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fNIRS evidence for enhanced brain activity during task-based Arabic learning in virtual reality.

Code ↔ Paper

2 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 2 matches · all tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Data analysis › fNIRS data analysis › Preprocessing ↔ preprocessNIRSData.m, the whole file · a weak match · score 0.95 · Beer Lambert law, band pass filter, Temporal Derivative Distribution, optical density, Repair, modified
  2. [2] § Data analysis › fNIRS data analysis › fNIRS system ↔ Channel_placement.m, the whole file · a weak match · score 0.66 · source detector pairs, Channel placement, fitted

Paper

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

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The authors' code

MATLAB · 61 lines · 2.1 KB · no license · 1 match

  3. %% Function for fNIRS data pre-processing
  4. % Noble C. Amadi
  5. function [Day1_data, Day2_data] = preprocessNIRSData(Subfolders, FolderPath)
  6. % Initialize data cell arrays
  7. Day1_data = cell(numel(Subfolders), 6);
  8. %Day2_data = cell(numel(Subfolders), 6);
  10. % Loop through subfolders for data
  11. for a = 1:numel(Subfolders)
  12. subfolderName = Subfolders(a).name;
  14. % List files in "Day 1" folder
  15. day1Files = dir(fullfile(FolderPath, subfolderName, 'Day 1', '*.nirs'));
  16. % List files in "Day 2" folder
  17. %day2Files = dir(fullfile(FolderPath, subfolderName, 'Day 2', '*.nirs'));
  19. % Loop through the files for each day
  20. for b = 1:numel(day1Files)
  21. % Load the data for Day 1 and Day 2
  22. dataDay1 = nirs.io.loadDotNirs(fullfile(FolderPath, subfolderName, 'Day 1', day1Files(b).name));
  23. %dataDay2 = nirs.io.loadDotNirs(fullfile(FolderPath, subfolderName, 'Day 2', day2Files(b).name));
  25. %% DATA PRE-PROCESSING
  26. %% converting to Optical density
  27. j = nirs.modules.OpticalDensity();
  28. %% Bandpass Filter
  29. j = eeg.modules.BandPassFilter(j);
  30. j.highpass = 0.2;
  31. j.lowpass = 1;
  32. j.do_downsample= 0;
  33. %% Temporal Derivative Distribution Repair (TDDR) method
  34. j = nirs.modules.TDDR(j);
  35. %% Convert to modified BL
  36. j = nirs.modules.BeerLambertLaw(j);
  37. %% OUTPUT
  38. hb_Day1 = j.run(dataDay1);
  39. %hb_Day2 = j.run(dataDay2);
  40. Day1_data{a, b} = hb_Day1.data;
  41. %Day2_data{a, b} = hb_Day2.data;
  43. %% QUALITY CHECK
  44. T = 500;
  45. % Store the data or NaN if any value is outside the threshold
  46. % (Day 1)
  47. for col = 1:size(Day1_data{a, b}, 2)
  48. if any(Day1_data{a, b}(:, col) > T | Day1_data{a, b}(:, col) < -T)
  49. Day1_data{a, b}(:, col) = NaN;
  50. end
  51. end
  52. % Store the data or NaN if any value is outside the threshold
  53. % (Day 2)
  54. % for col = 1:size(Day2_data{a, b}, 2)
  55. % if any(Day2_data{a, b}(:, col) > T | Day2_data{a, b}(:, col) < -T)
  56. % Day2_data{a, b}(:, col) = NaN;
  57. % end
  58. % end
  59. end
  60. end; clc
  61. end

preprocessNIRSData.m at commit b013a11, no license · at the source

Overview

Authors: Noble Amadi1, Wei-Chiang Lin1, Noha Elsakka2, Melissa Baralt2
ORCID iDs: Noble Amadi, Melissa Baralt
  1. Florida International University, Department of Biomedical Engineering, United States
  2. Florida International University, Department of Modern Languages, United States
Institutions: Florida International University (United States)
Journal: Neuroimage. Reports, volume 6, issue 2, article 100358
Dates: received 21 November 2025; accepted 14 May 2026; published online 17 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1016/j.ynirp.2026.100358 · PMID 42182079 · PMCID PMC13197770 · OpenAlex W7161477737
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: fNIRS (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Preprocessing
Keywords: SLA (second language acquisition), SL2 (social brain of language learning framework), TBLT (task-based language learning), Virtual reality, AL2 (Arabic as an additional language)
Topic: Virtual Reality Applications and Impacts (Human-Computer Interaction, Computer Science), according to OpenAlex
Funding: Aspen Institute; Bureau of Educational and Cultural Affairs
Citations: not cited yet (Europe PMC); 54 references in the paper

