OSCR

Action Intentions Shape Task- and Phase-Specific Integration of Object Features.

Code ↔ Paper

1 match 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 1 match
  1. [1] § Materials and Methods › EEG Acquisition and Preprocessing ↔ Scripts/SVMClassifyERPs.m, lines 1–87 · score 0.70 · MoveOn, EEGLAB Toolbox, preprocessing, Delorme, Makeig, movements

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 · 120 lines · 4.1 KB · no license · 1 match

  1. function SVMClassifyERPs
  3. %% Description
  4. % Decode time-resolved visual features and actions; requires preprocessed
  5. % ERP/EMG data aligned to event (Preview/stimulus onset or MoveOn/movement
  6. % onset and eeglab toolbox (Delorme and Makeig, 2004)
  8. %% Select participant files and event alignment
  9. subNum = 1:15;
  11. %% Initialization
  12. cd('/User/Scripts/toolboxes/eeglab/'); eeglab; close; % obtain eeglab functions
  14. %% Paths and Variables
  15. % Get participants
  16. nSubs = length(subNum);
  17. for i = 1:nSubs
  18. subjects{i} = ['p', num2str(subNum(i))];
  19. end
  21. % Set temporal window for concatenating time points and data path
  22. nCat = 5;
  23. ERPpath = '/User/Data/';
  25. %% Analysis Loop
  26. accurate = [];
  27. % select subjects
  28. for iSub = 1:length(subjects)
  29. disp(['Subject Number: ',num2str(iSub)]);
  30. disp('Getting ERPs...');
  31. tic
  33. %% Get ERPs
  34. load([ERPpath,subjects{iSub},'/PreprocessedERPAlignedtoPreview.mat']);
  35. %example using data aligned to Preview onset
  37. %% Concatenate data
  38. disp('concatenating data...');
  39. tic
  40. for iVar = 1:2
  41. if iVar == 1
  42. ERPdata = grasp;
  43. elseif iVar == 2
  44. ERPdata = knuckle;
  45. end
  46. fields=fieldnames(ERPdata);
  47. for categoryidx=1:length(fields)
  48. categoryname=fields{categoryidx};
  49. data = ERPdata.(categoryname);
  50. % concatenate according to nCat
  51. tempdata = []; newdata = [];
  52. for i = 1:size(data,3)
  53. tempdata = cat(2, tempdata, data(:,:,i));
  54. if mod(i,nCat) == 0
  55. newdata = cat(3, newdata, tempdata);
  56. tempdata = [];
  57. end
  58. end
  59. ERPdata.(categoryname)=newdata;
  60. end
  62. if iVar == 1
  63. grasp = ERPdata;
  64. elseif iVar == 2
  65. knuckle = ERPdata;
  66. end
  67. end
  68. toc
  70. %% Classification
  71. disp('Classifying...');
  72. tic
  74. %% Classify features separately for each movement
  75. accurate.Knuckle.Orientation(iSub,:) = ClassifyERP(cat(1,knuckle.ob1, knuckle.ob2, knuckle.ob3, knuckle.ob4),cat(1,knuckle.ob1, knuckle.ob2, knuckle.ob3, knuckle.ob4));
  76. accurate.Knuckle.Size(iSub,:) = ClassifyERP(cat(1,knuckle.ob1, knuckle.ob3, knuckle.ob2, knuckle.ob4),cat(1,knuckle.ob1, knuckle.ob3, knuckle.ob2, knuckle.ob4));
  77. accurate.Grasp.Orientation(iSub,:) = ClassifyERP(cat(1,grasp.ob1, grasp.ob2, grasp.ob3, grasp.ob4),cat(1,grasp.ob1, grasp.ob2, grasp.ob3, grasp.ob4));
  78. accurate.Grasp.Size(iSub,:) = ClassifyERP(cat(1,grasp.ob1, grasp.ob3, grasp.ob2, grasp.ob4),cat(1,grasp.ob1, grasp.ob3, grasp.ob2, grasp.ob4));
  79. toc
  81. %% save results
  82. savepath = '/User/Analyzed/Preview/';
  84. cd(savepath);
  85. save ClassifyERP_Preview.mat -mat accurate MyInfo
  86. end % subs
  87. end
  90. function [acc] = ClassifyERP(ERP1, ERP2)
  91. %% Description
  92. % Classifies binary categories in ERP. ERP should be organized in
  93. % nObservations x nFeatures x nTimepoints
  94. % Observations should to organized such that across nColumns,
  95. % ERP(1:nColumns/2, :, :) should be observations of category 1, and
  96. % ERP(nColumns/2+1, :, :) should be observations of category 2. ERP1 and
  97. % ERP2 should have the same structure, and so cross-decoding is possible if
  98. % ERP1 and ERP2 are different data. Otherwise, ERP1 and ERP2 should be the
  99. % same data.
  100. nFold = size(ERP1,1)/2;
  101. grouplabels = [ones(nFold,1); ones(nFold,1).*(-1)];
  102. nTimes = size(ERP1, 3);
  103. cvlabels = [1:nFold, 1:nFold];
  104. % Train & Classify at each timepoint
  105. for itime = 1:nTimes
  106. parfor icv = 1:nFold
  107. trainfold = ERP1(cvlabels~=icv, :, itime);
  108. testfold = ERP2(cvlabels==icv, :, itime);
  110. trainlabel = grouplabels(cvlabels~=icv);
  111. testlabel = grouplabels(cvlabels==icv);
  113. model = svmtrain(trainlabel, double(trainfold), sprintf('-q -t 0 -c %f', 1));
  114. [predictedlabels] = svmpredict(testlabel, double(testfold), model, '-q');
  115. iAcc(icv) = sum(predictedlabels==testlabel)/length(predictedlabels);
  117. end
  118. acc(itime) = mean(iAcc);
  119. end
  120. end

