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Clin Neurophysiol. 2012 Jul;123(7):1353-60. doi: 10.1016/j.clinph.2011.12.004. Epub 2012 Jan 2.

Cross-frequency decomposition: a novel technique for studying interactions between neuronal oscillations with different frequencies.

Author information

1
Neurophysics Group, Department of Neurology, Campus Benjamin Franklin, Charité - University Medicine Berlin, D-12200 Berlin, Germany. vadim.nikulin@charite.de

Abstract

OBJECTIVE:

We present a novel method for the extraction of neuronal components showing cross-frequency phase synchronization.

METHODS:

In general the method can be applied for the detection of phase interactions between components with frequencies f1 and f2, where f2 ≈ rf1 and r is some integer. We refer to the method as cross-frequency decomposition (CFD), which consists of the following steps: (a) extraction of f1-oscillations with the spatio-spectral decomposition algorithm (SSD); (b) frequency modification of the f1-oscillations obtained with SSD; and (c) finding f2-oscillations synchronous with f1-oscillations using least-squares estimation.

RESULTS:

Our simulations showed that CFD was capable of recovering interacting components even when the signal-to-noise ratio was as low as 0.01. An application of CFD to the real EEG data demonstrated that cross-frequency phase synchronization between alpha and beta oscillations can originate from the same or remote neuronal populations.

CONCLUSIONS:

CFD allows a compact representation of the sets of interacting components. The application of CFD to EEG data allows differentiating cross-frequency synchronization arising due to genuine neurophysiological interactions from interactions occurring due to quasi-sinusoidal waveform of neuronal oscillations.

SIGNIFICANCE:

CFD is a method capable of extracting cross-frequency coupled neuronal oscillations even in the presence of strong noise.

PMID:
22217959
DOI:
10.1016/j.clinph.2011.12.004
[Indexed for MEDLINE]

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