OSCR

A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery.

Code ↔ Paper

10 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 10 matches
  1. [1] § Background & Summary › Data preprocessing › EEG preprocessing ↔ Code/Preprocessing/reprocessing/EEGPreprocess.m, lines 1–81 · score 0.95 · pop_subcomp, Rejected components, PCA reduction, ICA decomposition, ICLabel, EEGLAB
  2. [2] § Technical Validation › EEG data analysis › Time–domain analysis (ERD/ERS) ↔ Code/Plot/ERDERS.m, lines 153–199 · score 0.70 · Event related desynchronization, baseline power, ERD, ERS, band, filtered
  3. [3] § Background & Summary › Data preprocessing › fNIRS preprocessing ↔ Code/Classification/HybridClassification.m, lines 67–78 · score 0.64 · 5–25 seconds, dimensional, HbR, HbO, event, fNIRS
  4. [4] § Background & Summary › Data preprocessing › EEG preprocessing ↔ Code/Preprocessing/reprocessing/EEG_process.py, lines 6–56 · score 0.64 · notch filtered, bad channels, MNE, preprocessed, raw, Event
  5. [5] § Background & Summary › Data preprocessing › EEG preprocessing ↔ Code/Preprocessing/reprocessing/utils.py, lines 151–201 · score 0.61 · spherical interpolation, bad channels, MNE, raw, preprocessed, EEG
  6. [6] § Technical Validation › EEG data analysis › Time–domain analysis (ERD/ERS) ↔ Code/Plot/ERDERS.m, lines 81–151 · score 0.58 · ERS curves, power changes, ERD
  7. [7] § Technical Validation › EEG data analysis › Frequency-domain analysis (ERSP) ↔ Code/Classification/HybridClassification.m, lines 137–164 · score 0.57 · central parietal, brain regions, frontal, EEG
  8. [8] § Technical Validation › EEG data analysis › Frequency-domain analysis (ERSP) ↔ Code/Plot/ERDERS.m, lines 56–77 · score 0.56 · 13–30 Hz, beta band, 13 Hz
  9. [9] § Technical Validation › fNIRS–EEG classification ↔ Code/Classification/EEGClassification.m, lines 128–146 · score 0.55 · Filter Bank, window classification, EEG
  10. [10] § Technical Validation › fNIRS–EEG classification ↔ Code/Classification/HybridClassification.m, lines 516–584 · score 0.51 · meta features, scores, fold, multimodal, modality, classifiers

