title: Model selection in electromagnetic source analysis with an application to VEFs.
abstract: In electromagnetic source analysis, it is necessary to determine how many sources are required to describe the electroencephalogram or magnetoencephalogram adequately. Model selection procedures (MSPs) or goodness of fit procedures give an estimate of the required number of sources. Existing and new MSPs 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 MSPs such as Bayes information criterion or the Wald test on source amplitudes are recommended if a more liberal approach is desirable. The MSPs are applied to empirical data (visual evoked fields).
authors.affiliation: Department of Psychology, University of Amsterdam, The Netherlands. waldorp@psy.uva.nl
authors.forename: Lourens J
authors.initials: LJ
authors.lastname: Waldorp
authors.forename: Hilde M
authors.initials: HM
authors.lastname: Huizenga
authors.forename: Raoul P P P
authors.initials: RP
authors.lastname: Grasman
authors.forename: Koen B E
authors.initials: KB
authors.lastname: Böcker
authors.forename: Jan C
authors.initials: JC
authors.lastname: de Munck
authors.forename: Peter C M
authors.initials: PC
authors.lastname: Molenaar
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000295:instrumentation / Q000379:methods*; D003198:Computer Simulation; D004569:Electroencephalography / Q000295:instrumentation / Q000379:methods; D005074:Evoked Potentials, Visual / Q000502:physiology*; D006801:Humans; D015225:Magnetoencephalography / Q000295:instrumentation / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D012680:Sensitivity and Specificity; D013269:Stochastic Processes