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High-Density EEG and Multi-Muscle EMG Dataset during Object Prehension with a sensorized Grasping Box in Humans.

Code ↔ Paper

4 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 4 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Technical Validation › EMG data ↔ EMG_run_SynergyAnalyzer.m, the whole file · a weak match · score 0.86 · negative matrix factorization, Synergy Analyzer, extracted synergy, Muscle synergies, EMG signals, NMF
  2. [2] § Technical Validation › EEG data ↔ EEG_main.m, lines 39–80 · score 0.61 · 13–35 Hz, beta band, pass filtered, 13 Hz, MRCPs, EEG
  3. [3] § Technical Validation › EMG data ↔ EEG_plot.m, lines 1–44 · score 0.57 · beta band activity, motor related, Population, PG, UG, WH
  4. [4] § Technical Validation › EEG data ↔ plot_MRCP_beta_mean.m, the whole file · a weak match · score 0.56 · 13–35 Hz, beta band, activity, MRCPs, 13 Hz, LED

Paper

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

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The authors' code

MATLAB · 82 lines · 4.1 KB · no license · 1 match

  1. function sinergie = EMG_run_SynergyAnalyzer(data_sa, info, par, parametri)
  2. % -------------------------------------------------------------------------
  3. % SCIDATA_SynergyAnalyzer
  4. % -------------------------------------------------------------------------
  5. % This function performs muscle synergy analysis using the SynergyAnalyzer
  6. % toolbox. It extracts spatial synergies (W) and their activation patterns (C)
  7. % from preprocessed EMG data.
  8. %
  9. % Steps:
  10. % 1. Initialize SynergyAnalyzer object
  11. % 2. Run the synergy extraction (NMF-based)
  12. % 3. Automatically determine the number of synergies (using R² threshold)
  13. % 4. Normalize the extracted synergies
  14. % 5. Return the final synergy structure
  15. %
  16. % INPUTS:
  17. % data_sa - structure containing normalized EMG data and metadata
  18. % info - structure with information about EMG signals
  19. % par - parameter structure (type, channel labels, etc.)
  20. % parametri - structure with experiment parameters (e.g., n_muscle)
  21. %
  22. % OUTPUTS:
  23. % sinergie - structure containing extracted synergies:
  24. % .W → spatial synergy matrix [muscles x N_synergies]
  25. % .C → temporal activation coefficients [N_synergies x time]
  26. % .N_sin → number of selected synergies
  27. % -------------------------------------------------------------------------
  29. % -------------------------------------------------------------------------
  30. % PARAMETERS
  31. % -------------------------------------------------------------------------
  32. threshold = 0.92; % R² threshold for selecting synergy number
  33. n_muscle = parametri.n_muscle; % number of muscles recorded
  35. % -------------------------------------------------------------------------
  36. % INITIALIZE SYNERGY ANALYZER
  37. % -------------------------------------------------------------------------
  38. % Create SynergyAnalyzer object using the input data and parameters
  39. sa = SynergyAnalyzer(data_sa, info, par);
  41. %% ------------------------------------------------------------------------
  42. % STEP 1: EXTRACT SPATIAL SYNERGIES
  43. % -------------------------------------------------------------------------
  44. % Configure the extraction options
  45. sa.opt.find.N = 1:n_muscle; % possible number of synergies to test
  46. sa.opt.find.nrep = 50; % number of repetitions (stability check)
  47. sa.opt.find.niter = [5 5 1e-4 100]; % [init_reps, max_iter, tol, iter_NMF]
  48. sa.opt.find.plot = 0; % disable internal plotting during extraction
  50. % Run synergy extraction (typically using Non-negative Matrix Factorization)
  51. s1 = sa.find; % returns structure with extracted synergies
  53. %% ------------------------------------------------------------------------
  54. % STEP 2: SELECT NUMBER OF SYNERGIES
  55. % -------------------------------------------------------------------------
  56. % Select the optimal number of synergies based on R² threshold criterion
  57. opt.type = 'R2thresh'; % use coefficient of determination R²
  58. opt.val = threshold; % threshold value (e.g., 0.92)
  59. Nsel = numsel(s1.syn, opt); % automatic selection of number of synergies
  61. %% ------------------------------------------------------------------------
  62. % STEP 3: EXTRACT AND NORMALIZE SYNERGY MATRICES
  63. % -------------------------------------------------------------------------
  64. % Retrieve spatial synergies (W) and normalize them
  65. W = s1.syn.W{Nsel, 1}; % spatial synergy matrix
  66. W_updated = zeros(size(W, 1), size(W, 2)); % preallocate normalized matrix
  68. % Normalize each synergy vector (L2 norm = 1)
  69. for index_syn = 1:Nsel
  70. Wnorm = sqrt(W(:, index_syn)' * W(:, index_syn)); % compute Euclidean norm
  71. W_updated(:, index_syn) = W(:, index_syn) / Wnorm; % normalize synergy
  72. end
  74. %% ------------------------------------------------------------------------
  75. % STEP 4: SAVE RESULTS IN OUTPUT STRUCTURE
  76. % -------------------------------------------------------------------------
  77. sinergie.W = W_updated; % normalized spatial synergies
  78. sinergie.C = s1.syn.C{Nsel, 1}; % temporal activations
  79. sinergie.N_sin = Nsel; % number of synergies selected
  82. end