Paper

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

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The authors' code

MATLAB · 250 lines · 8.4 KB · no license · 3 matches

  1. %% ERD/ERS Curves for Specific Frequency Bands - Four-Class Task (Individual Plots)
  3. % Subject configuration
  4. subject_index = 2;
  5. subject_id = sprintf('%02d', subject_index);
  6. filename = sprintf('subject_%s_motor_imagery_events.mat', subject_id);
  8. % Data directory configuration
  9. time = '0927';
  10. data_root = 'D:\Code\Dataset_4-classification MI';
  11. data_file_path = fullfile(data_root, 'Data', 'Dataset v2', time, 'EEG', filename);
  13. % Load data
  14. load(data_file_path)
  16. % Select C3 and C4 channels (motor cortex channels)
  17. selected_channels = [17, 21];
  19. % Prepare data with correct dimensions
  20. x_train = zeros(length(selected_channels), size(preprocessed_data, 2), size(preprocessed_data, 3));
  21. for i = 1:length(selected_channels)
  22. channel_data = preprocessed_data(selected_channels(i), :, :);
  23. x_train(i, :, :) = reshape(channel_data, [1, size(channel_data, 2), size(channel_data, 3)]);
  24. end
  25. fs = srate;
  27. % Prepare labels
  28. [~, y_label] = max(label, [], 1);
  30. % Task class names
  31. task_names = {'Left to right', 'Top to bottom', 'Top left to bottom right', 'Top right to bottom left'};
  33. %% Alpha band (8-12Hz) ERD/ERS curves - separate plot for each task
  34. for task = 1:4
  35. % Select data for current task
  36. task_indices = find(y_label == task);
  37. if isempty(task_indices)
  38. warning(['No data found for task ', num2str(task)]);
  39. continue;
  40. end
  42. x_task = x_train(:, :, task_indices);
  43. y_task = ones(size(task_indices));
  45. % Create figure
  46. figure('Name', ['Alpha Band ERD/ERS Curves - ', task_names{task}], 'Position', [100, 100, 1000, 500]);
  47. annotation('textbox', [0.1, 0.98, 0.8, 0.02], ...
  48. 'String', ['Four-Class Task - ', task_names{task}, ' Alpha Band (8-12Hz) ERD/ERS Curves'], ...
  49. 'HorizontalAlignment', 'center', 'VerticalAlignment', 'top', ...
  50. 'FontSize', 14, 'FontWeight', 'bold', 'EdgeColor', 'none', 'FitBoxToText', false);
  52. % Plot ERD/ERS curves
  53. plotERDERS(x_task, y_task, 8, 12, fs, 5, 15, ch_names, selected_channels, [-100, 250], [-5, 10]);
  54. end
  56. %% Beta band (13-30Hz) ERD/ERS curves - separate plot for each task
  57. for task = 1:4
  58. % Select data for current task
  59. task_indices = find(y_label == task);
  60. if isempty(task_indices)
  61. warning(['No data found for task ', num2str(task)]);
  62. continue;
  63. end
  65. x_task = x_train(:, :, task_indices);
  66. y_task = ones(size(task_indices));
  68. % Create figure
  69. figure('Name', ['Beta band ERD/ERS Curves - ', task_names{task}], 'Position', [100, 100, 1000, 500]);
  70. annotation('textbox', [0.1, 0.98, 0.8, 0.02], ...
  71. 'String', ['Four-Class Task - ', task_names{task}, ' Beta Band (13-30Hz) ERD/ERS Curves'], ...
  72. 'HorizontalAlignment', 'center', 'VerticalAlignment', 'top', ...
  73. 'FontSize', 14, 'FontWeight', 'bold', 'EdgeColor', 'none', 'FitBoxToText', false);
  75. % Plot ERD/ERS curves
  76. plotERDERS(x_task, y_task, 13, 30, fs, 5, 15, ch_names, selected_channels, [-100, 100], [-5, 10]);
  77. end
  79. %% Helper Functions
  81. function plotERDERS(data, labels, flow, fhigh, fs, baseline_end, trial_length, ch_names, selected_channels, ylim_range, time_range)
  82. % Plot ERD/ERS curves
  83. %
  84. % Inputs:
  85. % data - Input data [channels x time_points x trials]
  86. % labels - Task labels [1 x trials]
  87. % flow, fhigh - Lower and upper frequency band limits (Hz)
  88. % fs - Sampling rate (Hz)
  89. % baseline_end - Baseline period end time (seconds)
  90. % trial_length - Total trial length (seconds)
  91. % ch_names - Channel names cell array
  92. % selected_channels - Selected channel indices
  93. % ylim_range - Y-axis range [lower upper], e.g., [-100 250]
  94. % time_range - Time axis range [start end], e.g., [-2 10]
  96. % Set default time range if not provided
  97. if nargin < 11 || isempty(time_range)
  98. time_range = [0, trial_length];
  99. end
  101. % Plot style configuration
  102. colors = {'r', 'b'};
  103. line_width = 2;
  104. smoothPara = 80; % Smoothing window size
  106. % Calculate and plot ERD/ERS curves
  107. hold on;
  108. for ch = 1:size(data, 1)
  109. % Get all trials for current channel
  110. channel_trials = squeeze(data(ch, :, :));
  112. % Compute ERD/ERS
  113. [erders, t] = computeERDERS(channel_trials, flow, fhigh, fs, ...
  114. round(baseline_end*fs), round(trial_length*fs), smoothPara);
  116. % Apply smoothing
  117. erders = movingAverage(erders, smoothPara);
  119. % Adjust time axis (shift left by baseline_end seconds)
  120. t_adjusted = t - baseline_end;
  122. % Plot curve
  123. plot(t_adjusted, erders, colors{ch}, 'LineWidth', line_width);
  124. end
  126. % Set axis limits and labels
  127. xlim(time_range);
  128. xlabel('Time (s)');
  129. ylabel('Relative Power Change (%)');
  131. % Set Y-axis range
  132. if nargin >= 10 && ~isempty(ylim_range)
  133. ylim(ylim_range);
  134. else
  135. ylim([-100 100]);
  136. end
  137. grid on;
  139. % Add reference lines
  140. plot([baseline_end baseline_end], ylim_range, 'k:'); % Baseline end
  141. plot([0 0], ylim_range, 'k:'); % Zero time point
  142. plot([time_range(1) time_range(2)], [0 0], 'k--'); % Zero percent reference
  144. % Add legend
  145. legend_labels = cell(size(data, 1), 1);
  146. for i = 1:size(data, 1)
  147. legend_labels{i} = ['Channel ', ch_names{selected_channels(i)}];
  148. end
  149. legend(legend_labels, 'Location', 'best');
  150. hold off;
  151. end
  153. function [erders, t] = computeERDERS(trials, flow, fhigh, fs, baseline_samples, total_samples, smoothPara)
  154. % Compute ERD/ERS (Event-Related Desynchronization/Synchronization)
  155. %
  156. % Inputs:
  157. % trials - Input trials [time_points x trials]
  158. % flow, fhigh - Lower and upper frequency band limits (Hz)
  159. % fs - Sampling rate (Hz)
  160. % baseline_samples - Number of baseline samples
  161. % total_samples - Total number of samples
  162. % smoothPara - Smoothing window size
  163. %
  164. % Outputs:
  165. % erders - ERD/ERS values (%)
  166. % t - Time vector (seconds)
  168. % Ensure total samples doesn't exceed available data
  169. total_samples = min(total_samples, size(trials, 1));
  171. % Initialize
  172. n_trials = size(trials, 2);
  173. filtered_data = zeros(size(trials));
  174. butterOrder = 6; % Butterworth filter order
  176. % Filter each trial separately
  177. for i = 1:n_trials
  178. filtered_data(:, i) = filter_param(trials(:, i), flow, fhigh, fs, butterOrder);
  179. end
  181. % Calculate power
  182. power_data = filtered_data.^2;
  184. % Average power across all trials
  185. avg_power = mean(power_data, 2);
  186. avg_power = avg_power(1:total_samples);
  188. % Apply smoothing
  189. avg_power = movingAverage(avg_power, smoothPara);
  191. % Calculate baseline power (average during baseline period)
  192. baseline_power = mean(avg_power(1:baseline_samples));
  194. % Calculate ERD/ERS
  195. erders = ((avg_power - baseline_power) / baseline_power) * 100;
  197. % Create time vector
  198. t = (0:length(erders)-1) / fs;
  199. end
  201. function smoothed = movingAverage(data, window_size)
  202. % Custom moving average smoothing function
  203. %
  204. % Inputs:
  205. % data - Input data vector
  206. % window_size - Window size (must be odd)
  207. %
  208. % Output:
  209. % smoothed - Smoothed data vector
  211. % Ensure window size is odd
  212. if mod(window_size, 2) == 0
  213. window_size = window_size + 1;
  214. end
  216. n = length(data);
  217. half_window = floor(window_size / 2);
  218. smoothed = zeros(size(data));
  220. % Pad data to handle boundaries
  221. padded_data = [repmat(data(1), [half_window, 1]); data; repmat(data(end), [half_window, 1])];
  223. % Apply moving average
  224. for i = 1:n
  225. smoothed(i) = mean(padded_data(i:i+window_size-1));
  226. end
  227. end
  229. function filterdata = filter_param(data, low, high, sampleRate, filterorder)
  230. % Bandpass filter for EEG data
  231. %
  232. % Inputs:
  233. % data - EEG data to be filtered
  234. % low - High-pass filter cutoff frequency (Hz)
  235. % high - Low-pass filter cutoff frequency (Hz)
  236. % sampleRate - Sampling rate (Hz)
  237. % filterorder - Butterworth filter order
  238. %
  239. % Output:
  240. % filterdata - Filtered EEG data
  242. % Calculate normalized cutoff frequencies
  243. filtercutoff = [low*2/sampleRate high*2/sampleRate];
  245. % Design Butterworth filter
  246. [filterParamB, filterParamA] = butter(filterorder, filtercutoff);
  248. % Apply filter
  249. filterdata = filter(filterParamB, filterParamA, data);
  250. end

