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Updated: Jul 14, 2026

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

Published on: July 21, 2021

A cartesian time--frequency approach to reveal brain interaction dynamics.

L Marzetti¹, S Della Penna, G Nolte

¹Department of Clinical Sciences and Bioimaging, Gabriele D'Annunzio University, Chieti, Italy. lmarzetti@unich.it

Brain Topography

|June 26, 2007

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This study introduces a novel method using the imaginary part of complex coherence to accurately track dynamic neural signal interactions from magnetoencephalography (MEG) data. The approach overcomes limitations of traditional methods, revealing temporal variations in brain source synchronization during audio-visual stimulation.

Area of Science:

Neuroscience
Signal Processing
Biophysics

Background:

Magnetoencephalography (MEG) is crucial for studying brain activity.
Complex coherence magnitude is a common metric for neural coupling, but assumes signal stationarity.
Traditional methods can be biased by volume conduction and non-interacting sources.

Purpose of the Study:

To develop a novel method for analyzing neural signal interactions in MEG data.
To overcome limitations of traditional complex coherence analysis, particularly regarding signal non-stationarity and spurious connectivity.
To investigate dynamic source-level interactions in the brain.

Main Methods:

The study decomposes complex coherence into real and imaginary parts.
Focus is placed on the imaginary part, which is insensitive to volume-conducted self-interaction.

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Published on: March 21, 2019

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Published on: July 21, 2021

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

Time-frequency analysis, using the short-time Fourier transform, is applied to the imaginary part of complex coherence.

Main Results:

The proposed method accurately captures dynamic changes in neural coupling.
It effectively distinguishes true interactions from artifactual connectivity.
Application to audio-visual stimulation revealed dynamic synchronization patterns in temporal and occipital areas.

Conclusions:

The imaginary part of complex coherence in the time-frequency domain offers a robust measure of neural interactions.
This method provides unbiased insights into dynamic source-level brain connectivity.
Findings support the involvement of specific brain regions in integrated audio-visual information processing.