OSCR

SNIRF2BIDS: a GUI-based tool for converting functional near-infrared spectroscopy data to the Brain Imaging Data Structure in R.

Code ↔ Paper

6 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 6 matches
  1. [1] § Handling Files that Could Not Be Converted ↔ inst/shiny/myapp/app.R, lines 172–283 · score 0.73 · log.txt, Conversion failed, Conversion complete, mne bids, notification, messages
  2. [2] § Installation and Setup ↔ R/convert.R, lines 16–75 · score 0.62 · Python environment, Miniconda, MNE BIDS, command, reticulate, installation
  3. [3] § Main GUI and General Workflow ↔ R/convert.R, lines 95–198 · score 0.54 · subject ID, description.json, task mapping, MNE, BIDS formatted, root
  4. [4] § Define the Hierarchical Folder Structure and File-Naming Scheme › Step 3: Define Experimental Setup ↔ R/experimentalDesign.R, lines 46–111 · score 0.54 · took place, fNIRS, afternoon, day, declares, morning
  5. [5] § Presentation of (NIRS-)BIDS and Existing Software Tools ↔ R/convert.R, lines 16–75 · score 0.52 · raw bids, MNE BIDS, channels, Python, export
  6. [6] § Handling Files that Could Not Be Converted ↔ R/convert.R, lines 95–198 · score 0.51 · log.txt, raw bids, notification, Error, mne, SNIRF

