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How to Calculate and Validate Inter-brain Synchronization in a fNIRS Hyperscanning Study

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A fast statistical significance test for baseline correction and comparative analysis in phase locking.

Kunjan D Rana¹, Lucia M Vaina, Matti S Hämäläinen

¹Brain and Vision Research Laboratory, Department of Biomedical Engineering, Boston University Boston, MA, USA.

Frontiers in Neuroinformatics

|August 7, 2013

Summary

This study introduces a new method to accurately measure brain network connectivity using phase locking, overcoming issues caused by signal crosstalk in electroencephalography (EEG) and magnetoencephalography (MEG). The improved technique enhances the reliability of identifying functional brain networks.

Keywords:

MEG circular statistics cross-talk oscillation phase locking

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Area of Science:

Neuroscience
Cognitive Science
Biophysics

Background:

Human perception, cognition, and action rely on complex brain networks.
Inter-areal phase locking is a key measure for characterizing these networks over time and frequency.
Non-invasive methods like electroencephalography (EEG) and magnetoencephalography (MEG) are used, but suffer from spatial limitations.

Purpose of the Study:

To address the issue of false positives in phase locking measures due to crosstalk in EEG and MEG data.
To propose a novel method for improving the reliability of inter-areal phase locking analysis.
To enhance the accurate characterization of functional brain networks.

Main Methods:

Developed a novel method to improve phase locking reliability.
Incorporated baseline phase angle sampling (prestimulus or resting-state).
Contrasted phase angle distributions from baseline and time-of-interest periods.

Main Results:

The proposed method mitigates false positives caused by crosstalk in phase locking measures.
Reliability of detecting true inter-areal phase locking is enhanced.
Improved characterization of brain network dynamics is achieved.

Conclusions:

The novel phase locking method offers a more reliable approach to studying brain connectivity.
This technique is crucial for accurate analysis of neural oscillations and network function.
It advances our understanding of how brain regions interact during cognitive processes.