EMG_run_SynergyAnalyzer.m, no license · at the source

Overview

Authors: G Lomele1, T Lencioni1, S D’Ambrosio1,2,3, A Comanducci1, F Lucchetti1, A Marzegan1, C Derchi1, S Garzonio2,4,5,6, T Atzori1, M Rabuffetti1, P Castiglioni1,7, M Ferrarin1, L Fornia1,8
ORCID iDs: S Garzonio
  1. IRCCS Fondazione Don Carlo Gnocchi, Milan, Italy
  2. Department of Biomedical and Clinical Sciences, Università degli Studi di Milano, Milan, Italy
  3. Department of Clinical and Experimental Epilepsy, University College London, London, UK
  4. Department of Humanities and Life Sciences, Scuola Universitaria Superiore IUSS, Pavia, Italy
  5. Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genova, Italy
  6. Department of Rehabilitation, Azienda Socio Sanitaria Territoriale (ASST) Gaetano Pini - CTO, Milan, Italy
  7. Università degli Studi dell’Insubria, Varese, Italy
  8. Department of Medical Biotechnology and Translational Medicine, Università degli Studi di Milano, Milan, Italy
Institutions: Don Carlo Gnocchi Foundation (Italy); University of Milan (Italy); University College London (United Kingdom); Istituto Universitario di Studi Superiori di Pavia (Italy); University of Genoa (Italy); University of Insubria (Italy)
Journal: Scientific data, volume 13, issue 1, article 932
Dates: received 21 November 2025; accepted 13 April 2026; published online 18 April 2026
Type: Data paper · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41597-026-07242-y · PMID 42000809 · PMCID PMC13287487 · OpenAlex W7154824746
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), other (modality), human (organism), methods / tools (subfield)
Methods: Smoothing, state filtering, decompositions, Physiology & signal measures
MeSH: Electroencephalography*, Electromyography*, Hand Strength*, Muscle, Skeletal*, Humans, Movement (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (101071900); Ministry of University and Research (MUR) (project MNESYS (PE0000006)); European Union – NextGenerationEU (ECS_00000035)
Citations: cited by 1 paper (Europe PMC); 36 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) 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 4 matches between paragraphs and lines of code.

OSF g6yta

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Languages: MATLAB (13)
Size: 45 files, 13 scripts
Software Heritage: not checked
Found in: “Code availability”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (2 files), Signal Processing Toolbox (2 files)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
13 files
At the source:

Code availability statement

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  • it points to the authors' code: OSF g6yta

Read it in the paper: doi.org/10.1038/s41597-026-07242-y.

Tracing map

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What the map holds:

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  • 13 scripts, each with its path and the digest of its content;
  • 4 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

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41597-026-07242-y.

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, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 6 MeSH terms, 3 funders, 34 references.

Cite

This paper

Lomele, G., Lencioni, T., D’Ambrosio, S., Comanducci, A., Lucchetti, F., Marzegan, A., Derchi, C., Garzonio, S., Atzori, T., Rabuffetti, M., Castiglioni, P., Ferrarin, M., & Fornia, L. (2026). High-Density EEG and Multi-Muscle EMG Dataset during Object Prehension with a sensorized Grasping Box in Humans. Scientific data, 13(1), 932. https://doi.org/10.1038/s41597-026-07242-y

BibTeX

@article{lomele2026high,
author = {Lomele, G and Lencioni, T and D’Ambrosio, S and Comanducci, A and Lucchetti, F and Marzegan, A and Derchi, C and Garzonio, S and Atzori, T and Rabuffetti, M and Castiglioni, P and Ferrarin, M and Fornia, L},
title = {{High-Density EEG and Multi-Muscle EMG Dataset during Object Prehension with a sensorized Grasping Box in Humans}},
journal = {Scientific data},
year = {2026},
month = apr,
volume = {13},
number = {1},
pages = {932},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-07242-y},
url = {https://doi.org/10.1038/s41597-026-07242-y},
pmid = {42000809},
pmcid = {PMC13287487}
}

RIS

TY - JOUR
AU - Lomele, G
AU - Lencioni, T
AU - D’Ambrosio, S
AU - Comanducci, A
AU - Lucchetti, F
AU - Marzegan, A
AU - Derchi, C
AU - Garzonio, S
AU - Atzori, T
AU - Rabuffetti, M
AU - Castiglioni, P
AU - Ferrarin, M
AU - Fornia, L
TI - High-Density EEG and Multi-Muscle EMG Dataset during Object Prehension with a sensorized Grasping Box in Humans
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/04/18
VL - 13
IS - 1
SP - 932
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-07242-y
UR - https://doi.org/10.1038/s41597-026-07242-y
LA - en
ER -

CSL-JSON

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