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

R · 288 lines · 9.3 KB · no license · 4 matches

  1. #### SETUP ####
  2. library(reticulate)
  3. library(rhdf5)
  4. library(jsonlite)
  5. library(here)
  7. vendor_hooks_path <- here("R", "functions", "vendor_hooks.R")
  9. # Always normalize paths for Windows compatibility
  10. vendor_hooks_path <- normalizePath(vendor_hooks_path, winslash = "/", mustWork = FALSE)
  12. # old version (use local venv)
  13. # reticulate::use_virtualenv(venv_path, required = TRUE)
  14. # connect previously created python environment + packages
  16. #' Activate MNE Python environment
  17. #'
  18. #' Sets up or activates the required conda environment for MNE.
  19. #' @export
  20. activate_mne_env <- function () {
  21. # Step 0: Ensure unattended install
  22. Sys.setenv(CONDA_ALWAYS_YES = "true")
  24. # Step 1: Ensure Miniconda exists
  25. if (reticulate::miniconda_path() == "" || !file.exists(reticulate::miniconda_path())) {
  26. message("Installing Miniconda...")
  27. reticulate::install_miniconda()
  29. # Alternative code to accept ToS automatically
  30. # (Can be run in command line, currently not running if executed within activate_mne_env)
  31. # conda_bin <- file.path(reticulate::miniconda_path(), "condabin", "conda.bat")
  32. # for (ch in c("https://repo.anaconda.com/pkgs/main",
  33. #"https://repo.anaconda.com/pkgs/r",
  34. #"https://repo.anaconda.com/pkgs/msys2")) {
  35. #system2(conda_bin, c("tos", "accept", "--override-channels", "--channel", ch),
  36. #stdout = TRUE, stderr = TRUE, wait = TRUE)
  37. #}
  39. }
  41. # Step 2: Now it's safe to query conda
  42. conda_envs <- reticulate::conda_list()[["name"]]
  44. # Step 3: Create env if missing
  45. if (!"mne-env" %in% conda_envs) {
  46. message("Creating mne-env...")
  48. reticulate::conda_create("mne-env", packages = "python=3.10")
  50. reticulate::conda_install(
  51. "mne-env",
  52. packages = c("mne", "numpy", "scipy", "mne-bids", "matplotlib", "pandas"),
  53. channel = "conda-forge"
  54. )
  55. }
  57. # Step 4: Activate env
  58. reticulate::use_condaenv("mne-env", required = TRUE)
  60. # Step 5: Import Python modules
  61. mne <- reticulate::import("mne")
  62. mnebids <- reticulate::import("mne_bids")
  63. h5py <- reticulate::import("h5py")
  64. pathlib <- reticulate::import("pathlib")
  66. # Return objects (important!)
  67. list(
  68. mne = mne,
  69. mnebids = mnebids,
  70. h5py = h5py,
  71. pathlib = pathlib,
  72. BIDSPath = mnebids$BIDSPath,
  73. write_raw_bids = mnebids$write_raw_bids
  74. )
  75. }
  77. #### ACCESS THE PATH TO SAVE THE CONVERTED VALUES ####
  78. make_output_folder <- function(file_path_reactive) {
  79. reactive({
  80. file_path <- file_path_reactive()
  81. if (is.null(file_path) || file_path == "") return(NULL)
  82. basename(dirname(file_path))
  83. })
  84. }
  86. #### CONVERT ROUTINE (ONE FILE) ####
  87. # Function that reads a source SNIRF file, checks for manufacturer name
  89. # If routine = "json", it checks for a description.json file in the same folder containing the SNIRF
  90. # Reads subject ID and experiment description from there
  91. # And then deducts the task name and session number based on task mapping
  93. # If routine = "folder", it reads the subject ID, session number and task name from the folder structure
  95. #' snirf2bids function for conversion
  96. #'
  97. #' @param source_snirf SNIRF to be converted
  98. #' @param converted_root Output folder for converted SNIRFs
  99. #' @param experiment_description Subject, session and task metadata - only for "json" routine
  100. #' @param routine Switch between "json" and "folder"
  101. #' @param py_env List. Python environment returned by activate_mne_env()
  102. #' @export
  103. snirf2bids <- function (source_snirf, converted_root, experiment_description = NULL, routine = c("json", "folders"), py_env) {
  104. routine <- match.arg(routine)
  105. log_file <- file.path(converted_root, "log.txt")
  106. if (routine == "json") {
  107. task_map <- read.csv(experiment_description, colClasses = c("session" = "character"))
  108. json_path <- check_description_json(source_snirf) # Use NIRx vendor hook
  109. # Read the JSON content
  110. json_content <- fromJSON(json_path)
  112. # Convert the JSON content to a data frame
  113. file_tags <- as.data.frame(t(unlist(json_content)), stringsAsFactors = FALSE)
  115. # Add the subfolder name as a column
  116. file_tags$subfolder <- basename(dirname(json_path))
  118. # IF the information inside description.json matches experiment description, create regular BIDS path with corresponding info
  119. if (any(task_map$name == file_tags$experiment)) {
  120. # Read task and session from the experiment overview
  121. file_tags$task <- task_map$task[task_map$name == file_tags$experiment]
  122. file_tags$session <- task_map$session[task_map$name == file_tags$experiment]
  124. bids_path <- py_env$BIDSPath(
  125. subject = file_tags$subject,
  126. session = file_tags$session,
  127. task = file_tags$task,
  128. root = converted_root
