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Related Experiment Video

Updated: May 21, 2026

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

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

Published on: October 6, 2023

Testing for time-localized coherence in bivariate data.

L W Sheppard¹, A Stefanovska, P V E McClintock

¹Department of Physics, Lancaster University, Lancaster, United Kingdom.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|June 12, 2012

Summary

We developed a new significance testing method for time series coherence, even with varying amplitude and frequency. This approach uses self-correlations to identify causal relationships, overcoming spectral biases in data analysis.

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Conducting Hyperscanning Experiments with Functional Near-Infrared Spectroscopy

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Published on: January 19, 2019

Area of Science:

Signal processing
Time series analysis
Biomedical engineering

Background:

Evaluating coherence between oscillatory time series is crucial in many scientific fields.
Existing methods can be biased by spectral characteristics and variability in amplitude and frequency.
A robust significance testing method is needed for accurate causal inference.

Purpose of the Study:

To introduce a novel statistical method for assessing the significance of coherence between two oscillatory time series.
To address limitations of current methods, particularly those affected by variable amplitude, frequency, and spectral biases.
To provide a reliable approach for detecting causal relationships in time series data.

Main Methods:

The method relies on evaluating the self-correlations of the time series.
Wavelet-based coherence measures are applied to both artificial and physiological datasets.
Significance is determined by estimating the distribution of coherence values arising from chance associations, considering data's autocorrelation and higher-order statistics.

Main Results:

The proposed method effectively tests significance for coherence in time series with variable amplitude and frequency.
The expectation value and standard deviation of chance coherence distributions are shown to depend on time series statistics.
Coherence values falling outside the chance distribution indicate a significant relationship, irrespective of spectral similarities.

Conclusions:

The developed method offers a robust way to evaluate time series coherence significance.
It successfully identifies causal relationships by distinguishing true coherence from chance associations, even with complex signal properties.
This approach enhances the reliability of coherence analysis in diverse scientific applications.