OSCR

Neural Oscillatory Dynamics in Joint Action: Dissociable Roles of Entrainment and Beta Modulation in Self-Other Integration.

Code ↔ Paper

15 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 15 matches
  1. [1] § Methods › EEG Preprocessing ↔ Preprocessing_TapSwap_pipeline.m, lines 27–171 · score 0.87 · muscular activity, high pass filter, slow drifts, preprocessing, notch, split
  2. [2] § Methods › Generalized Eigendecomposition ↔ GED_TapSwap_pipeline_v4.m, lines 71–89 · score 0.81 · 18–22 Hz, full width, beta component, entrained component, slope, Gaussian
  3. [3] § Methods › Dyadic EEG Analyses ↔ GED_TapSwap_pipeline_v4.m, lines 210–270 · score 0.75 · GED components, power timeseries, beta components, entrained components, Hilbert, plateau
  4. [4] § Methods › Dyadic EEG Analyses ↔ BetaMod_TapSwap_pipeline_v4.m, lines 6–102 · score 0.71 · Spatial activation patterns, explained variance, beta component, spatial filter, eigenvalue, eigenspectra
  5. [5] § Methods › Generalized Eigendecomposition ↔ GED_TapSwap_pipeline_v4.m, lines 92–207 · score 0.71 · narrow band signal, broad band signal, covariance matrix, vectors, GED, channels
  6. [6] § Methods › EEG Preprocessing ↔ Preprocessing_TapSwap_pipeline.m, lines 27–171 · score 0.69 · artifact removal, preprocessing, epochs, waveguard, FieldTrip, ICA
  7. [7] § Methods › Dyadic EEG Analyses ↔ BetaMod_TapSwap_pipeline_v4.m, lines 365–441 · score 0.67 · power timeseries, phase timeseries, sinewave, Extreme, Beta modulation, bin
  8. [8] § Methods › Statistical Modeling ↔ stats_eegTapSwap_neuralEntrainment.R, lines 56–97 · score 0.64 · orthogonal polynomials, response variable, quadratic, neural entrainment, cycle, fits
  9. [9] § Methods › Dyadic EEG Analyses ↔ NeuralEntrainment_TapSwap_pipeline_v2.m, lines 156–265 · score 0.61 · Gaussian filter, instantaneous frequency, unwrapped, Hilbert, Neural entrainment, timeseries
  10. [10] § Methods › Generalized Eigendecomposition ↔ GED_TapSwap_pipeline_v4.m, lines 92–207 · score 0.59 · covariance matrices, broad band, narrow, selection, signal, GED
  11. [11] § Results › Beta Modulation ↔ stats_eegTapSwap_MPA.R, lines 143–197 · score 0.57 · beta modulation strength, Error bars, standard errors, amplitude, log, sine
  12. [12] § Methods › Dyadic EEG Analyses ↔ NeuralEntrainment_TapSwap_pipeline_v2.m, lines 69–150 · score 0.57 · Spatial activation patterns, explained variance, score, topographical, eigenspectra, channels
  13. [13] § Methods › Dyadic EEG Analyses ↔ NeuralEntrainment_TapSwap_pipeline_v2.m, lines 274–408 · score 0.57 · drifting metronomes cycle, instantaneous frequency, sub, bins, segmented, phase
  14. [14] § Results › Neural Entrainment ↔ NeuralEntrainment_TapSwap_pipeline_v2.m, lines 274–408 · score 0.57 · drifting metronomes cycle, instantaneous frequency, relative phase, Neural entrainment, SEM, Hz
  15. [15] § Methods › Generalized Eigendecomposition ↔ Preprocessing_TapSwap_pipeline.m, lines 621–666 · score 0.51 · covariance matrices, spatial filter, bias, noise, temporal

