DocumentCode
1487290
Title
The effect of artifact rejection by signal-space projection on source localization accuracy in MEG measurements
Author
Nolte, Guido ; Curio, Gabriel
Author_Institution
Dept. of Neurology, Freie Univ. Berlin, Germany
Volume
46
Issue
4
fYear
1999
fDate
4/1/1999 12:00:00 AM
Firstpage
400
Lastpage
408
Abstract
The consequences of artifact suppression by means of signal-space projection on dipole localization accuracy for magnetoencephalography measurements are studied. Approximate analytical formulas, equivalent to the Cramer-Rao bound, are presented and verified by Monte Carlo simulations which relate the increase of localization error for individual coordinates to the similarity of the artifact field and respective (contravariant) quadrupole fields obtained by differentiating the dipole field with respect to its origin. The expressions simplify significantly for dipoles placed below the center of the measuring system giving rise to highly symmetric field patterns. Formulas are presented both for single- and for multiple-artifact rejection. As illustrative examples artifact fields are constructed which (a) lead to highly decreasing signal-to-noise ratio and goodness-of-fit (GOF), while the localization error is unaffected for all coordinates and (b) lead to an increase of localization error while the SNR and the GOF stays constant. Finally, the rich structure of localization error increase is demonstrated for a class of artifact fields originating from artifact current dipoles.
Keywords
Monte Carlo methods; magnetoencephalography; measurement errors; medical signal processing; Cramer-Rao bound; MEG measurements; approximate analytical formulas; artifact rejection; contravariant quadrupole fields; localization error; signal-space projection; source localization accuracy; Brain; Conductors; Current density; Data analysis; Magnetic analysis; Magnetic field measurement; Magnetic heads; Magnetoencephalography; Nervous system; Signal to noise ratio; Artifacts; Bayes Theorem; Electromagnetic Fields; Likelihood Functions; Magnetoencephalography; Models, Cardiovascular; Models, Statistical; Signal Processing, Computer-Assisted;
fLanguage
English
Journal_Title
Biomedical Engineering, IEEE Transactions on
Publisher
ieee
ISSN
0018-9294
Type
jour
DOI
10.1109/10.752937
Filename
752937
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