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Related Experiment Video

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New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies

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Phase synchronization analysis by assessment of the phase difference gradient.

Martin Vejmelka¹, Milan Palus, W T Lee

¹Institute of Computer Science, Academy of Sciences of the Czech Republic, Praha 182 07, Czech Republic. vejmelka@cs.cas.cz

Chaos (Woodbury, N.Y.)

|July 2, 2009

Summary

This summary is machine-generated.

This study introduces a new method to detect phase synchronization in time series data by analyzing the gradient of phase differences. The technique accurately identifies synchronization epochs, even in noisy experimental data.

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Area of Science:

Nonlinear Dynamics
Time Series Analysis
Physiological Signal Processing

Background:

Phase synchronization is a key phenomenon in nonlinear dynamics.
Identifying phase synchronization epochs in experimental data is challenging, especially with noise.
Existing methods like synchrogram analysis may have limitations with real-world data.

Purpose of the Study:

To develop and validate a novel method for identifying phase synchronization epochs.
To assess the robustness of the proposed method in the presence of noise.
To compare the new method with existing synchrogram analysis for cardiorespiratory data.

Main Methods:

Estimating the gradient of generalized phase differences between phase slips in experimental time series.
Developing a statistical test to determine if the gradient is significantly different from zero.
Validating the method using numerical models and comparing with prior results.

Main Results:

The proposed method successfully identifies phase synchronization epochs.
The gradient of generalized phase differences is zero in phase-synchronized systems, even with noise.
Application to human cardiorespiratory data demonstrated the method's efficacy.

Conclusions:

The developed method provides a reliable approach for detecting phase synchronization in experimental time series.
This technique offers an alternative to synchrogram analysis, addressing potential issues with noisy data.
The findings have implications for understanding coupled physiological systems.