Paper

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The authors' code

MATLAB · 270 lines · 12 KB · no license · 4 matches

  1. % This script performs source separation based on generalized
  2. % eigendecomposition (GED), finding the best combination of channels to
  3. % separate a target narrow-band component from the multivariate
  4. % broad-band signal.
  6. % The GED settings are defined for separating a high-frequency component
  7. % with a plateau-shaped filter, and for separating a low-frequency
  8. % component with a Gaussian filter. The components of interest are a 'beta
  9. % component' and an 'entrained component', respectively.
  11. % Set the flag == 1 if you want to separate beta component;
  12. % Set the flag == 0 if you want to separate entrained component;
  14. % Mattia Rosso. Ghent (BE), 19/1/2024
  17. clear
  18. close all
  19. clc
  21. beta_flag = 0;
  22. pca_flag = 0;
  25. %% Design settings
  27. dyads_eeg = [1:6 8:14 16:20]; % not all dyads had eeg recording; use this vector in for loops
  28. ndyads = length(dyads_eeg);
  29. nsubs = 2;
  30. nconds = 4;
  31. ncycles = 10;
  32. nchans = 64;
  33. nsteps = 64; % Number of chosen metronome's steps
  34. condlabels = {'Allocentric - Other','Egocentric - Self' , 'Egocentric - Other' , 'Allocentric - Self'};
  36. % Set main path (concatenate strings for subdirectories)
  37. path_home = '/Users/mattiaipem/Desktop/TEMP_TapSwap_eeg';
  38. cd(path_home)
  39. % Import behavioural data, for segmentation and for CFC
  40. load tapping_TapSwap_processed.mat
  42. % ROIs selection
  43. % chans2keep = chanlocs;
  44. chans2keep = {'T7','C3','Cz','C4','T8','CP5','CP1','CP2','CP6','P7','P3','Pz','P4',...
  45. 'P8', 'POz','O1','Oz','O2','C5','C1','C2','C6','CP3','CPz','CP4','P5',...
  46. 'P1','P2','P6','PO5','PO3','PO4','PO6','TP7','TP8','PO7','PO8' }; % centro-posterior
  48. % chans2keep = {'Fp1','Fpz','Fp2','F7','F3' ,'Fz','F4','F8','FC5','FC1','FC2','FC6', ...
  49. % 'AF7','AF3','AF4','F5','F1','F2','F6','FC3','FCz','FC4'}; % centro-frontal
  50. %
  52. %Initializion for GED stuff (dyads,partners)
  54. % Define how many eigenvalues you want to carry over to next blocls
  55. nevals2keep = 10; % set a low amount to prevent going out of memory
  57. % components time series and spectra
  58. [gedComp,gedSpctr,gedSnr,gedPow] = deal( cell(ndyads,2,nconds) );
  59. % covariance matrices (higher-level structure; cell content will be initialized later)
  60. covS = cell(ndyads,2,nconds);
  61. covR = cell(ndyads,2,nconds);
  62. % and average
  63. covS_avg = cell(ndyads,2,nconds);
  64. covR_avg = cell(ndyads,2,nconds);
  65. % GED
  66. gedMap = zeros(ndyads,2,nconds,nchans,nevals2keep); %forward model, for scalp topography; always full size! (64)
  67. ged_evals = zeros(ndyads,2,nconds,nevals2keep); %eigen values
  68. ged_evecs = zeros(ndyads,2,nconds,nevals2keep,nevals2keep); %eigen vectors
  71. %% GED parameters
  73. % Parameters are defined for
  74. % - entrained component
  75. % - beta component
  76. % In the next block, one of the two approaches is chosen
  78. % Gaussian filter
  79. stimfrex = [1.667 1.641]; % average of the two metronomes' frequencies
  80. fwhm = .3; % full-width at half the maximum
  82. % Plateau filter
  83. frange = [18 22]; %frequency range
  84. trans_width = .15; %percentage for transition zone (slope cut-off)
  85. filt_ord = 20; %filter order %NB I used 20 for beta band... why so?
  86. % shrinkage proportion
  87. shr = .01;
  88. % time window for covarince matrix
  89. timewin = [-300 300];
  92. %% Apply source separation
  94. % Re-arrange tapping events in cell
  95. tidx = cell(ndyads,2,nconds);
  96. for dyadi = dyads_eeg
