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dc.contributor.authorLópez, José David-
dc.contributor.authorFriston, Karl J.-
dc.contributor.authorEspinosa Oviedo, Jairo José-
dc.contributor.authorLitvak, Vladimir-
dc.contributor.authorBarnes, Gareth Robert-
dc.date.accessioned2023-06-23T15:48:46Z-
dc.date.available2023-06-23T15:48:46Z-
dc.date.issued2014-
dc.identifier.citationLópez, J. D., Litvak, V., Espinosa, J. J., Friston, K., & Barnes, G. R. (2014). Algorithmic procedures for Bayesian MEG/EEG source reconstruction in SPM. NeuroImage, 84, 476–487. https://doi.org/10.1016/j.neuroimage.2013.09.002spa
dc.identifier.issn1053-8119-
dc.identifier.urihttps://hdl.handle.net/10495/35604-
dc.description.abstractABSTRACT: The MEG/EEG inverse problem is ill-posed, giving different source reconstructions depending on the initial assumption sets. Parametric Empirical Bayes allows one to implement most popular MEG/EEG inversion schemes (Minimum Norm, LORETA, etc.) within the same generic Bayesian framework. It also provides a cost-function in terms of the variational Free energy—an approximation to the marginal likelihood or evidence of the solution. In this manuscript, we revisit the algorithm for MEG/EEG source reconstruction with a view to providing a didactic and practical guide. The aim is to promote and help standardise the development and consolidation of other schemes within the same framework. We describe the implementation in the Statistical Parametric Mapping (SPM) software package, carefully explaining each of its stages with the help of a simple simulated data example. We focus on the Multiple Sparse Priors (MSP) model, which we compare with the well-known Minimum Norm and LORETA models, using the negative variational Free energy for model comparison. The manuscript is accompanied by Matlab scripts to allow the reader to test and explore the underlying algorithmspa
dc.format.extent13spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherElsevierspa
dc.type.hasversioninfo:eu-repo/semantics/publishedVersionspa
dc.rightsinfo:eu-repo/semantics/openAccessspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/*
dc.titleAlgorithmic procedures for Bayesian MEG/EEG source reconstruction in SPMspa
dc.typeinfo:eu-repo/semantics/articlespa
dc.publisher.groupSistemas Embebidos e Inteligencia Computacional (SISTEMIC)spa
dc.identifier.doi10.1016/j.neuroimage.2013.09.002-
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.rights.accessrightshttp://purl.org/coar/access_right/c_abf2spa
oaire.citationtitleNeuroImagespa
oaire.citationstartpage476spa
oaire.citationendpage487spa
oaire.citationvolume84spa
oaire.citationissue100spa
dc.rights.creativecommonshttps://creativecommons.org/licenses/by-nc-nd/4.0/spa
oaire.fundernameDepartamento Administrativo de Ciencia, Tecnología e Innovación, COLCIENCIASspa
dc.publisher.placeEstados Unidosspa
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa
dc.type.redcolhttps://purl.org/redcol/resource_type/ARTspa
dc.type.localArtículo de investigaciónspa
dc.subject.decsAlgoritmos-
dc.subject.decsAlgorithms-
dc.subject.decsInteligencia Artificial-
dc.subject.decsArtificial Intelligence-
dc.subject.decsTeorema de Bayes-
dc.subject.decsBayes Theorem-
dc.subject.decsElectroencefalografía - Métodos-
dc.subject.decsElectroencephalography- Métodos-
dc.subject.decsReproducibilidad de los Resultados-
dc.subject.decsReproducibility of Results-
dc.subject.proposalMEG/EEG inverse problemspa
oaire.funderidentifier.crossreffunderRoR:048jthh02-
dc.description.researchgroupidCOL0010717spa
oaire.awardnumber1115-489-25190 y 1115-545-31374spa
dc.relation.ispartofjournalabbrevNeuroImagespa
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