SVMClassifyERPs.m, no license · at the source

Overview

Authors: Nina Lee1, Matthias Niemeier1,2
ORCID iDs: Matthias Niemeier
  1. Department of Psychology at Scarborough University of Toronto Scarborough Ontario Canada
  2. Centre for Vision Research York University Toronto Ontario Canada
Institutions: University of Toronto Scarborough (Canada); York University (Canada)
Journal: The European journal of neuroscience, volume 64, issue 2, article e70639
Dates: received 3 July 2025; accepted 2 July 2026; published online 21 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1111/ejn.70639 · PMID 42478278 · PMCID PMC13386189 · OpenAlex W7169842091
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Machine learning, Evoked potentials, Physiology & signal measures
Keywords: EEG, feature integration, grasping, machine learning, object‐based attention
MeSH: Attention*, Brain*, Intention*, Psychomotor Performance*, Adult, Electroencephalography, Female, Hand Strength, Humans, Male, Young Adult (* major topic)
Topic: Motor Control and Adaptation (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Natural Sciences and Engineering Research Council of Canada (RGPIN‐2020‐06018)
Citations: cited by 1 paper (Europe PMC); 42 references in the paper

Abstract

Theories of object‐based attention suggest that attending to an object binds its features together. Yet, there is a growing body of work to suggest that the intention to grasp an object can alter the representation of features such that they are separately represented during different stages of motor planning and execution, whereas some object features such as shape and size might form integrated representations when afforded by motor control. However, it remains untested whether these features were integrated as an outcome of the requirements of grasping motor control, or due to attention towards the object in general. Therefore, here we investigated how task‐relevancy modulates the integration of grasp‐relevant object features. To this end, we recorded electroencephalography while human participants grasped or reached for objects that varied in their orientation and size. Using multivariate analyses, we found a superadditive integration of object orientation and size during action planning for grasping but not reaching. These integrated representations likely facilitated the calculation of stable grasp points as further evidenced by the representations of grasp‐specific visual size and grip size emerging at similar times. Our results provide novel insights into the vital role of action intention on cognitive representations in the human brain.

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

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

OSF 6fq9h

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Languages: MATLAB (1)
Size: 26 files, 1 script
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
1 file
At the source: doi.org/10.17605/OSF.IO/6FQ9H

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;
  • 1 script, each with its path and the digest of its content;
  • 1 match 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 Statement

The data supporting the findings of this study are openly available on Open Science Framework (OSF) at https://doi.org/10.17605/OSF.IO/6FQ9H, including preprocessed EEG/ERP data, behavioural data, and scripts necessary to reproduce the main analyses.

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

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 2, 28 September 2026

  • Publisher: n/a → Wiley

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 5 keywords, 11 MeSH terms, 1 funder, 40 references.