  98. % Load eeg data
  99. load (['TapSwap_' num2str(dyadi) '_eeg.mat']); %the order of conditions is already standardized 1234 for all subjects (maybe double-check in preprocessing script)
  102. for condi = 1:nconds
  103. % Assign timestamps, excluding the very extremes (occasionally, they are 'artificial')
  104. tidx{dyadi,1,condi} = onset_sub1{dyadi,condi}(2:end-1);
  105. tidx{dyadi,2,condi} = onset_sub2{dyadi,condi}(2:end-1);
  107. end
  109. % Time and frequency vectors
  110. % Prepare long time vector
  111. time = eegTime; % for concatenated trial
  112. % number of time points in filter
  113. pnts = length(time);
  114. frexres = 1/max(time); %Rayleigh frequency
  115. % FFT parameters
  116. nfft = ceil( srate/frexres );
  117. hz = linspace(0,srate,nfft); %vector of frequencies; shortcut: all the frex above nyquist are not valid
  119. for subi = 1:nsubs
  121. % Assign channel locations
  122. chanlocs = eegChan{subi,condi};
  123. % Get indexes of channels to include in cov matrix
  124. roi_logic = cellfun(@(c)strcmp(c,chanlocs),chans2keep,'UniformOutput',false);
  125. roi_idx = zeros(length(roi_logic),1);
  126. for i = 1:length(roi_logic)
  127. if find(roi_logic{i}) >= 1 % correction ad-hoc for dyad4
  128. roi_idx(i) = find(roi_logic{i});
  129. end
  130. end
  131. roi_idx = roi_idx(roi_idx~=0); % correction ad-hoc for dyad4
  133. for condi = 1:nconds
  135. %if length(eegChan{subi}) == 64 %needs to match size of chanlocs.... SOLVE preprocessing when it's not the case: now I can interpolate anything
  136. disp(['GED: dyad ' num2str(dyadi) '. Subject ' num2str(subi) '. Condition: ' condlabels{condi}])
  138. %temporary variable for time index
  139. tempt = tidx{dyadi,subi,condi};
  141. %initialize covariance matrix with 3rd dimension (based on N segments)
  142. covS{dyadi,subi,condi} = zeros(length(roi_idx),length(roi_idx),length(tempt));
  143. covR{dyadi,subi,condi} = zeros(length(roi_idx),length(roi_idx),length(tempt));
  144. %distance from grand-average (temporary variable)
  145. covS_dist = zeros(length(tempt),1);
  146. covR_dist = zeros(length(tempt),1);
  148. %Assign trials selection to temporary variable
  149. broad_long = eegData{subi,condi}(roi_idx,:); %broadband signal
  150. % Narrow band signal (the filter is based on the selection at the start of this block)
  151. if beta_flag == 1
  152. narrow_long = filter_plateau (broad_long, srate, frange, trans_width, filt_ord, 0); %Plateau-shaped filter
  153. %narrow_long = filterFGx(broad_long, srate, mean(frange), abs(diff(frange)), 0);
  154. else
  155. narrow_long = filterFGx(broad_long, srate, stimfrex(subi), fwhm, 0); % gaussian filter
  156. end
  158. % Compute individual covariance matrices
  159. for ti = 1:length(tempt)-1
  161. % S covariance matrix
  162. data = narrow_long(:,(timewin(1)+tempt(ti)):(timewin(end)+tempt(ti))); % segment and assign time-window for covariance matrix
  163. covS{dyadi,subi,condi}(:,:,ti) = cov(data'); % compute covariance matrix
  165. % R covariance matricex
  166. data = broad_long(:,(timewin(1)+tempt(ti)):(timewin(end)+tempt(ti)-1)); % assign broad-band segment to input data
  167. % ... and reshape
  168. %data = reshape(data,length(eegChan{subi}),[],1);
  169. covR{dyadi,subi,condi}(:,:,ti) = cov(data'); % compute covariance matrix
  170. % Apply shrinkage to covR (optional)
  171. covR{dyadi,subi,condi}(:,:,ti) = (1-shr)*covR{dyadi,subi,condi}(:,:,ti)...
  172. + shr*mean(eig(covR{dyadi,subi,condi}(:,:,ti)))*eye(size(covR{dyadi,subi,condi}(:,:,ti)));
  174. end
  176. % Grand-Average matrices
  177. %Compute average covariance matrices
  178. covS_avg{dyadi,subi,condi} = squeeze(mean(covS{dyadi,subi,condi} , 3)); %along 3D dimension
