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Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings

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dc.contributor.authorVidaurre, Carmen
dc.contributor.authorGurunandan, Kshipraes_ES
dc.contributor.authorJamshidi Idaji, Minaes_ES
dc.contributor.authorNolte, Guidoes_ES
dc.contributor.authorGómez Fernández, Marisol
dc.contributor.authorVillringer, Arnoes_ES
dc.contributor.authorMüller, Klaus Robertes_ES
dc.contributor.authorNikulin, Vadim V.es_ES
dc.contributor.departmentEstadística, Informática y Matemáticases_ES
dc.contributor.departmentEstatistika, Informatika eta Matematikaeu
dc.date.accessioned2023-10-10T07:26:05Z
dc.date.available2023-10-10T07:26:05Z
dc.date.issued2023
dc.date.updated2023-10-10T07:13:08Z
dc.description.abstractInstantaneous and peak frequency changes in neural oscillations have been linked to many perceptual, motor, and cognitive processes. Yet, the majority of such studies have been performed in sensor space and only occasionally in source space. Furthermore, both terms have been used interchangeably in the literature, although they do not reflect the same aspect of neural oscillations. In this paper, we discuss the relation between instantaneous frequency, peak frequency, and local frequency, the latter also known as spectral centroid. Furthermore, we propose and validate three different methods to extract source signals from multichannel data whose (instantaneous, local, or peak) frequency estimate is maximally correlated to an experimental variable of interest. Results show that the local frequency might be a better estimate of frequency variability than instantaneous frequency under conditions with low signal-to-noise ratio. Additionally, the source separation methods based on local and peak frequency estimates, called LFD and PFD respectively, provide more stable estimates than the decomposition based on instantaneous frequency. In particular, LFD and PFD are able to recover the sources of interest in simulations performed with a realistic head model, providing higher correlations with an experimental variable than multiple linear regression. Finally, we also tested all decomposition methods on real EEG data from a steady-state visual evoked potential paradigm and show that the recovered sources are located in areas similar to those previously reported in other studies, thus providing further validation of the proposed methods.en
dc.description.sponsorshipC.V. was supported by the Spanish Ministry of Economy with Grant RyC 2014-15671, Spanish Ministry of Research and Innovation PID2020-118829RB-100, H2020-FETPROACT-EIC-2018-2020 Grant MAIA-951910, Diputacion Foral de Gipuzkoa Brain2Move project, Diputacion Foral de Gipuzkoa Neurocog Project, and Ikerbasque (Basque Foundation for Science). K.G. was supported by the Basque Government postdoctoral grant POS-2021-1-0007. G.N. was partially funded by the German Research Foundation (DFG, SFB936 Z3 and TRR169, B4). K.-R.M. work was supported by German Ministry for Education and Research (BMBF) under Grants 01IS14013A-E, 01GQ1115 and 01GQ0850; by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grants funded by the Korea government (MSIT) (No. 2019-0-00079, Artificial Intelligence Graduate School Program, Korea University and No. 2022-0-00984, Development of Artificial Intelligence Technology for Personalized Plug-and-Play Explanation and Verification of Explanation).en
dc.format.mimetypeapplication/pdfen
dc.identifier.citationVidaurre, C., Gurunandan, K., Idaji, M. J., Nolte, G., Gómez, M., Villringer, A., Müller, K.-R., & Nikulin, V. V. (2023). Novel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordings. NeuroImage, 276, 120178. https://doi.org/10.1016/j.neuroimage.2023.120178en
dc.identifier.doi10.1016/j.neuroimage.2023.120178
dc.identifier.issn1053-8119
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/46483
dc.language.isoengen
dc.publisherElsevieren
dc.relation.ispartofNeuroImage 276 (2023) 120178en
dc.relation.projectIDinfo:eu-repo/grantAgreement/European Commission/Horizon 2020 Framework Programme/951910en
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118829RB-I00/ES/en
dc.relation.publisherversionhttps://doi.org/10.1016/j.neuroimage.2023.120178
dc.rights© 2023 This is an open access article under the CC BY-NC-ND license.en
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCorrelation optimizationen
dc.subjectDecomposition methodsen
dc.subjectElectroencephalography (EEG)en
dc.subjectInstantaneous frequencyen
dc.subjectLocal frequencyen
dc.subjectMagnetoencephalography (MEG)en
dc.subjectMultimodal methodsen
dc.subjectMultiple linear regressionen
dc.subjectMultivariate methodsen
dc.subjectPeak frequencyen
dc.subjectSource separationen
dc.subjectSpatial filtersen
dc.subjectSpectral centroiden
dc.titleNovel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordingsen
dc.typeArtículo / Artikuluaes
dc.typeinfo:eu-repo/semantics/articleen
dc.type.versionVersión publicada / Argitaratu den bertsioaes
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dspace.entity.typePublication
relation.isAuthorOfPublicationbfc272aa-95a8-45b2-ada9-5e679009a082
relation.isAuthorOfPublication71fc3a8f-62c3-41cf-bca2-eeaaa41d54af
relation.isAuthorOfPublication.latestForDiscoverybfc272aa-95a8-45b2-ada9-5e679009a082

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