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

Highlights

• Three frequency estimation measures called instantaneous frequency, local frequency and peak frequency are explained and compared.

• We also present three novel multivariate methods for the extraction of frequency shifts. They are based on frequency changes detected by instantaneous frequency, local frequency or peak frequency.

• The proposed decomposition methods extract brain sources whose frequency estimate of interest is maximally correlated to the external/internal variable of interest.

• All methods were thoroughly validated in realistic simulations and with real EEG data of 24 participants who performed a steady-state visual evoked paradigm in a BCI experiment.

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.