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Related Experiment Video

Updated: Jun 14, 2026

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

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Published on: October 6, 2023

A phase synchrony measure for quantifying dynamic functional integration in the brain.

Selin Aviyente¹, Edward M Bernat, Westley S Evans

¹Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824, USA. aviyente@egr.msu.edu

Human Brain Mapping

|March 26, 2010

Summary

This summary is machine-generated.

This study introduces a novel method for measuring brain signal synchrony, enhancing our understanding of neural communication. The new technique offers improved time and frequency resolution for analyzing brain network coordination.

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Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Area of Science:

Neuroscience
Computational Neuroscience
Signal Processing

Background:

Neural activity coordination across brain networks is fundamental to cognitive functions.
Analyzing phase relationships between neural signals quantifies temporal interactions in brain networks.
Existing methods for neural synchrony quantification have limitations in time-frequency resolution.

Purpose of the Study:

To introduce a new time-varying phase synchrony measure.
To improve the characterization of neural synchrony with high time and frequency resolution.
To validate the proposed measure using synthesized and electroencephalography (EEG) data.

Main Methods:

Development of a novel phase synchrony measure based on Cohen's class of time-frequency distributions.
Application of the measure to synthesized signals for controlled testing.
Validation using electroencephalography (EEG) data to assess real-world effectiveness.

Main Results:

The proposed measure provides uniformly high resolution across both time and frequency.
The new index effectively characterizes signal similarity between separable brain regions.
Demonstrated effectiveness in estimating phase changes and quantifying neural synchrony in EEG data.

Conclusions:

The novel time-varying phase synchrony measure offers enhanced capabilities for analyzing neural communication.
This method improves upon existing techniques by providing superior time-frequency resolution.
The findings contribute to a better understanding of brain network dynamics underlying cognition and behavior.