ERDERS.m at commit dc0536f, no license · at the source

Overview

Authors: Lufeng Feng1, Baomin Xu1, Haoran Zhang1, Bihai Lin1, Zuxuan Deng1, Sidi Tao1, Chenyu Liu2, Shifan Jia3, Li Duan1, Ziyu Jia4
  1. Beijing Jiaotong University,Beijing, 100044 China
  2. Nanyang Technological University,50 Nanyang Avenue, Singapore, Singapore
  3. Simon Fraser University, Vancouver V5A 1S6,Burnaby, Canada
  4. Institute of Automation, Chinese Academy of Sciences,Beijing, 100044 China
Institutions: Beijing Jiaotong University (China); Nanyang Technological University (Singapore); Simon Fraser University (Canada); Chinese Academy of Sciences (China)
Journal: Scientific data, volume 13, issue 1, article 1159
Dates: received 5 January 2026; accepted 30 June 2026; published online 10 August 2026
Type: Data paper · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41597-026-07807-x · PMID 42575895 · PMCID PMC13458765 · OpenAlex W7202073435
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), fNIRS (modality), human (organism), methods / tools (subfield)
Methods: Spectral & time-frequency, Preprocessing, Connectivity, Smoothing, state filtering, decompositions, Machine learning, Evoked potentials, fMRI & imaging, Physiology & signal measures
MeSH: Electroencephalography*, Imagination*, Upper Extremity*, Adult, Female, Humans, Male, Spectroscopy, Near-Infrared, Young Adult (* major topic)
Journal subjects: Data Descriptor
Funding: Beijing Nova Program (No.20230484257); National Natural Science Foundation (62272031); National Key R&D Program of China (No.2021ZD0113002)
Citations: cited by 1 paper (Europe PMC); 50 references in the paper

