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

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.