OSCR

NIRSTORM: a Brainstorm extension dedicated to functional near-infrared spectroscopy data analysis, advanced 3D reconstructions, and optimal probe design

Code ↔ Paper

22 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 22 matches · 7 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Standard Channel Space Analysis of fNIRS Signals › Preprocessing › Motion correction ↔ bst_plugin/preprocessing/process_nst_motion_correction.m, lines 28–81 · score 0.82 · motion correction algorithm, temporal derivative distribution, spline interpolation, TDDR, repair, artifacts
  2. [2] § Standard Channel Space Analysis of fNIRS Signals › Preprocessing › Physiological noise regression using short separation channel regression ↔ bst_plugin/GLM/process_nst_glm_fit.m, lines 39–172 · score 0.80 · source detector distance, superficial channels, Short separation channels, slow fluctuations, fitted, bands
  3. [3] § Standard Channel Space Analysis of fNIRS Signals › Preprocessing › Bad channel detection ↔ bst_plugin/preprocessing/process_nst_quality_check.m, lines 270–352 · score 0.79 · scalp coupling, bandpass filtering, standard deviation, quality, 2.5 Hz, SCI
  4. [4] § fNIRS 3D Reconstruction Using Near-Infrared Optical Tomography › Solving NIROT Inverse Problem › Minimum norm estimate ↔ bst_plugin/math/nst_mne_lcurve.m, the whole file · a weak match · score 0.75 · depth weighted factor, noise covariance, covariance matrix, identity, curve, MNE
  5. [5] § Standard Channel Space Analysis of fNIRS Signals › Preprocessing › Motion correction ↔ bst_plugin/math/nst_tddr_correction.m, the whole file · a weak match · score 0.75 · temporal derivative distribution, motion correction, TDDR, optical density, repair, fNIRS
  6. [6] § fNIRS 3D Reconstruction Using Near-Infrared Optical Tomography › Model of NIR Light Propagation Within the Head Tissues ↔ bst_plugin/forward/process_nst_cpt_fluences.m, lines 258–362 · score 0.74 · Monte Carlo simulations, optical properties, MCXlab, skin, tissues, fluences
  7. [7] § fNIRS 3D Reconstruction Using Near-Infrared Optical Tomography › Solving NIROT Inverse Problem › Minimum norm estimate ↔ bst_plugin/math/nst_mne_lcurve_MAP.m, the whole file · a weak match · score 0.74 · depth weighted factor, noise covariance, covariance matrix, identity, curve, MNE
  8. [8] § Appendix A: Detection of Brain Activation Using the General Linear Model ↔ bst_plugin/GLM/process_nst_glm_fit.m, lines 39–172 · score 0.71 · nuisance regressors, HRF model, design matrix, superficial, event, Linear
  9. [9] § Standard Channel Space Analysis of fNIRS Signals › Preprocessing › Physiological noise regression using short separation channel regression ↔ bst_plugin/preprocessing/process_nst_remove_ssc.m, lines 28–66 · score 0.71 · source detector distance, superficial channels, Short separation channels, SSCs, noise, filtering
  10. [10] § Standard Channel Space Analysis of fNIRS Signals › Preprocessing › Frequency filtering ↔ bst_plugin/math/nst_math_build_basis_dct.m, the whole file · a weak match · score 0.70 · discrete cosines transform, frequency bands, physiological, regressing, signal
