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Decoupling simultaneous motor imagination and execution via orthogonal ECoG neural representations.

Code ↔ Paper

13 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 13 matches · 2 of them tie a paragraph to a whole file without given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Data acquisition and signal preprocessing ↔ Global_parameters.py, the whole file · a weak match · score 0.95 · 13–22 Hz, 22–37 Hz, 37–60 Hz, 60–250 Hz, 7–13 Hz, ECoG · Report a mismatch
  2. [2] § Results ↔ Global_parameters.py, the whole file · a weak match · score 0.92 · 13–22 Hz, 22–37 Hz, 37–60 Hz, 60–250 Hz, 7–13 Hz, high beta · Report a mismatch
  3. [3] § Methods › Experimental setup ↔ Utilities_and_Loading.py, lines 199 to 231 · score 0.81 · flexor digitorum, extensor digitorum, biceps, trapezius, triceps, ECoG · Report a mismatch
  4. [4] § Methods › Neural subspaces identification ↔ Manifolds_analysis.py, lines 166 to 192 · score 0.69 · Stiefel manifold, exclusive subspaces, squared, Pymanopt, gradient, sum · Report a mismatch
  5. [5] § Methods › Movement decoding ↔ Decoding.py, lines 126 to 174 · score 0.59 · balanced accuracies, fitted, stratified, classes, fold, LDA · Report a mismatch
  6. [6] § Results › Neural spaces maintain stable task-relevance across months ↔ Main_Manifolds_Ex_and_Im_subspaces_separation_sessions_decoding_LOSO.ipynb, lines 737 to 754 · score 0.59 · pairwise comparison, post hoc, FDR corrected, Friedman, decoding, subspaces · Report a mismatch
  7. [7] § Results › Execution and imagination dynamics show partial overlap at the mesoscale level ↔ Decoding.py, lines 37 to 79 · score 0.59 · linear discriminant, confusion matrices, folds, LDA, shuffling, permutation · Report a mismatch
  8. [8] § Results › Neural spaces maintain stable task-relevance across months ↔ Main_Manifolds_Ex_and_Im_subspaces_separation_sessions_stability_LOSO.ipynb, lines 509 to 526 · score 0.58 · pairwise comparison, post hoc, FDR corrected, Friedman, subspaces, execution · Report a mismatch
  9. [9] § Results › Exclusive neural spaces enable the separation of concurrent imagination and execution activity ↔ Main_Manifolds_Dual_Tasks.ipynb, lines 547 to 561 · score 0.57 · paired permutation, Sh Im, dual task, Ex Im, predictions, FDR · Report a mismatch
  10. [10] § Results ↔ Utilities_and_Loading.py, lines 199 to 231 · score 0.57 · extensor digitorum, ECoG, flexors, deltoid, lateral, muscles · Report a mismatch
  11. [11] § Results › Frequency bands contribute differently to neural spaces ↔ Plotting.py, lines 765 to 866 · score 0.54 · log ratios, Hartigan dip, unimodal, Sh, Ex, bands · Report a mismatch
  12. [12] § Results › Exclusive neural spaces enable the separation of concurrent imagination and execution activity ↔ Main_Manifolds_Dual_Tasks.ipynb, lines 85 to 121 · score 0.54 · wrist extension executed, reaching executed, dual task, GRI, WLI, REE · Report a mismatch
  13. [13] § Methods › Data acquisition and signal preprocessing ↔ Preprocessing.py, lines 90 to 103 · score 0.53 · bandpass filtered, Hilbert, envelope, block, signals, preprocessing · Report a mismatch

