the opportunity to test which source is active at which time sample.

Mesh:

Year:  2005        PMID: 15759571     DOI: 10.1109/TBME.2004.842982

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  4 in total

1.  Estimation of number of independent brain electric sources from the scalp EEGs.

Authors:  Xiaoxiao Bai; Bin He
Journal:  IEEE Trans Biomed Eng       Date:  2006-10       Impact factor: 4.538

2.  Activated region fitting: a robust high-power method for fMRI analysis using parameterized regions of activation.

Authors:  Wouter D Weeda; Lourens J Waldorp; Ingrid Christoffels; Hilde M Huizenga
Journal:  Hum Brain Mapp       Date:  2009-08       Impact factor: 5.038

3.  Spatio-temporal Bayesian model selection for disease mapping.

Authors:  R Carroll; A B Lawson; C Faes; R S Kirby; M Aregay; K Watjou
Journal:  Environmetrics       Date:  2016-09-28       Impact factor: 1.900

4.  The distressed brain: a group blind source separation analysis on tinnitus.

Authors:  Dirk De Ridder; Sven Vanneste; Marco Congedo
Journal:  PLoS One       Date:  2011-10-06       Impact factor: 3.240

  4 in total

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