  11. [11] § fNIRS 3D Reconstruction Using Near-Infrared Optical Tomography › Solving NIROT Inverse Problem › Computing HbO and HbR fluctuations along the cortical surface ↔ bst_plugin/OM/process_nst_compare_montage.m, lines 328–358 · score 0.67 · spatial dispersion, ground truth, ROC, AUC, metrics, SD
  12. [12] § fNIRS 3D Reconstruction Using Near-Infrared Optical Tomography › Model of NIR Light Propagation Within the Head Tissues ↔ bst_plugin/forward/panel_nst_fluences.m, lines 190–243 · score 0.64 · Monte Carlo simulations, forward model, GPU, photon, MCXLab, tissues
  13. [13] § fNIRS 3D Reconstruction Using Near-Infrared Optical Tomography › Model of NIR Light Propagation Within the Head Tissues ↔ bst_plugin/forward/process_nst_import_head_model.m, lines 174–319 · score 0.63 · surface interpolation, sensitivity map, kernel, cortical surface, voxel, Voronoi
  14. [14] § Multimodal Integration–Illustration in the Clinical Context of Epilepsy › Review of the Simultaneous EEG-fNIRS Data ↔ bst_plugin/preprocessing/process_nst_remove_ssc.m, lines 28–66 · score 0.63 · superficial noise, short separation channels, pre processed, filtering
  15. [15] § Standard Channel Space Analysis of fNIRS Signals › Preprocessing › Motion correction ↔ bst_plugin/math/nst_tddr_correction.m, the whole file · a weak match · score 0.62 · temporal derivative, signal corrected, TDDR, Optical density, repair, fNIRS
  16. [16] § Standard Channel Space Analysis of fNIRS Signals › Preprocessing › Frequency filtering ↔ bst_plugin/preprocessing/process_nst_iir_filter.m, lines 182–227 · score 0.61 · infinite impulse, Butterworth, IIR, detrending, Filtering, signal
  17. [17] § Standard Channel Space Analysis of fNIRS Signals › Preprocessing › Motion correction ↔ bst_plugin/preprocessing/process_nst_motion_correction.m, lines 28–81 · score 0.57 · temporal derivative, spline interpolation, TDDR, repair, motion, fNIRS
  18. [18] § Appendix A: Detection of Brain Activation Using the General Linear Model ↔ bst_plugin/math/nst_math_build_basis_dct.m, the whole file · a weak match · score 0.56 · discrete cosine basis, physiological, regressors, matrix, signals
  19. [19] § Overview of NIRSTORM ↔ scripts/tutorial_nirstorn_2024.m, lines 1–80 · score 0.54 · corresponding online, tutorial, article, protocol, tapping, NIRSTORM
  20. [20] § fNIRS 3D Reconstruction Using Near-Infrared Optical Tomography › Solving NIROT Inverse Problem › Coherent maximum entropy on the mean ↔ scripts/tutorial_nirstorn_2024.m, lines 247–326 · score 0.53 · BEst, cMEM, localize, field, overlapping, Brainstorm
  21. [21] § Standard Channel Space Analysis of fNIRS Signals › Standard Hemodynamic Response Estimation Using fNIRS Signal Averaging ↔ scripts/nst_tutorial_tapping.m, the whole file · a weak match · score 0.52 · finger tapping, block, epochs, baseline, brain, event
  22. [22] § Standard Channel Space Analysis of fNIRS Signals › Preprocessing › Estimation of HbO and HbR fluctuations using the modified Beer-Lambert law ↔ bst_plugin/mbll/process_nst_mbll.m, lines 465–546 · score 0.51 · partial volume factor, age, PVF, wavelength