  129. )
  130. }
  132. # ELSE create BIDS path inside "no_mapping" folder with session "999"
  133. else {
  134. file_tags$task <- gsub("[-_/]", "", file_tags$experiment) # Remove BIDS non-conforming characters from the string
  135. file_tags$session <- "999"
  136. no_mapping_path <- file.path(converted_root, "no_mapping")
  137. dir.create(no_mapping_path, recursive = TRUE, showWarnings = FALSE)
  139. bids_path <- py_env$BIDSPath(
  140. subject = file_tags$subject,
  141. session = file_tags$session,
  142. task = file_tags$task,
  143. root = no_mapping_path
  144. )
  146. }
  147. # Load data with MNE and convert to BIDS format
  148. raw <- py_env$mne$io$read_raw_snirf(source_snirf, preload = FALSE)
  150. tryCatch({
  151. py_env$write_raw_bids(raw, bids_path) # this will cause an error, i.e., FileExistsError because the "overwrite=T" parameter is missing
  152. }, error = function(e) {
  153. py_last_error() # restore previous exception
  154. error <- py_last_error()
  155. writeLines(error$message, log_file)
  156. showNotification(error$message, type = "error")
  157. })
  158. }
  160. # In "folders" routine, extract subject ID, session number and task name from folder structure
  161. else if (routine == "folders"){
  163. # Split path into components
  164. path_parts <- strsplit(normalizePath(source_snirf), "[/\\\\]")[[1]]
  166. if (length(path_parts) < 4) {
  167. stop("Path is too short to extract subject/session/task structure")
  168. }
  170. # Extract last elements relative to file
  171. task <- path_parts[length(path_parts) - 1]
  172. session <- path_parts[length(path_parts) - 2]
  173. subject <- path_parts[length(path_parts) - 3]
  175. file_tags <- data.frame(
  176. subject = subject,
  177. session = session,
  178. task = task
  179. )
  181. bids_path <- py_env$BIDSPath(
  182. subject = subject,
  183. session = session,
  184. task = task,
  185. root = converted_root
  186. )
  188. raw <- py_env$mne$io$read_raw_snirf(source_snirf, preload = FALSE)
  189. tryCatch({
  190. py_env$write_raw_bids(raw, bids_path) # this will cause an error, i.e., FileExistsError because the "overwrite=T" parameter is missing
  191. }, error = function(e) {
  192. py_last_error() # restore previous exception
  193. error <- py_last_error()
  194. writeLines(error$message, log_file)
  195. showNotification(error$message, type = "error")
  196. })
  197. }
  198. }
  200. #### NO MAPPING OVERVIEW ####
  201. create_no_mapping_overview <- function(converted_root) {
  203. no_mapping_dir <- file.path(converted_root, "no_mapping")
  205. # Do nothing if the directory does not exist
  206. if (!dir.exists(no_mapping_dir)) {
  207. return(invisible(NULL))
  208. }
  210. # List all converted SNIRF files
  211. files <- list.files(
  212. path = no_mapping_dir,
  213. pattern = "_nirs\\.snirf$",
  214. recursive = TRUE,
  215. full.names = TRUE
  216. )
  218. # If there is no such file, do nothing
  219. if (length(files) == 0) {
  220. return(invisible(NULL))
  221. }
  223. # Extract ID, task and filename
  224. unmapped_df <- data.frame(
  225. ID = stringr::str_extract(basename(files), "^sub-[^_]+"),
  226. task = stringr::str_match(
  227. basename(files),
  228. "_task-([^_]+)"
  229. )[, 2],
  230. filename = basename(files),
  231. stringsAsFactors = FALSE
  232. )
  234. # Only list each file once
  235. unmapped_df <- unique(unmapped_df)
  237. # Write to csv
  238. write.csv(
  239. unmapped_df,
  240. file.path(no_mapping_dir, "unmapped_recordings.csv"),
  241. row.names = FALSE
  242. )
  244. invisible(unmapped_df)
  245. }
  247. #### CONVERT ROUTINE (ONE FOLDER) ####
  248. # Helper function that finds all .snirf files in a specific folder
  249. get_snirf_files <- function(folder) {
  250. list.files(folder, pattern = "\\.snirf$", ignore.case = TRUE, full.names = TRUE)
  251. }
  253. # Runs SNIRF2BIDS with lapply on data directory
  254. #' snirf2bids function for conversion
  255. #'
  256. #' @param source_root Input folder for raw SNIRFs
  257. #' @param converted_root Output folder for converted SNIRFs
  258. #' @param experiment_description Subject, session and task metadata - only for "json" routine
  259. #' @param routine Switch between "json" and "folder"
  260. #' @param py_env List. Python environment returned by activate_mne_env()
  261. #' @export
  262. convert_root <- function(source_root, converted_root, experiment_description = NULL,
  263. routine = c("json", "folders"), py_env) {
  265. routine <- match.arg(routine)
  267. # Find all SNIRFs in the folder and subfolders
  268. all_snirfs <- list.files(source_root, pattern = "\\.snirf$", recursive = TRUE, full.names = TRUE)
  270. # Process them one by one
  271. for (snirf_path in all_snirfs) {
  272. cat("Processing:", snirf_path, "\n")
  273. snirf2bids(
  274. source_snirf = snirf_path,
  275. converted_root = converted_root,
  276. experiment_description = experiment_description,
  277. routine = routine,
  278. py_env = py_env
  279. )
  280. }
  281. create_no_mapping_overview(converted_root)
  282. }

