Interbrain synchrony analysis for fNIRS: A signal processing primer
- 1Department of Cognition, Emotion, and Methods in Psychology, Faculty of Psychology, University of Vienna, Vienna, Austria
- 2Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne, Geneva, Switzerland
- 3Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
Abstract
Interbrain synchrony (IBS), also referred to as interpersonal neural synchrony (INS), has become a central construct in social neuroscience and beyond. It describes the temporal alignment of neural activity across individuals and has been studied in a range of paradigms, including sequential interactions, shared-stimulus settings, and hyperscanning, that is, the simultaneous neuroimaging of two or more individuals during real-time interaction. Functional near-infrared spectroscopy (fNIRS) is increasingly used for such studies because it supports portable, naturalistic, face-to-face paradigms. A central goal in these experiments is to quantify IBS between interacting individuals. Wavelet transform coherence (WTC) has become the most widely used method for estimating IBS in fNIRS studies. However, its application involves several non-trivial signal-processing assumptions and time–frequency trade-offs that are often not explicitly addressed, making it difficult to apply and interpret the method consistently. Reliable estimation of IBS is crucial not only for experimental design but also for the interpretation and comparison of results within and across studies. Here, we aim to support this endeavor by providing a principled overview of the relevant signal-processing foundations. To enable researchers to make informed methodological choices, the paper first introduces key concepts in time–frequency analysis and clarifies their relation to WTC. These principles are then connected to the analysis of IBS in fNIRS datasets, with particular emphasis on the time–frequency constraints inherent to wavelet-based methods. Building on these foundations, we provide practical recommendations for experiment design, preprocessing, and frequency targeting. By making resolution limits and analysis assumptions explicit, this primer aims to support improved study planning and more consistent interpretation of IBS in the rapidly growing field of fNIRS-based investigations and beyond. Establishing a shared methodological foundation for IBS analysis is essential for advancing toward more rigorous, transparent, and reproducible measurement of interpersonal brain dynamics in naturalistic neuroscience.
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BibTeX
@article{lodrant2026interbrain,
title = {{Interbrain synchrony analysis for fNIRS: A signal processing primer}},
author = {Katarina Lodrant and Dimitri Van De Ville and Frank Scharnowski and Kathrin Kostorz},
journal = {Imaging Neuroscience},
year = {2026},
month = oct,
doi = {10.1162/imag.a.1398},
url = {https://doi.org/10.1162/imag.a.1398},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Interbrain synchrony analysis for fNIRS: A signal processing primer
AU - Katarina Lodrant
AU - Dimitri Van De Ville
AU - Frank Scharnowski
AU - Kathrin Kostorz
PY - 2026
DA - 2026/10/29
JO - Imaging Neuroscience
DO - 10.1162/imag.a.1398
UR - https://doi.org/10.1162/imag.a.1398
AB - Interbrain synchrony (IBS), also referred to as interpersonal neural synchrony (INS), has become a central construct in social neuroscience and beyond. It describes the temporal alignment of neural activity across individuals and has been studied in a range of paradigms, including sequential interactions, shared-stimulus settings, and hyperscanning, that is, the simultaneous neuroimaging of two or more individuals during real-time interaction. Functional near-infrared spectroscopy (fNIRS) is increasingly used for such studies because it supports portable, naturalistic, face-to-face paradigms. A central goal in these experiments is to quantify IBS between interacting individuals. Wavelet transform coherence (WTC) has become the most widely used method for estimating IBS in fNIRS studies. However, its application involves several non-trivial signal-processing assumptions and time–frequency trade-offs that are often not explicitly addressed, making it difficult to apply and interpret the method consistently. Reliable estimation of IBS is crucial not only for experimental design but also for the interpretation and comparison of results within and across studies. Here, we aim to support this endeavor by providing a principled overview of the relevant signal-processing foundations. To enable researchers to make informed methodological choices, the paper first introduces key concepts in time–frequency analysis and clarifies their relation to WTC. These principles are then connected to the analysis of IBS in fNIRS datasets, with particular emphasis on the time–frequency constraints inherent to wavelet-based methods. Building on these foundations, we provide practical recommendations for experiment design, preprocessing, and frequency targeting. By making resolution limits and analysis assumptions explicit, this primer aims to support improved study planning and more consistent interpretation of IBS in the rapidly growing field of fNIRS-based investigations and beyond. Establishing a shared methodological foundation for IBS analysis is essential for advancing toward more rigorous, transparent, and reproducible measurement of interpersonal brain dynamics in naturalistic neuroscience.
SN - 2837-6056
ER -