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Updated: Sep 19, 2026
New Framework for Understanding Cross-Brain Coherence in Functional Near-Infrared Spectroscopy (fNIRS) Hyperscanning Studies
Published on: October 6, 2023
SNIRF2BIDS: a GUI-based tool for converting functional near-infrared spectroscopy data to the Brain Imaging Data
Raphaël Lorenz-de Laigue1,2, Jennifer Svaldi1,2, Philipp A Schroeder1,2
1University of Tübingen, Department of Psychology, Clinical Psychology & Psychotherapy, Tübingen, Germany.
Significance:
Optical imaging with functional near-infrared spectroscopy (fNIRS) often produces complex datasets. The Brain Imaging Data Structure specification (BIDS) has evolved to harmonize fNIRS recordings and to facilitate data sharing, transparency, and reproducibility. However, the conversion of raw fNIRS recordings and associated metadata into BIDS format remains time-consuming and error-prone.
Aim:
We aim to present SNIRF2BIDS, an R package that streamlines the conversion of fNIRS data to the BIDS format through a graphical user interface.
Approach:
SNIRF2BIDS automates dataset restructuring, metadata organization, and file naming in accordance with the current BIDS specification. The SNIRF2BIDS-GUI requires R ( ), including recent versions of shiny ( ) and rlang ( ). The backend for conversion configures Python and MNE-BIDS to be available for R via reticulate ( ). After initial setup, the tool operates locally without requiring data transfer to online platforms.
Results:
We demonstrate the functionality of SNIRF2BIDS in a step-by-step tutorial, guiding users from raw fNIRS recordings to fully BIDS-compliant datasets.
Conclusions:
SNIRF2BIDS enables standardized organization of fNIRS datasets, thereby reducing manual work and minimizing the risk of human error. It fosters data sharing, thus supporting reproducible workflows and meta-scientific progress in the growing fNIRS research community.