Cite

This paper

Lee, N., & Niemeier, M. (2026). Action Intentions Shape Task- and Phase-Specific Integration of Object Features. The European journal of neuroscience, 64(2), e70639. https://doi.org/10.1111/ejn.70639

BibTeX

@article{lee2026action,
author = {Lee, Nina and Niemeier, Matthias},
title = {{Action Intentions Shape Task- and Phase-Specific Integration of Object Features}},
journal = {The European journal of neuroscience},
year = {2026},
month = jul,
volume = {64},
number = {2},
pages = {e70639},
publisher = {Wiley},
issn = {0953-816X},
doi = {10.1111/ejn.70639},
url = {https://doi.org/10.1111/ejn.70639},
pmid = {42478278},
pmcid = {PMC13386189}
}

RIS

TY - JOUR
AU - Lee, Nina
AU - Niemeier, Matthias
TI - Action Intentions Shape Task- and Phase-Specific Integration of Object Features
T2 - The European journal of neuroscience
J2 - Eur J Neurosci
PY - 2026
DA - 2026/07/01
VL - 64
IS - 2
SP - e70639
SN - 0953-816X
PB - Wiley
DO - 10.1111/ejn.70639
UR - https://doi.org/10.1111/ejn.70639
LA - en
ER -

CSL-JSON

{
"id": "10.1111/ejn.70639",
"type": "article-journal",
"title": "Action Intentions Shape Task- and Phase-Specific Integration of Object Features",
"container-title": "The European journal of neuroscience",
"author": [
{
"family": "Lee",
"given": "Nina"
},
{
"family": "Niemeier",
"given": "Matthias"
}
],
"container-title-short": "Eur J Neurosci",
"volume": "64",
"issue": "2",
"page": "e70639",
"DOI": "10.1111/ejn.70639",
"PMID": "42478278",
"PMCID": "PMC13386189",
"ISSN": "0953-816X",
"publisher": "Wiley",
"URL": "https://doi.org/10.1111/ejn.70639",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
1
]
]
}
}

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

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1371/journal.pcbi.1014435 [code]
Predictive coding explains asymmetric connectivity in the brain: A neural network study.
Journal: PLoS computational biology
In common: 1 reference, author Matthias Niemeier
[2] doi:10.1016/j.neuroimage.2026.122004
Preventing visual feedback of the moving limb during grasping preferentially activates parietofrontal premotor areas.
Journal: NeuroImage
In common: 4 references
[3] doi:10.1111/ejn.70521 [code]
Multivariate Pattern Analysis Identifies Potential Intertrial Resting-State EEG Biomarkers in Fibromyalgia.
Journal: The European journal of neuroscience
In common: EEGLAB, EEG, 3 references
[4] doi:10.1523/jneurosci.0154-26.2026 [code]
Faster but less precise: expectation enhances response speed while reducing sensory fidelity.
Journal: The Journal of neuroscience : the official journal of the Society for Neuroscience
In common: EEGLAB, EEG, cognitive, 2 references
[5] doi:10.1038/s41598-026-47785-z [code]
Modulations of the P3b effect as a function of bilingual language experience.
Journal: Scientific reports
In common: EEGLAB, EEG, cognitive, 2 references
[6] doi:10.1371/journal.pone.0353990 [code]
Positive mood enhances accessibility of unrelated concepts in the first language but not in the foreign language.
Journal: PloS one
In common: EEGLAB, EEG, cognitive, 2 references
[7] doi:10.1016/j.ibneur.2026.05.013 [code]
Anticipatory slow potentials before auditory feedback show posterior predominance but limited condition effects in speech-in-noise.
Journal: IBRO neuroscience reports
In common: EEGLAB, EEG, cognitive, 2 references
[8] doi:10.1162/imag.a.105 [code]
Right posterior theta reflects human parahippocampal phase resetting by salient cues during goal-directed navigation
Journal: n/a
In common: EEGLAB, EEG, cognitive, 2 references
[9] doi:10.1523/eneuro.0076-26.2026 [code]
Exogenously Driven Neural Reactivation of Spatially Matching Visual Working-Memory Contents.
Journal: eNeuro
In common: EEG, cognitive, 2 references
[10] doi:10.1162/opmi.a.372 [code]
Broadening the Agent Preference Hypothesis Through Experiencers: Eye-Tracking and EEG Evidence of Proto-Agents and Proto-Patients.
Journal: Open mind : discoveries in cognitive science
In common: EEGLAB, EEG, 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.