Paper

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

The paper is loaded when this pane is shown.

The authors' code

MATLAB · 200 lines · 7.3 KB · GPL-3.0 · 2 matches

  1. function varargout = process_nst_motion_correction( varargin )
  3. % @=============================================================================
  4. % This software is part of the Brainstorm software:
  5. % http://neuroimage.usc.edu/brainstorm
  6. %
  7. % Copyright (c)2000-2013 Brainstorm by the University of Southern California
  8. % This software is distributed under the terms of the GNU General Public License
  9. % as published by the Free Software Foundation. Further details on the GPL
  10. % license can be found at http://www.gnu.org/copyleft/gpl.html.
  11. %
  12. % FOR RESEARCH PURPOSES ONLY. THE SOFTWARE IS PROVIDED "AS IS," AND THE
  13. % UNIVERSITY OF SOUTHERN CALIFORNIA AND ITS COLLABORATORS DO NOT MAKE ANY
  14. % WARRANTY, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF
  15. % MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, NOR DO THEY ASSUME ANY
  16. % LIABILITY OR RESPONSIBILITY FOR THE USE OF THIS SOFTWARE.
  17. %
  18. % For more information type "brainstorm license" at command prompt.
  19. % =============================================================================@
  20. %
  21. % Authors: Thomas Vincent (2015-2018), Edouard Delaire (2023)
  23. eval(macro_method);
  24. end
  27. %% ===== GET DESCRIPTION =====
  28. function sProcess = GetDescription()
  29. % Description the process
  30. sProcess.Comment = 'Motion correction';
  31. sProcess.FileTag = '_motioncorr';
  32. sProcess.Category = 'Filter';
  33. sProcess.SubGroup = {'NIRS', 'Pre-process'};
  34. sProcess.Index = 1305;
  35. sProcess.Description = 'https://neuroimage.usc.edu/brainstorm/Tutorials/NIRSTORM#Motion_correction';
  36. sProcess.isSeparator = 0;
  37. % Definition of the input accepted by this process
  38. sProcess.InputTypes = {'data', 'raw'};
  39. sProcess.OutputTypes = {'data', 'raw'};
  40. sProcess.nInputs = 1;
  41. sProcess.nMinFiles = 1;
  42. % Definition of the options
  45. sProcess.options.method.Type = 'radio_linelabel';
  46. sProcess.options.method.Comment = {'Spline correction', ' Temporal Derivative Distribution Repair','Motion correction algorithm'; 'spline', 'tddr',''};
  47. sProcess.options.method.Controller = struct('spline','spline','tddr','tddr');
  48. sProcess.options.method.Value = 'spline';
  51. sProcess.options.option_event_name.Comment = 'Movement event name: ';
  52. sProcess.options.option_event_name.Type = 'text';
  53. sProcess.options.option_event_name.Value = '';
  54. sProcess.options.option_event_name.Class = 'spline';
  56. sProcess.options.option_smoothing.Comment = 'Smoothing Parameters';
  57. sProcess.options.option_smoothing.Type = 'value';
  58. sProcess.options.option_smoothing.Value = {0.99,'',3};
  59. sProcess.options.option_smoothing.Class = 'spline';
  61. sProcess.options.citation.Comment = '<b>Source:</b>';
  62. sProcess.options.citation.Type = 'label';
  64. sProcess.options.citation_spline.Comment = ['<p>Scholkmann, F., Spichtig, S., Muehlemann, T., & Wolf, M. (2010). <br />' ...
  65. 'How to detect and reduce movement artifacts in near-infrared imaging <br />' ...
  66. 'using moving standard deviation and spline interpolation. <br />' ...
  67. 'Physiological measurement, 31(5) <br />' ...
  68. 'https://doi.org/10.1088/0967-3334/31/5/004<p>'];
  70. sProcess.options.citation_spline.Type = 'label';
  71. sProcess.options.citation_spline.Class = 'spline';
  73. sProcess.options.citation_tddr.Comment = ['<p>Fishburn F.A., Ludlum R.S., Vaidya C.J., & Medvedev A.V. (2019). <br />' ...
  74. 'Temporal Derivative Distribution Repair (TDDR): A motion correction <br />' ...
  75. 'method for fNIRS. NeuroImage, 184, 171-179. <br />' ...
  76. 'https://doi.org/10.1016/j.neuroimage.2018.09.025</p>'];
  77. sProcess.options.citation_tddr.Type = 'label';
