Abstract
Synchronization of neural oscillations as a mechanism of brain function is attracting increasing attention. Neural oscillation is a rhythmic neural activity that can be easily observed by noninvasive electroencephalography (EEG). Neural oscillations show the same frequency and cross-frequency synchronization for various cognitive and perceptual functions. However, it is unclear how this neural synchronization is achieved by a dynamical system. If neural oscillations are weakly coupled oscillators, the dynamics of neural synchronization can be described theoretically using a phase oscillator model. We propose an estimation method to identify the phase oscillator model from real data of cross-frequency synchronized activities. The proposed method can estimate the coupling function governing the properties of synchronization. Furthermore, we examine the reliability of the proposed method using time-series data obtained from numerical simulation and an electronic circuit experiment, and show that our method can estimate the coupling function correctly. Finally, we estimate the coupling function between EEG oscillation and the speech sound envelope, and discuss the validity of these results.
Highlights
Synchronization of neural oscillations is considered an important activity that can help reveal the mechanisms underlying various cognitive functions
We propose an estimation method to identify a dynamical system from rhythmic time-series data
It is well known that such synchronization can be described theoretically by a phase oscillator model under the condition that the rhythmic activities can be considered weakly coupled limit-cycle oscillators
Summary
Synchronization of neural oscillations is considered an important activity that can help reveal the mechanisms underlying various cognitive functions. Neural oscillations are classified into a few frequency bands (e.g. delta, theta and alpha frequency bands) and are synchronized within the same-frequency band between different brain areas during various cognitive tasks [1,2,3,4]. Synchronization of oscillations of the same frequency is considered to integrate distributed brain activities [5] and regulate communication between distant neural groups [6, 7]. 1:p phase synchronization is considered important from the perspective of brain function, to the best of our knowledge, there is no effective and practical method to analyze the 1:p phase synchronization mechanism
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