Harmoni: A method for eliminating spurious interactions due to the harmonic components in neuronal data

dc.contributor.authorIdaji, Mina Jamshidi
dc.contributor.authorZhang, Juanli
dc.contributor.authorStephani, Tilman
dc.contributor.authorNolte, Guido
dc.contributor.authorMüller, Klaus-Robert
dc.contributor.authorVillringer, Arno
dc.contributor.authorNikulin, Vadim V.
dc.date.accessioned2022-12-30T13:29:39Z
dc.date.available2022-12-30T13:29:39Z
dc.date.issued2022-03-02
dc.description.abstractCross-frequency synchronization (CFS) has been proposed as a mechanism for integrating spatially and spectrally distributed information in the brain. However, investigating CFS in Magneto- and Electroencephalography (MEG/EEG) is hampered by the presence of spurious neuronal interactions due to the non-sinusoidal waveshape of brain oscillations. Such waveshape gives rise to the presence of oscillatory harmonics mimicking genuine neuronal oscillations. Until recently, however, there has been no methodology for removing these harmonics from neuronal data. In order to address this long-standing challenge, we introduce a novel method (called HARMOnic miNImization - Harmoni) that removes the signal components which can be harmonics of a non-sinusoidal signal. Harmoni’s working principle is based on the presence of CFS between harmonic components and the fundamental component of a non-sinusoidal signal. We extensively tested Harmoni in realistic EEG simulations. The simulated couplings between the source signals represented genuine and spurious CFS and within-frequency phase synchronization. Using diverse evaluation criteria, including ROC analyses, we showed that the within- and cross-frequency spurious interactions are suppressed significantly, while the genuine activities are not affected. Additionally, we applied Harmoni to real resting-state EEG data revealing intricate remote connectivity patterns which are usually masked by the spurious connections. Given the ubiquity of non-sinusoidal neuronal oscillations in electrophysiological recordings, Harmoni is expected to facilitate novel insights into genuine neuronal interactions in various research fields, and can also serve as a steppingstone towards the development of further signal processing methods aiming at refining within- and cross-frequency synchronization in electrophysiological recordings.en
dc.description.sponsorshipDFG, 424778381, TRR 295: Behandlung motorischer Netzwerkstörungen mittels Neuromodulation
dc.identifier.eissn1095-9572
dc.identifier.issn1053-8119
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/17925
dc.identifier.urihttps://doi.org/10.14279/depositonce-16715
dc.language.isoen
dc.relation.ispartof10.14279/depositonce-16416
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc620 Ingenieurwissenschaften und zugeordnete Tätigkeitende
dc.subject.othercross-frequency couplingen
dc.subject.othernon-sinusoidal oscillationsen
dc.subject.otherspurious interactionsen
dc.subject.otherHarmonic Minimizationen
dc.titleHarmoni: A method for eliminating spurious interactions due to the harmonic components in neuronal dataen
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.articlenumber119053
dcterms.bibliographicCitation.doi10.1016/j.neuroimage.2022.119053
dcterms.bibliographicCitation.journaltitleNeuroImage
dcterms.bibliographicCitation.originalpublishernameElsevier
dcterms.bibliographicCitation.originalpublisherplaceAmsterdam
dcterms.bibliographicCitation.volume252
dcterms.rightsHolder.referenceCreative-Commons-Lizenz
tub.accessrights.dnbfree
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Softwaretechnik und Theoretische Informatik::FG Maschinelles Lernen
tub.publisher.universityorinstitutionTechnische Universität Berlin

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