  179. covR_avg{dyadi,subi,condi} = squeeze(mean(covR{dyadi,subi,condi} , 3));
  181. %Remove 'bad' matrices based on distance
  182. for ti = 1:length(tempt)
  183. %Compute distance
  184. covS_dist(ti) = sqrt(trace(covS{dyadi,subi,condi}(:,:,ti)*covS_avg{dyadi,subi,condi}));
  185. covR_dist(ti) = sqrt(trace(covR{dyadi,subi,condi}(:,:,ti)*covR_avg{dyadi,subi,condi}));
  186. end
  188. %Normalize distance (temporary variable)
  189. covS_z = (covS_dist - mean(covS_dist)) / std(covS_dist);
  190. covR_z = (covR_dist - mean(covR_dist)) / std(covR_dist);
  191. z_thresh = 2.3; %~.01
  193. %find indexes outliers
  194. toofarS = abs(covS_z) > z_thresh;
  195. toofarR = abs(covR_z) > z_thresh;
  197. %remove distances just for visualization, using logical indexing
  198. covS_z(toofarS) = NaN;
  199. covR_z(toofarR) = NaN;
  201. %Remove actual outlier matrices, using logical indexing
  202. covS{dyadi,subi,condi}(:,:,toofarS) = NaN;
  203. covR{dyadi,subi,condi}(:,:,toofarR) = NaN;
  205. %Re-compute grand-averages (whatch out the NaNs)
  206. covS_avg{dyadi,subi,condi} = squeeze(mean(covS{dyadi,subi,condi} , 3 , 'omitnan')); %along 3D dimension
  207. covR_avg{dyadi,subi,condi} = squeeze(mean(covR{dyadi,subi,condi} , 3 , 'omitnan'));
  210. %% Generalized Eigendecomposition (GED)
  212. % Initialize to store all components
  213. [gedComp{dyadi,subi,condi},gedSpctr{dyadi,subi,condi},gedPow{dyadi,subi,condi}] = deal( zeros(nevals2keep,size(broad_long,2)) );
  215. % Compute GED
  216. [tempvecs,tempvals] = eig(covS_avg{dyadi,subi,condi},covR_avg{dyadi,subi,condi}); %assign to temporary vars
  217. [tempvals,sidx] = sort(diag(tempvals),'descend'); %sort components
  218. tempvecs = tempvecs(:,sidx); %vectors of weights, for weighted average
  219. tempvals = 100*tempvals/sum(tempvals); %express in percentage of explained variance (for eigenspectrum)
  220. nevals = length(tempvals); % for loop iterations
  222. for evi = 1:nevals2keep % only for the evals to carry over
  224. disp(['Eval #' num2str(evi)])
  226. % Pick the component by ranking of associated eigenvalue
  227. evalidx = evi; % this leaves the flexibility to hard-code
  229. % Compute filter forward model and flip sign
  230. gedMap(dyadi,subi,condi,roi_idx,evi) = tempvecs(:,evalidx)'*covS_avg{dyadi,subi,condi};
  231. [~,maxchan] = max(abs(gedMap(dyadi,subi,condi,:,evi)));
  232. gedMap(dyadi,subi,condi,:,evi) = gedMap(dyadi,subi,condi,:,evi)*sign(gedMap(dyadi,subi,condi,maxchan,evi));
  234. % Compute component time series (i.e., apply spatial filter)
  235. gedComp{dyadi,subi,condi}(evalidx,:) = tempvecs(:,evalidx)'*reshape(broad_long,length(roi_idx),[]);
  237. % Power spectrum averaged over trials
  238. gedSpctr{dyadi,subi,condi}(evalidx,:) = abs(fft(gedComp{dyadi,subi,condi}(evalidx,:)')).^2;
  240. % Power timeseries of the GED components
  241. temp_pow = filter_plateau(gedComp{dyadi,subi,condi}(evalidx,:), srate, frange, trans_width, filt_ord, 0)'; %Plateau-shaped filter
  242. temp_pow = abs(hilbert(temp_pow)).^2;
  243. gedPow{dyadi,subi,condi}(evalidx,:) = temp_pow'; % assignment of temporary variables
  246. end
  247. % Store temporary variables
  248. ged_evals(dyadi,subi,condi,:) = tempvals(1:nevals2keep);
  249. ged_evecs(dyadi,subi,condi,:,:) = tempvecs(1:nevals2keep,1:nevals2keep);
  252. clc
  254. end
  256. end
  258. end
  261. %clear EEG structure for topoplot and temp variable
  262. clear data
  263. clear EEG
  266. if beta_flag == 1
  267. save('TapSwap_Beta_Component.mat', 'gedComp', 'gedMap', 'gedPow', 'ged_evals', 'ged_evecs', '-v7.3')
  268. else
  269. save('TapSwap_Entrained_Component.mat', 'gedComp', 'gedMap', 'gedPow', 'ged_evals', 'ged_evecs', '-v7.3')
  270. end

