Model selection in electromagnetic source analysis with an application to VEF’s (2002)
| Venue: | IEEE Transactions on Biomedical Engineering |
| Citations: | 7 - 4 self |
BibTeX
@ARTICLE{Waldorp02modelselection,
author = {Lourens J. Waldorp and Hilde M. Huizenga and Raoul P. P. P. Grasman and Koen B. E. Böcker and Jan C. De Munck and Peter C. M. Molenaar},
title = {Model selection in electromagnetic source analysis with an application to VEF’s},
journal = {IEEE Transactions on Biomedical Engineering},
year = {2002},
volume = {49},
pages = {1121--1129}
}
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Abstract
Abstract — In electromagnetic source analysis it is necessary to determine how many sources are required to describe the EEG or MEG adequately. Model selection procedures (MSP’s, or goodness of fit procedures) give an estimate of the required number of sources. Existing and new MSP’s are evaluated in different source and noise settings: two sources which are close or distant, and noise which is uncorrelated or correlated. The commonly used MSP residual variance is seen to be ineffective, that is it often selects too many sources. Alternatives like the adjusted Hotelling’s test, Bayes information criterion, and the Wald test on source amplitudes are seen to be effective. The adjusted Hotelling’s test is recommended if a conservative approach is taken, and MSP’s such as Bayes information criterion or the Wald test on source amplitudes are recommended if a more liberal approach is desirable. The MSP’s are applied to empirical data (visual evoked fields). I.