  78. sProcess.options.citation_tddr.Class = 'tddr';
  79. end
  81. %% ===== FORMAT COMMENT =====
  82. function [Comment, fileTag] = FormatComment(sProcess)
  83. % Get options
  85. % Format comment
  86. if strcmp(sProcess.options.method.Value,'spline')
  87. Comment = 'Motion Corrected (spline)';
  88. fileTag = 'motion';
  89. else
  90. Comment = 'Motion Corrected (TDDR)';
  91. fileTag = 'motion';
  92. end
  93. end
  95. %% ===== RUN =====
  96. function sInputs = Run(sProcess, sInputs)
  98. if strcmp(sProcess.options.method.Value,'spline')
  99. if ~license('test', 'Curve_Fitting_Toolbox')
  100. bst_error('Curve Fitting Toolbox not available');
  101. return
  102. elseif isempty(which('csaps'))
  103. bst_error(['Curve Fitting Toolbox OK but function csaps not found.<BR>' ...
  104. 'Try refreshing matlab cache using command: rehash toolboxcache']);
  105. return
  106. end
  107. end
  109. % Get selected events
  110. event_name = strtrim(sProcess.options.option_event_name.Value);
  112. % Load Events
  113. if strcmp(sInputs.FileType, 'data') % Imported data structure
  114. sDataIn = in_bst_data(sInputs.FileName, 'Events');
  115. events = sDataIn.Events;
  116. elseif strcmp(sInputs.FileType, 'raw') % Continuous data file
  117. sDataRaw = in_bst_data(sInputs.FileName, 'F');
  118. events = sDataRaw.F.events;
  119. end
  121. event = [];
  122. if strcmp(sProcess.options.method.Value,'spline')
  123. ievt_mvt = [];
  124. for ievt=1:length(events)
  125. if strcmp(events(ievt).label, event_name)
  126. event = events(ievt);
  127. ievt_mvt = ievt;
  128. break;
  129. end
  130. end
  131. if isempty(event)
  132. warning(['Event "' event_name '" does not exist in file.']);
  133. end
  134. end
  138. % Process only NIRS channels
  139. channels = in_bst_channel(sInputs.ChannelFile);
  140. nirs_ichans = channel_find(channels.Channel, 'NIRS');
  141. data_nirs = sInputs.A(nirs_ichans, :)';
  143. prev_negs = any(data_nirs <= 0, 1);
  145. data_corr = Compute(data_nirs, sInputs.TimeVector', event,sProcess.options.method.Value,sProcess.options.option_smoothing.Value{1});
  147. new_negs = any(data_corr <= 0, 1) & ~prev_negs;
  148. negative_chan=find(new_negs);
  149. pair_indexes = nst_get_pair_indexes_from_names({channels.Channel(nirs_ichans).Name});
  151. if any(new_negs)
  152. bst_report('Warning', sProcess, sInputs, 'Motion correction introduced negative values. Will be fixed by local offset');
  154. for ineg=1:length(negative_chan)
  155. ipair=find(any(pair_indexes(:, :) == negative_chan(ineg),2));
  156. offset = 2*abs(min(min(data_corr(:, pair_indexes(ipair, :)))));
  157. data_corr(:, pair_indexes(ipair, :)) = data_corr(:, pair_indexes(ipair, :)) + offset;
  159. end
  160. [isrcs, idets, measures, channel_type] = nst_unformat_channels({channels.Channel(pair_indexes(ipair, 1)).Name});
  162. msg=sprintf('S%dD%d corrected with offset: %.2f',isrcs,idets,offset);
  163. bst_report('Warning', sProcess, sInputs, msg);
  165. end
  167. % Export
  168. sInputs.A(nirs_ichans,:) = data_corr';
  169. sInputs.CommentTag = FormatComment(sProcess);
  171. end
  174. %% ===== Compute =====
  175. function [data_corr] = Compute(nirs_sig, t, event, method,exta_parameters)
  176. if nargin < 4
  177. method = 'spline';
  178. end
  180. if nargin < 5
  181. exta_parameters=0.99;
  182. end
  184. data_corr = nirs_sig;
  185. if strcmp(method,'spline') && ~isempty(event) && ~isempty(event.times)
  186. samples = time_to_sample_idx(event.times, t);
  187. data_corr = nst_spline_correction(nirs_sig, t, samples',exta_parameters);
  188. elseif strcmp(method,'tddr')
  189. fs = 1/(t(2)-t(1));
  190. data_corr = nst_tddr_correction( nirs_sig , fs );
  191. end
  192. end
  194. function samples = time_to_sample_idx(time, ref_time)
  195. if nargin < 2
  196. assert(all(diff(diff(time))==0));
  197. ref_time = time;
  198. end
  199. samples = round((time - ref_time(1)) / diff(ref_time(1:2))) + 1;
  200. end