Paper

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The authors' code

Python · 89 lines · 5.6 KB · CC-BY-4.0 · 2 matches

  1. import seaborn as sns
  2. from Utilities_and_Loading import *
  4. def define_global_variables(script_vers):
  5. my_general_parameters = {}
  6. # Paths
  7. my_general_parameters['data_path'] = '../../Data/'
  8. vars_path = '../../Results/variables'
  9. figures_path = '../../Results/figures'
  10. my_general_parameters['vars_path'] = create_sub_folder(vars_path, script_vers)
  11. my_general_parameters['figures_path'] = create_sub_folder(figures_path, script_vers)
  12. # Mapping between trials and codes
  13. my_general_parameters['Trial_map'] = {'Mouvement2Dréel' : 'RE-E',
  14. 'Mouvement2Dimaginé' : 'RE-I',
  15. 'Extensiondepoignetréelle' : 'WE-E',
  16. 'Extensiondepoignetimaginée' : 'WE-I',
  17. 'Mouvement2Dréeletextensiondepoignetréelle' : 'RE-E + WE-E',
  18. 'Mouvement2Dréeletextensiondepoignetimaginée' : 'RE-E + WE-I',
  19. 'Mouvement2Dimaginéetextensiondepoignetréelle' : 'RE-I + WE-E',
  20. 'Extensiondepoignetstimulée' : 'WE-S',
  21. 'Mouvementépaulestimulé' : 'RE-S',
  22. 'Mouvementépauleréel' : 'RE-E',
  23. 'Mouvementépauleimaginé' : 'RE-I',
  24. 'Executedreaching+Imaginedwristextension' : 'RE-E + WE-I',
  25. 'Executedreaching+Imaginedwalking' : 'RE-E + WL-I',
  26. 'Executedreaching+Imaginedgrasping' : 'RE-E + GR-I',
  27. 'Executedwristextension+Imaginedwalking': 'WE-E + WL-I',
  28. 'Executedwristextension+Imaginedreaching' : 'RE-I + WE-E',
  29. 'Imaginedreaching' : 'RE-I',
  30. 'Executedreaching' : 'RE-E',
  31. 'Imaginedwristextension' : 'WE-I',
  32. 'Executedwristextension' : 'WE-E',
  33. 'Imaginewalking' : 'WL-I',
  34. 'Imaginegrasping' : 'GR-I'}
  35. # Sampling frequency of ECoG signals
  36. my_general_parameters['fs'] = 585.137507314219
  37. # Experimental values
  38. my_general_parameters['Cue_time_pre_event'] = 1 #seconds
  39. my_general_parameters['Duration_movement'] = 4 #seconds
  40. # Filtering values
  41. my_general_parameters['Low_cutoff'] = 0.5 #Hz
  42. my_general_parameters['High_cutoff'] = 292.8 #Hz
  43. # Frequenct bands definition
  44. my_general_parameters['Freq_bands'] = {'delta' : [1,4],
  45. 'theta' : [4,7],
  46. 'alpha' : [7,13],
  47. 'low_beta' : [13,22],
  48. 'high_beta' : [22,37],
  49. 'low_gamma' : [37,60],
  50. 'high_gamma' : [60,250]}
  51. # Plotting
  52. pal = sns.color_palette('Set2', 12)
  53. my_colors = pal.as_hex()
  54. my_general_parameters['Colors_movements'] = {'RE-E' : my_colors[0],
  55. 'RE-I' : my_colors[1],
  56. 'WE-E' : my_colors[2],
  57. 'WE-I' : my_colors[3],
  58. 'GR-I' : my_colors[4],
  59. 'WL-I' : my_colors[5],
  60. 'RE-E + WE-I' : my_colors[6],
  61. 'RE-I + WE-E' : my_colors[7],
  62. 'RE-E + WE-E' : my_colors[8],
  63. 'RE-E + WL-I' : my_colors[9],
  64. 'RE-E + GR-I' : my_colors[10],
  65. 'WE-E + WL-I' : my_colors[11]}
  67. my_general_parameters['Colormaps_bands'] = {'delta' : 'Purples',
  68. 'theta' : 'Blues',
  69. 'alpha' : 'Greens',
  70. 'beta' : 'Oranges',
  71. 'low_gamma' : 'Reds',
  72. 'high_gamma' : 'Reds',
  73. 'All_bands' : 'bone'}
  74. my_general_parameters['Colormaps_conditions'] = {'Ex' : 'Greens',
  75. 'Im' : 'Blues',
  76. 'Sh' : 'Greys'}
  77. # my_general_parameters['Color_shared'] = {'M1' : 'lightgray',
  78. # 'S1' : 'darkgray',
  79. # 'Full' : 'lightgray'}
  80. # Channel locations
  81. my_general_parameters['Channels_location'] = ['M1', 'S1', 'M1', 'S1', 'M1', 'S1', 'M1', 'M1',
  82. 'M1', 'M1', 'M1', 'M1', 'M1', 'M1', 'M1', 'M1',
  83. 'M1', 'M1', 'M1', 'S1', 'S1', 'S1', 'S1', 'S1',
  84. 'S1', 'S1', 'S1', 'S1', 'S1', 'S1', 'S1', 'S1']
  85. # EMG
  86. my_general_parameters['EMG_fs'] = 2000
  87. my_general_parameters['EMG_sess'] = ['ses-004', 'ses-005', 'ses-006', 'ses-007', 'ses-008']
  89. return my_general_parameters