GED_TapSwap_pipeline_v4.m at commit 61873a7, no license · at the source

Overview

Authors: Mattia Rosso1,2, Bavo Van Kerrebroeck2,3,4, Peter Erik Keller1,5, Marc Leman2, Pieter‐Jan Maes2, Peter Vuust1
ORCID iDs: Peter Erik Keller
  1. Center for Music in the Brain, Department of Clinical Medicine Aarhus University & The Royal Academy of Music Aarhus/Aalborg Denmark
  2. IPEM – Institute for Psychoacoustics and Electronic Music Ghent University Ghent Belgium
  3. SPL – Sequence Production Lab McGill University Montreal Canada
  4. IDMIL – Input Devices and Music Interaction Laboratory McGill University Montreal Canada
  5. MARCS Institute for Brain, Behaviour and Development Western Sydney University Sydney Australia
Institutions: Aarhus University (Denmark); Ghent University (Belgium); Royal Academy of Music (Denmark); McGill University (Canada); Western Sydney University (Australia)
Journal: Annals of the New York Academy of Sciences, volume 1561, issue 1, article e70314
Dates: received 9 September 2025; accepted 21 April 2026; published online 8 July 2026; in print July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/nyas.70314 · PMID 42418244 · PMCID PMC13344869 · OpenAlex W7167731345
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Smoothing, state filtering, decompositions, Connectivity, Statistics, fMRI & imaging, Physiology & signal measures
Keywords: beta modulation, electroencephalography (EEG), hyperscanning, interpersonal synchronization, neural entrainment, oscillations, self–other integration
MeSH: Beta Rhythm*, Brain*, Psychomotor Performance*, Adult, Electroencephalography, Female, Humans, Male, Young Adult (* major topic)
Topic: Action Observation and Synchronization (Social Psychology, Psychology), according to OpenAlex
Funding: Bijzonder Onderzoeksfonds (01D21819); Danmarks Grundforskningsfond (Danish National Research Foundation) (DNRF117)
Citations: cited by 2 papers (Europe PMC); 123 references in the paper

Abstract

Temporal coordination is fundamental for human communication and collaboration, yet the underlying neural mechanisms remain poorly understood. Central to this process is self–other integration, defined here as the extent to which a partner is processed as self‐relevant and incorporated into one's sensorimotor representations. Recent evidence suggests that beta‐band oscillatory dynamics may provide a shared sensorimotor framework supporting such integration. Here, we leveraged an immersive virtual‐reality body‐swap illusion to experimentally manipulate the embodiment of a partner's hand during joint rhythmic action, thereby testing the sensitivity of oscillatory brain dynamics to distinct levels of self–other integration. Forty participants, paired into 20 dyads, performed a finger‐tapping task while viewing either their partner's hand in first‐person (1P) or second‐person (2P) perspective, or their own hand in uncoupled control conditions. Electroencephalography hyperscanning demonstrated that both neural entrainment of low‐frequency oscillations and beta modulation linked to partner‐generated movements occurred in visually coupled conditions. However, only beta modulation was selectively enhanced when participants perceived their partner's hand from a 1P perspective. These findings suggest that while neural entrainment reflects a general mechanism for tracking a partner's rhythmic behavior, beta modulation specifically supports the integration of the other's effector into one's bodily representation.