process_nst_motion_correction.m at commit 1603dad, under GPL-3.0 · at the source

Overview

Authors: Édouard Delaire1,2, Thomas Vincent3, Zhengchen Cai1,2,4, Alexis Machado5, Laurent Hugueville6, Denis Schwartz6,7, Francois Tadel8, Raymundo Cassani9, Louis Bherer3,10, Jean-Marc Lina11, Mélanie Pélégrini-Issac12, Christophe Grova1,2,5
ORCID iDs: Édouard Delaire, Denis Schwartz, Francois Tadel, Christophe Grova
  1. Concordia University, School of Health, PERFORM Centre, Montréal, Quebec, Canada
  2. Concordia University, Multimodal Functional Imaging Laboratory, Department of Physics, Montréal, Quebec, Canada
  3. Montreal Heart Institute, EPIC Center, Montréal, Quebec, Canada
  4. McGill University, Montreal Neurological Institute, Montreal, Quebec, Canada
  5. McGill University, Multimodal Functional Imaging Laboratory, Biomedical Engineering Department, Neurology and Neurosurgery Department, Montreal, Quebec, Canada
  6. Institut du Cerveau ICM, Centre MEG-EEG, Paris, France
  7. Inserm, CNRS, Centre de Recherche en Neurosciences de Lyon, Lyon, France
  8. Independent Research Engineer, Grenoble, France
  9. McGill University, Montreal Neurological Institute, McConnell Brain Imaging Centre, Montreal, Quebec, Canada
  10. Université de Montréal, Department of Medicine, Montréal, Quebec, Canada
  11. École de Technologie Supérieure, Electrical Engineering Department, Montréal, Quebec, Canada
  12. Sorbonne Université, CNRS, Inserm, Laboratoire d’Imagerie Biomédicale, LIB, CNRS, INSERM, Paris, France
Institutions: Concordia University (United States); Concordia University (Canada); Montreal Heart Institute (Canada); McGill University (Canada); Institut du Cerveau (France); Montreal Neurological Institute and Hospital (Canada); École de Technologie Supérieure (Canada); Centre National de la Recherche Scientifique (France); Inserm (France); Sorbonne Université (France); Laboratoire d’Imagerie Biomédicale (France)
Journal: n/a, volume 12, issue 2, article 025011
Dates: received 5 September 2024; accepted 2 April 2025; published online 15 May 2025; in print April 2025
Type: Research article · Language: English
License: none stated
Identifiers: DOI 10.1117/1.nph.12.2.025011 · PMCID PMC12081164 · OpenAlex W4410387301
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), fNIRS (modality), methods / tools (subfield)
Methods: Spectral & time-frequency, Statistics, Preprocessing, Evoked potentials, fMRI & imaging
Keywords: toolbox, functional near-infrared spectroscopy, conventional functional near-infrared spectroscopy analysis, near-infrared optical tomography, optimal montage, advanced multimodal integration
Topic: Optical Imaging and Spectroscopy Techniques (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: Fonds de Recherche du Québec—Nature et technologies (FRQNT) Research team; NIH grant for USC: “Signal &amp; Image Processing Institute, University of Southern California, Los Angeles, CA USA”; Canadian Institutes of Health Research (PJT-159448); Natural Sciences and Engineering Research Council of Canada Discovery; NSERC Research Tools and Instrumentation Program and the Canadian Foundation for Innovation
Citations: cited by 6 papers (Europe PMC); 114 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (none stated) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 22 matches between paragraphs and lines of code.

Nirstorm/nirstorm

License: GPL-3.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 1603dad92e93000172a2069b182ff8d7d0e7fa62, 21 September 2026
Languages: MATLAB (148), C/C++ (1), C (1), Python (1)
Size: 162 files, 151 scripts
Software Heritage: not archived
Found in: “Code and Data Availability”
Holds: README, license file, tests
Not found: CITATION.cff, environment file, continuous integration, documentation
Tools: Brainstorm (93 files), Signal Processing Toolbox (5 files), export_fig (1 file), Statistics and Machine Learning Toolbox (1 file), Matplotlib (1 file), NumPy (1 file), SPM (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
153 files
At the source: github.com/Nirstorm/nirstorm

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;
  • 151 scripts, each with its path and the digest of its content;
  • 22 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

Datasets cited

Code and data availability statement

The paper has a code and data availability statement. Its license (none stated) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1117/1.nph.12.2.025011.

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, 12 authors, 6 keywords, 5 funders, 103 references.