Abstract

Significance: As one of the world's most widely spoken languages, Arabic holds immense cultural and geopolitical importance, but traditional teaching methods often fall short in preparing learners for real-life interaction. Virtual reality (VR) and task-based language teaching (TBLT) offer promising, immersive alternatives, but the neurobiological mechanisms underlying their effectiveness remain unclear. Understanding these mechanisms can inform second language pedagogy.

Aim: This study investigated whether immersive, task-based Arabic vocabulary learning in VR elicits stronger brain activity and superior learning outcomes than traditional lecture-style instruction.

Approach: Twelve English-dominant adults with no prior Arabic knowledge were randomly assigned to VR or traditional learning groups. Participants completed receptive and productive vocabulary pre- and posttests while cortical activity was recorded using functional near-infrared spectroscopy (fNIRS). fNIRS signals were preprocessed and compared across groups.

Results: Both groups showed significant pre-to-post gains in receptive vocabulary scores. However, the VR group exhibited significantly greater improvements in productive performance (p = 0.002). fNIRS analyses revealed consistently higher hemodynamic variability in prefrontal (Broca's area), parietal and superior temporal (Wernicke's area) regions for The VR group during learning and subsequent tests, indicating greater brain activity.

Conclusions: VR-mediated TBLT enhances cortical activation and productive Arabic performance compared with traditional instruction, offering neurobiological evidence for immersive technologies in language learning.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

NOBLE1264

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: the link answers
Software Heritage: not checked
Found in: “Data availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
At the source: github.com/NOBLE1264

NOBLE1264/fNIRS-Arabic-Language-Learning

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: b013a1159a83590a78d79d88deb269a877766f8d, 25 October 2025
Languages: MATLAB (14)
Size: 309 files, 14 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Statistics and Machine Learning Toolbox (4 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
15 files
At the source: github.com/NOBLE1264/fNIRS-Arabic-Language-Learning

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:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 14 scripts, each with its path and the digest of its content;
  • 2 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.

Data availability

The data and analysis scripts that support the findings of this study are openly available in the GitHub repository: NOBLE1264 (https://github.com/NOBLE1264)/fNIRS-Arabic-Language-Learning (https://github.com/NOBLE1264/fNIRS-Arabic-Language-Learning). The repository includes all MATLAB code used for preprocessing, feature extraction, and statistical analysis, as well as representative anonymized fNIRS datasets for VR and Trad groups.

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

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 5 keywords, 2 funders, 28 references.

Cite

This paper

Amadi, N., Lin, W.-C., Elsakka, N., & Baralt, M. (2026). fNIRS evidence for enhanced brain activity during task-based Arabic learning in virtual reality. Neuroimage. Reports, 6(2), 100358. https://doi.org/10.1016/j.ynirp.2026.100358

BibTeX

@article{amadi2026fnirs,
author = {Amadi, Noble and Lin, Wei-Chiang and Elsakka, Noha and Baralt, Melissa},
title = {{fNIRS evidence for enhanced brain activity during task-based Arabic learning in virtual reality}},
journal = {Neuroimage. Reports},
year = {2026},
month = may,
volume = {6},
number = {2},
pages = {100358},
publisher = {Elsevier},
issn = {2666-9560},
doi = {10.1016/j.ynirp.2026.100358},
url = {https://doi.org/10.1016/j.ynirp.2026.100358},
pmid = {42182079},
pmcid = {PMC13197770}
}

RIS

TY - JOUR
AU - Amadi, Noble
AU - Lin, Wei-Chiang
AU - Elsakka, Noha
AU - Baralt, Melissa
TI - fNIRS evidence for enhanced brain activity during task-based Arabic learning in virtual reality
T2 - Neuroimage. Reports
J2 - Neuroimage Rep
PY - 2026
DA - 2026/05/17
VL - 6
IS - 2
SP - 100358
SN - 2666-9560
PB - Elsevier
DO - 10.1016/j.ynirp.2026.100358
UR - https://doi.org/10.1016/j.ynirp.2026.100358
LA - en
ER -

CSL-JSON

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

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