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 15 matches between paragraphs and lines of code.

mattiaRosso92/Oscillatory_Dynamics_Joint_Action

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 61873a733aa113f606734752552e66fdb73ffa63, 23 April 2025
Languages: MATLAB (4), R (2)
Size: 6 files, 6 scripts
Software Heritage: not archived
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: broom (2 files), ggplot2 (2 files), lme4 (2 files), Signal Processing Toolbox (2 files), psych (2 files), reshape2 (2 files), tidyverse (2 files), FieldTrip (1 file), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
6 files
At the source: github.com/mattiaRosso92/Oscillatory_Dynamics_Joint_Action

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;
  • 6 scripts, each with its path and the digest of its content;
  • 15 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 preprocessed data that support the findings of this study are openly available on Zenodo: https://zenodo.org/records/20492602. All analysis scripts are publicly available at: https://github.com/mattiaRosso92/Oscillatory_Dynamics_Joint_Action. The GED implementation used here is also available in the FREQNESS toolbox, at: https://github.com/mattiaRosso92/Frequency‐resolved_brain_network_estimation_via_source_separation_FREQ‐NESS/tree/main/FREQNESS_Toolbox (https://github.com/mattiaRosso92/Frequency-resolved_brain_network_estimation_via_source_separation_FREQ-NESS/tree/main/FREQNESS_Toolbox).

Reproduced under the paper's license (CC BY), 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, 6 authors, 7 keywords, 9 MeSH terms, 2 funders, 113 references.

Cite

This paper

Rosso, M., Van Kerrebroeck, B., Keller, P. E., Leman, M., Maes, P., & Vuust, P. (2026). Neural Oscillatory Dynamics in Joint Action: Dissociable Roles of Entrainment and Beta Modulation in Self-Other Integration. Annals of the New York Academy of Sciences, 1561(1), e70314. https://doi.org/10.1111/nyas.70314

BibTeX

@article{rosso2026neural,
author = {Rosso, Mattia and Van Kerrebroeck, Bavo and Keller, Peter Erik and Leman, Marc and Maes, Pieter‐Jan and Vuust, Peter},
title = {{Neural Oscillatory Dynamics in Joint Action: Dissociable Roles of Entrainment and Beta Modulation in Self-Other Integration}},
journal = {Annals of the New York Academy of Sciences},
year = {2026},
month = jul,
volume = {1561},
number = {1},
pages = {e70314},
publisher = {Wiley},
issn = {0077-8923},
doi = {10.1111/nyas.70314},
url = {https://doi.org/10.1111/nyas.70314},
pmid = {42418244},
pmcid = {PMC13344869}
}

RIS

TY - JOUR
AU - Rosso, Mattia
AU - Van Kerrebroeck, Bavo
AU - Keller, Peter Erik
AU - Leman, Marc
AU - Maes, Pieter‐Jan
AU - Vuust, Peter
TI - Neural Oscillatory Dynamics in Joint Action: Dissociable Roles of Entrainment and Beta Modulation in Self-Other Integration
T2 - Annals of the New York Academy of Sciences
J2 - Ann N Y Acad Sci
PY - 2026
DA - 2026/07/01
VL - 1561
IS - 1
SP - e70314
SN - 0077-8923
PB - Wiley
DO - 10.1111/nyas.70314
UR - https://doi.org/10.1111/nyas.70314
LA - en
ER -

CSL-JSON

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"id": "10.1111/nyas.70314",
"type": "article-journal",
"title": "Neural Oscillatory Dynamics in Joint Action: Dissociable Roles of Entrainment and Beta Modulation in Self-Other Integration",
"container-title": "Annals of the New York Academy of Sciences",
"author": [
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"family": "Rosso",
"given": "Mattia"
},
{
"family": "Van Kerrebroeck",
"given": "Bavo"
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{
"family": "Keller",
"given": "Peter Erik"
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{
"family": "Leman",
"given": "Marc"
},
{
"family": "Maes",
"given": "Pieter‐Jan"
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{
"family": "Vuust",
"given": "Peter"
}
],
"container-title-short": "Ann N Y Acad Sci",
"volume": "1561",
"issue": "1",
"page": "e70314",
"DOI": "10.1111/nyas.70314",
"PMID": "42418244",
"PMCID": "PMC13344869",
"ISSN": "0077-8923",
"publisher": "Wiley",
"URL": "https://doi.org/10.1111/nyas.70314",
"language": "en",
"issued": {
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[
2026,
7,
1
]
]
}
}

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

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