Global_parameters.py, under CC-BY-4.0 · at the source

Overview

Authors: Leonardo Pollina1, Lucas Struber2, Valeria de Seta1,3, Eleonora Russo4, Serpil Karakas2, Stephan Chabardes2,5, Tetiana Aksenova2, Guillaume Charvet2, Solaiman Shokur1,4,6, Silvestro Micera1,4,6
ORCID iDs: Leonardo Pollina, Lucas Struber, Valeria de Seta, Eleonora Russo, Guillaume Charvet, Solaiman Shokur, Silvestro Micera
  1. Translational Neural Engineering Laboratory, Neuro-X Institute, EPFL,Geneva, Switzerland
  2. University Grenoble Alpes, CEA, LETI, Clinatec,Grenoble, France
  3. Department of Clinical Neuroscience, Lausanne University Hospital, UNIL,Lausanne, Switzerland
  4. The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna,Pontedera, Italy
  5. University Grenoble Alpes, Grenoble University Hospital,Grenoble, France
  6. Modular Implantable Neuroprostheses Laboratory, Università Vita-Salute San Raffaele and Scuola Superiore Sant’Anna,Milan, Italy
Institutions: École Polytechnique Fédérale de Lausanne (Switzerland); Clinatec (France); University of Lausanne (Switzerland); Scuola Superiore Sant'Anna (Italy); Centre Hospitalier Universitaire de Grenoble (France); Vita-Salute San Raffaele University (Italy)
Journal: Nature communications, volume 17, issue 1, article 4662
Dates: received 25 May 2025; accepted 13 March 2026; published online 31 March 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41467-026-71234-0 · PMID 41917049 · PMCID PMC13201640 · OpenAlex W7147215884
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: intracranial EEG (iEEG / ECoG / SEEG) (modality), human (organism), systems (subfield)
Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Physiology & signal measures
Keywords: Neural decoding, Biomedical engineering
MeSH: Electrocorticography*, Imagination*, Motor Cortex*, Brain-Computer Interfaces, Humans, Male, Movement, Quadriplegia (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 59 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

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Zenodo 18317718

License: CC-BY-4.0
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: Jupyter (14), Python (7)
Size: 21 files, 21 scripts
Software Heritage: not checked
Found in: “Code availability”
Holds: 14 notebooks
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (18 files), Matplotlib (14 files), seaborn (14 files), SciPy (4 files), pandas (3 files), scikit-learn (3 files), h5py (1 file), Pingouin (1 file), PyTorch (1 file), statsmodels (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
19 files
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Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 2 keywords, 8 MeSH terms, 56 references.

Cite

This paper

Pollina, L., Struber, L., de Seta, V., Russo, E., Karakas, S., Chabardes, S., Aksenova, T., Charvet, G., Shokur, S., & Micera, S. (2026). Decoupling simultaneous motor imagination and execution via orthogonal ECoG neural representations. Nature communications, 17(1), 4662. https://doi.org/10.1038/s41467-026-71234-0

BibTeX

@article{pollina2026decoupling,
author = {Pollina, Leonardo and Struber, Lucas and de Seta, Valeria and Russo, Eleonora and Karakas, Serpil and Chabardes, Stephan and Aksenova, Tetiana and Charvet, Guillaume and Shokur, Solaiman and Micera, Silvestro},
title = {{Decoupling simultaneous motor imagination and execution via orthogonal ECoG neural representations}},
journal = {Nature communications},
year = {2026},
month = mar,
volume = {17},
number = {1},
pages = {4662},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-71234-0},
url = {https://doi.org/10.1038/s41467-026-71234-0},
pmid = {41917049},
pmcid = {PMC13201640}
}

RIS

TY - JOUR
AU - Pollina, Leonardo
AU - Struber, Lucas
AU - de Seta, Valeria
AU - Russo, Eleonora
AU - Karakas, Serpil
AU - Chabardes, Stephan
AU - Aksenova, Tetiana
AU - Charvet, Guillaume
AU - Shokur, Solaiman
AU - Micera, Silvestro
TI - Decoupling simultaneous motor imagination and execution via orthogonal ECoG neural representations
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/03/31
VL - 17
IS - 1
SP - 4662
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-71234-0
UR - https://doi.org/10.1038/s41467-026-71234-0
LA - en
ER -

CSL-JSON

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