Cite

This paper

Delaire, É., Vincent, T., Cai, Z., Machado, A., Hugueville, L., Schwartz, D., Tadel, F., Cassani, R., Bherer, L., Lina, J.-M., Pélégrini-Issac, M., & Grova, C. (2025). NIRSTORM: a Brainstorm extension dedicated to functional near-infrared spectroscopy data analysis, advanced 3D reconstructions, and optimal probe design. Neurophotonics, 12(2), 025011. https://doi.org/10.1117/1.nph.12.2.025011

BibTeX

@article{delaire2025nirstorm,
author = {Delaire, Édouard and Vincent, Thomas and Cai, Zhengchen and Machado, Alexis and Hugueville, Laurent and Schwartz, Denis and Tadel, Francois and Cassani, Raymundo and Bherer, Louis and Lina, Jean-Marc and Pélégrini-Issac, Mélanie and Grova, Christophe},
title = {{NIRSTORM: a Brainstorm extension dedicated to functional near-infrared spectroscopy data analysis, advanced 3D reconstructions, and optimal probe design}},
journal = {Neurophotonics},
year = {2025},
volume = {12},
number = {2},
pages = {025011},
publisher = {Society of Photo-Optical Instrumentation Engineers},
issn = {2329-423X},
doi = {10.1117/1.nph.12.2.025011},
url = {https://doi.org/10.1117/1.nph.12.2.025011},
pmcid = {PMC12081164}
}

RIS

TY - JOUR
AU - Delaire, Édouard
AU - Vincent, Thomas
AU - Cai, Zhengchen
AU - Machado, Alexis
AU - Hugueville, Laurent
AU - Schwartz, Denis
AU - Tadel, Francois
AU - Cassani, Raymundo
AU - Bherer, Louis
AU - Lina, Jean-Marc
AU - Pélégrini-Issac, Mélanie
AU - Grova, Christophe
TI - NIRSTORM: a Brainstorm extension dedicated to functional near-infrared spectroscopy data analysis, advanced 3D reconstructions, and optimal probe design
T2 - Neurophotonics
J2 - Neurophotonics
PY - 2025
DA - 2025
VL - 12
IS - 2
SP - 025011
SN - 2329-423X
PB - Society of Photo-Optical Instrumentation Engineers
DO - 10.1117/1.nph.12.2.025011
UR - https://doi.org/10.1117/1.nph.12.2.025011
LA - en
ER -

CSL-JSON

{
"id": "10.1117/1.nph.12.2.025011",
"type": "article-journal",
"title": "NIRSTORM: a Brainstorm extension dedicated to functional near-infrared spectroscopy data analysis, advanced 3D reconstructions, and optimal probe design",
"container-title": "Neurophotonics",
"author": [
{
"family": "Delaire",
"given": "Édouard"
},
{
"family": "Vincent",
"given": "Thomas"
},
{
"family": "Cai",
"given": "Zhengchen"
},
{
"family": "Machado",
"given": "Alexis"
},
{
"family": "Hugueville",
"given": "Laurent"
},
{
"family": "Schwartz",
"given": "Denis"
},
{
"family": "Tadel",
"given": "Francois"
},
{
"family": "Cassani",
"given": "Raymundo"
},
{
"family": "Bherer",
"given": "Louis"
},
{
"family": "Lina",
"given": "Jean-Marc"
},
{
"family": "Pélégrini-Issac",
"given": "Mélanie"
},
{
"family": "Grova",
"given": "Christophe"
}
],
"container-title-short": "Neurophotonics",
"volume": "12",
"issue": "2",
"page": "025011",
"DOI": "10.1117/1.nph.12.2.025011",
"PMCID": "PMC12081164",
"ISSN": "2329-423X",
"publisher": "Society of Photo-Optical Instrumentation Engineers",
"URL": "https://doi.org/10.1117/1.nph.12.2.025011",
"language": "en",
"issued": {
"date-parts": [
[
2025
]
]
}
}

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

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Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
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[7] doi:10.3389/fnhum.2026.1856059 [code]
Feasibility of fNIRS for assessing n-back task-related prefrontal cortex oxygenation after continuous cycling at varying intensities: an evaluation of protocol feasibility.
Journal: Frontiers in human neuroscience
In common: Signal Processing Toolbox, fNIRS, 6 references
[8] doi:10.1117/1.nph.13.3.035006 [code]
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Journal: Neurophotonics
In common: Matplotlib, NumPy, fNIRS, 6 references
[9] doi:10.1093/braincomms/fcaf170 [code]
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Journal: n/a
In common: Brainstorm, Signal Processing Toolbox, Statistics and Machine Learning Toolbox, EEG, 4 references
[10] doi:10.1111/desc.70188
Contextual Transparency Supports Cognitive Control by Reducing Prefrontal Activation and Enhancing Cue Prioritization in Children.
Journal: Developmental science
In common: fNIRS, 6 references

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