AUTHOR=Volk Denis , Dubinin Igor , Myasnikova Alexandra , Gutkin Boris , Nikulin Vadim V. 

TITLE=Generalized Cross-Frequency Decomposition: A Method for the Extraction of Neuronal Components Coupled at Different Frequencies

JOURNAL=Frontiers in Neuroinformatics

VOLUME=Volume 12 - 2018

YEAR=2018

URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2018.00072

DOI=10.3389/fninf.2018.00072

ISSN=1662-5196

ABSTRACT=Perceptual, motor and cognitive processes are based on rich interactions between remote regions in the human brain. Such interactions can be carried out through phase synchronization of oscillatory signals. Neuronal synchronization has been primarily studied within the same frequency range, e.g. within alpha or beta frequency bands. Yet, recent research shows that neuronal populations can also demonstrate phase synchronization between different frequency ranges. An extraction of such cross-frequency interactions in EEG/MEG recordings remains, however, methodologically challenging.

Here we present a new method for robust extraction of cross-frequency phase-to-phase synchronized components. Cross-Frequency Synchrony Analysis (XSA) reconstructs the time courses of synchronized neuronal components, their spatial filters and patterns. The algorithm uses no explicit inverse model and thus evades the usual ambiguity. Our method extends the previous state of the art, Cross-Frequency Decomposition (CFD), to a more general range of frequencies. XSA gives a compact description of non-linearly interacting neuronal sources on the basis of their cross-frequency phase coupling. XSA is applicable for frequencies f1 and f2, f1:f2 = p:q, where p,q are integers. 

The new method has been successfully validated in simulations and tested with real EEG recordings including  resting state data and steady state visually evoked potentials (SSVEP).