Vidaurre, CarmenGurunandan, KshipraJamshidi Idaji, MinaNolte, GuidoGómez Fernández, MarisolVillringer, ArnoMüller, Klaus RobertNikulin, Vadim V.2023-10-102023-10-102023Vidaurre, 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.2023.1201781053-811910.1016/j.neuroimage.2023.120178https://academica-e.unavarra.es/handle/2454/46483Instantaneous 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.application/pdfeng© 2023 This is an open access article under the CC BY-NC-ND license.Correlation optimizationDecomposition methodsElectroencephalography (EEG)Instantaneous frequencyLocal frequencyMagnetoencephalography (MEG)Multimodal methodsMultiple linear regressionMultivariate methodsPeak frequencySource separationSpatial filtersSpectral centroidNovel multivariate methods to track frequency shifts of neural oscillations in EEG/MEG recordingsArtículo / Artikulua2023-10-10Acceso abierto / Sarbide irekiainfo:eu-repo/semantics/openAccess