Abstract

Unilateral limb motor imagery (MI) plays an important role in upper-limb motor rehabilitation and precise control of external devices, and places higher demands on spatial resolution. However, most existing public datasets focus on binary- or four-class left–right limb paradigms that mainly exploit coarse hemispheric lateralization, and there is still a lack of multimodal datasets that simultaneously record electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) for unilateral multi-directional MI. To address this gap, we present MIND, a public motor imagery fNIRS–EEG dataset based on a four-class directional MI paradigm of the right upper limb. The dataset includes 64-channel EEG recordings (1000 Hz) and 51-channel fNIRS recordings (47.62 Hz) from 30 participants (12 females, 18 males; aged 19.0–25.0 years). We analyze the spatiotemporal characteristics of EEG spectral power and hemodynamic responses, and provide baseline classification summaries for EEG, fNIRS, and combined modalities as technical validation of task-related information in the dataset. We expect that this dataset will facilitate the evaluation and comparison of neuroimaging analysis and decoding methods.

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 10 matches between paragraphs and lines of code.

useflf/Multimodal-fNIRS-EEG-Dataset

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: dc0536ffa7add0b1ab893210a31e5ea742b3e617, 25 December 2025
Languages: MATLAB (6), Python (2)
Size: 9 files, 8 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (3 files), Statistics and Machine Learning Toolbox (3 files), EEGLAB (1 file), ICLabel (1 file), Matplotlib (1 file), MNE-Python (1 file), NumPy (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/useflf/Multimodal-fNIRS-EEG-Dataset

Code availability

The usage instructions for this dataset are openly available in our GitHub mirror repository (https://github.com/useflf/Multimodal-fNIRS-EEG-Dataset). The folder Code/Preprocessing/ contains preprocessing scripts for both EEG and fNIRS. For EEG preprocessing, /EEG_process.py and EEG preprocessing /EEGPreprocess.m are provided. For fNIRS preprocessing, /2024_11 _11_snirf_trans_merge.ipynb is used for automatic event labeling, and fNIRS preprocessing /no mrk.ipynb is used for manual label refinement. The folder Code/Plot/ includes scripts for EEG time–frequency analysis and topographic mapping, as well as fNIRS averaged hemodynamic response analysis and topographic visualization. The folder Code/Classification/ provides unimodal and multimodal classification code based on the FBCSP + sLDA pipeline. All researchers are free to download, use, and cite these resources. We recommend reading the accompanying documentation carefully before using the dataset, and following the original acquisition protocol and ethics statement.

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

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;
  • 10 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 9 MeSH terms, 3 funders, 45 references.

Cite

This paper

Feng, L., Xu, B., Zhang, H., Lin, B., Deng, Z., Tao, S., Liu, C., Jia, S., Duan, L., & Jia, Z. (2026). A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery. Scientific data, 13(1), 1159. https://doi.org/10.1038/s41597-026-07807-x

BibTeX

@article{feng2026multimodal,
author = {Feng, Lufeng and Xu, Baomin and Zhang, Haoran and Lin, Bihai and Deng, Zuxuan and Tao, Sidi and Liu, Chenyu and Jia, Shifan and Duan, Li and Jia, Ziyu},
title = {{A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery}},
journal = {Scientific data},
year = {2026},
month = aug,
volume = {13},
number = {1},
pages = {1159},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-07807-x},
url = {https://doi.org/10.1038/s41597-026-07807-x},
pmid = {42575895},
pmcid = {PMC13458765}
}

RIS

TY - JOUR
AU - Feng, Lufeng
AU - Xu, Baomin
AU - Zhang, Haoran
AU - Lin, Bihai
AU - Deng, Zuxuan
AU - Tao, Sidi
AU - Liu, Chenyu
AU - Jia, Shifan
AU - Duan, Li
AU - Jia, Ziyu
TI - A Multimodal fNIRS-EEG Dataset for Unilateral Limb Motor Imagery
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/08/10
VL - 13
IS - 1
SP - 1159
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-07807-x
UR - https://doi.org/10.1038/s41597-026-07807-x
LA - en
ER -

CSL-JSON

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