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Development of auditory and spontaneous movement responses to music over the first postnatal year.

Code ↔ Paper

6 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 6 matches · 2 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Results › EEG: Event-related potentials (ERP) ↔ Step4_tinydancers_grouperp.m, lines 455–508 · score 0.87 · 104–227 ms, 116–284 ms, 177–305 ms, 307–325 ms, 104 ms, 116 ms
  2. [2] § Materials and methods › Data processing › EEG pre-processing ↔ functions/RFT_IClabel.m, the whole file · a weak match · score 0.84 · IClabel, interpolated channels, EEGLAB, classified, eye, rejected
  3. [3] § Materials and methods › Data processing › EEG pre-processing ↔ functions/RFT_clean_asr.m, lines 1–60 · score 0.82 · pass filtered, standard deviation, reconstruction, subspace, band, ASR
  4. [4] § Materials and methods › Data processing › Video data pre-processing ↔ R/MUSICOM_kinematics.R, lines 207–267 · score 0.76 · left elbow, right elbow, left knee, right knee, head, DeepLabCut
  5. [5] § Materials and methods › Data analyses › EEG: Auditory steady state response (ASSR) analysis ↔ functions/Trinh_freq_normalize.m, the whole file · a weak match · score 0.67 · neighbouring frequency bins, background noise, subtracting, amplitude
  6. [6] § Materials and methods › Data analyses › EEG: Event-related potentials (ERP) analysis ↔ functions/RFT_clean_asr.m, lines 1–60 · score 0.67 · EEG signal, standard deviation, contaminated, artifacts, threshold, event