convert.R at commit fddbd6d, no license · at the source

Overview

Authors: Raphaël Lorenz-de Laigue1,2, Jennifer Svaldi1,2, Philipp A. Schroeder1,2
ORCID iDs: Raphaël Lorenz-de Laigue, Philipp A. Schroeder
  1. University of Tübingen, Department of Psychology, Clinical Psychology & Psychotherapy, Tübingen, Germany
  2. German Center for Mental Health (DZPG), Tübingen, Germany
Institutions: University of Tübingen (Germany)
Journal: Neurophotonics, volume 13, issue Suppl 3, article S32603
Dates: received 1 April 2026; accepted 13 August 2026; published online 17 September 2026; in print August 2026
Type: Methods article · Language: English
License: CC BY
Identifiers: DOI 10.1117/1.nph.13.s3.s32603 · PMID 42757085 · PMCID PMC13585469 · OpenAlex W7213517912
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fNIRS (modality), methods / tools (subfield)
Keywords: BIDS, data sharing, R, conversion, open-source software
Journal subjects: fNIRS Commons: Datasets and Tools for Open, Reproducible Science
Topic: Optical Imaging and Spectroscopy Techniques (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Deutsches Zentrum für Psychische Gesundheit (TÜ2D)
Citations: not cited yet (Europe PMC); 38 references in the paper

Abstract

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 (≥4.0.0), including recent versions of shiny (≥1.13) and rlang (≥1.1.7). The backend for conversion configures Python and MNE-BIDS to be available for R via reticulate (≥1.45). 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.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

raphael-lorenzdelaigue/snirf2bids

License: none: the authors keep all their rights
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: fddbd6db85a32695bd0ef524d65f893f7891bef6, 7 August 2026
Languages: R (8)
Size: 37 files, 8 scripts
Software Heritage: not archived
Found in: “Code and Data Availability”
Holds: environment (DESCRIPTION), documentation
Not found: README, license file, CITATION.cff, tests, continuous integration
Tools: tidyverse (4 files), reticulate (2 files)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
8 files
At the source: github.com/raphael-lorenzdelaigue/snirf2bids

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 8 scripts, each with its path and the digest of its content;
  • 6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Code and Data Availability

All data and code reported in this paper are openly available on Github: https://github.com/raphael-lorenzdelaigue/snirf2bids.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 1 funder, 25 references.

Cite

This paper

Lorenz-de Laigue, R., Svaldi, J., & Schroeder, P. A. (2026). SNIRF2BIDS: a GUI-based tool for converting functional near-infrared spectroscopy data to the Brain Imaging Data Structure in R. Neurophotonics, 13(Suppl 3), S32603. https://doi.org/10.1117/1.nph.13.s3.s32603

BibTeX

@article{lorenzdelaigue2026snirf2bids,
author = {Lorenz-de Laigue, Raphaël and Svaldi, Jennifer and Schroeder, Philipp A.},
title = {{SNIRF2BIDS: a GUI-based tool for converting functional near-infrared spectroscopy data to the Brain Imaging Data Structure in R}},
journal = {Neurophotonics},
year = {2026},
month = aug,
volume = {13},
number = {Suppl 3},
pages = {S32603},
publisher = {Society of Photo-Optical Instrumentation Engineers},
issn = {2329-423X},
doi = {10.1117/1.nph.13.s3.s32603},
url = {https://doi.org/10.1117/1.nph.13.s3.s32603},
pmid = {42757085},
pmcid = {PMC13585469}
}

RIS

TY - JOUR
AU - Lorenz-de Laigue, Raphaël
AU - Svaldi, Jennifer
AU - Schroeder, Philipp A.
TI - SNIRF2BIDS: a GUI-based tool for converting functional near-infrared spectroscopy data to the Brain Imaging Data Structure in R
T2 - Neurophotonics
J2 - Neurophotonics
PY - 2026
DA - 2026/08/01
VL - 13
IS - Suppl 3
SP - S32603
SN - 2329-423X
PB - Society of Photo-Optical Instrumentation Engineers
DO - 10.1117/1.nph.13.s3.s32603
UR - https://doi.org/10.1117/1.nph.13.s3.s32603
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "SNIRF2BIDS: a GUI-based tool for converting functional near-infrared spectroscopy data to the Brain Imaging Data Structure in R",
"container-title": "Neurophotonics",
"author": [
{
"family": "Lorenz-de Laigue",
"given": "Raphaël"
},
{
"family": "Svaldi",
"given": "Jennifer"
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"volume": "13",
"issue": "Suppl 3",
"page": "S32603",
"DOI": "10.1117/1.nph.13.s3.s32603",
"PMID": "42757085",
"PMCID": "PMC13585469",
"ISSN": "2329-423X",
"publisher": "Society of Photo-Optical Instrumentation Engineers",
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"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
1
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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