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

Date

2023

Authors

Gurunandan, Kshipra
Jamshidi Idaji, Mina
Nolte, Guido
Villringer, Arno
Müller, Klaus Robert
Nikulin, Vadim V.

Director

Publisher

Elsevier
Acceso abierto / Sarbide irekia
Artículo / Artikulua
Versión publicada / Argitaratu den bertsioa

Project identifier

European Commission/Horizon 2020 Framework Programme/951910openaire
AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-118829RB-I00/ES/recolecta

Abstract

Instantaneous 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.

Description

Keywords

Correlation optimization, Decomposition methods, Electroencephalography (EEG), Instantaneous frequency, Local frequency, Magnetoencephalography (MEG), Multimodal methods, Multiple linear regression, Multivariate methods, Peak frequency, Source separation, Spatial filters, Spectral centroid

Department

Estadística, Informática y Matemáticas / Estatistika, Informatika eta Matematika

Faculty/School

Degree

Doctorate program

item.page.cita

Vidaurre, 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.2023.120178

item.page.rights

© 2023 This is an open access article under the CC BY-NC-ND license.

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