Paper

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The authors' code

MATLAB · 197 lines · 12 KB · CC0-1.0 · 2 matches

  1. function [signal, ref_section] = RFT_clean_asr(signal,cutoff,windowlen,stepsize,maxdims,ref_maxbadchannels,ref_tolerances,ref_wndlen,usegpu,useriemannian,maxmem)
  2. % Run the ASR method on some high-pass filtered recording.
  3. % Signal = clean_asr(Signal,StandardDevCutoff,WindowLength,BlockSize,MaxDimensions,ReferenceMaxBadChannels,RefTolerances,ReferenceWindowLength,UseGPU,UseRiemannian,MaxMem)
  4. %
  5. % This is an automated artifact rejection function that ensures that the data contains no events
  6. % that have abnormally strong power; the subspaces on which those events occur are reconstructed
  7. % (interpolated) based on the rest of the EEG signal during these time periods.
  8. %
  9. % The basic principle is to first find a section of data that represents clean "reference" EEG and
  10. % to compute statistics on there. Then, the function goes over the whole data in a sliding window
  11. % and finds the subspaces in which there is activity that is more than a few standard deviations
  12. % away from the reference EEG (this threshold is a tunable parameter). Once the function has found
  13. % the bad subspaces it will treat them as missing data and reconstruct their content using a mixing
  14. % matrix that was calculated on the clean data.
  15. %
  16. % Notes:
  17. % This function by default attempts to use the Statistics toolbox in order to automatically
  18. % extract calibration data for use by ASR from the given recording. This step is automatically
  19. % skipped if no Statistics toolbox is present (then the entire recording will be used for
  20. % calibration, which is fine for mildly contaminated data -- see ReferenceMaxBadChannels below).
  21. %
  22. % In:
  23. % Signal : continuous data set, assumed to be *zero mean*, e.g., appropriately high-passed (e.g.
  24. % >0.5Hz or with a 0.5Hz - 1.0Hz transition band)
  25. %
  26. % Cutoff : Standard deviation cutoff for removal of bursts (via ASR). Data portions whose variance
  27. % is larger than this threshold relative to the calibration data are considered missing
  28. % data and will be removed. The most aggressive value that can be used without losing
  29. % much EEG is 3. For new users it is recommended to at first visually inspect the difference
  30. % between the original and cleaned data to get a sense of the removed content at various
  31. % levels. An aggressive value is 5, and conservative value is 20. Default: 5.
  32. %
  33. % The following are detail parameters that usually do not have to be tuned. If you cannot get
  34. % the function to do what you want, you might consider adapting these better to your data.
  35. %
  36. % WindowLength : Length of the statistcs window, in seconds. This should not be much longer
  37. % than the time scale over which artifacts persist, but the number of samples in
  38. % the window should not be smaller than 1.5x the number of channels. Default:
  39. % max(0.5,1.5*Signal.nbchan/Signal.srate);
  40. %
  41. % StepSize : Step size for processing. The reprojection matrix will be updated every this many
  42. % samples and a blended matrix is used for the in-between samples. If empty this will
  43. % be set the WindowLength/2 in samples. Default: []
  44. %
  45. % MaxDimensions : Maximum dimensionality to reconstruct. Up to this many dimensions (or up to this
  46. % fraction of dimensions) can be reconstructed for a given data segment. This is
  47. % since the lower eigenvalues are usually not estimated very well. Default: 2/3.
  48. %
  49. % ReferenceMaxBadChannels : If a number is passed in here, the ASR method will be calibrated based
  50. % on sufficiently clean data that is extracted first from the recording
  51. % that is then processed with ASR. This number is the maximum tolerated
  52. % fraction of "bad" channels within a given time window of the recording
  53. % that is considered acceptable for use as calibration data. Any data
  54. % windows within the tolerance range are then used for calibrating the
  55. % threshold statistics. Instead of a number one may also directly pass
  56. % in a data set that contains calibration data (for example a minute of
  57. % resting EEG) or the name of a data set in the workspace.
  58. %
  59. % If this is set to 'off', all data is used for calibration. This will
  60. % work as long as the fraction of contaminated data is lower than the
  61. % the breakdown point of the robust statistics in the ASR calibration
  62. % (50%, where 30% of clearly recognizable artifacts is a better estimate
  63. % of the practical breakdown point).
  64. %
  65. % A lower value makes this criterion more aggressive. Reasonable range:
  66. % 0.05 (very aggressive) to 0.3 (quite lax). If you have lots of little
  67. % glitches in a few channels that don't get entirely cleaned you might
  68. % want to reduce this number so that they don't go into the calibration
  69. % data. Default: 0.075.
  70. %
  71. %
  72. % ReferenceTolerances : These are the power tolerances outside of which a channel in a
  73. % given time window is considered "bad", in standard deviations relative to
  74. % a robust EEG power distribution (lower and upper bound). Together with the
  75. % previous parameter this determines how ASR calibration data is be
  76. % extracted from a recording. Can also be specified as 'off' to achieve the
  77. % same effect as in the previous parameter. Default: [-3.5 5.5].
  78. %
  79. % ReferenceWindowLength : Granularity at which EEG time windows are extracted
  80. % for calibration purposes, in seconds. Default: 1.
  81. %
  82. % UseRiemannian : [true|false] Use Riemannian distance instead of Euclidian distance.
  83. % Riemannian distance used the modication in the following publication
  84. % Blum Sarah, Jacobsen Nadine S. J., Bleichner Martin G., Debener Stefan (2019)
  85. % A Riemannian Modification of Artifact Subspace Reconstruction for EEG Artifact
  86. % Handling, Frontiers in Human Neuroscience, 13, 141. DOI=10.3389/fnhum.2019.00141.
  87. %
  88. % MaxMem : Amount of memory to use. See asr_process for more information.
  89. %
  90. % UseGPU : Whether to run on the GPU. This makes sense for offline processing if you have a a card with
  91. % enough memory and good double-precision performance (e.g., NVIDIA GTX Titan or K20).
  92. % Note that for this to work you need to a) have the Parallel Computing toolbox and b) remove
  93. % the dummy gather.m file from the path. Default: false
  94. %
  95. % Out:
  96. % Signal : data set with local peaks removed
  97. %
  98. % Examples:
  99. % % use the defaults
  100. % eeg = clean_asr(eeg);
  101. %
  102. % % use a more aggressive threshold
  103. % eeg = clean_asr(eeg,2.5);
  104. %
  105. % % disable subset selection of calibration data (use all data instead)
  106. % eeg = clean_asr(eeg,[],[],[],[],'off');
  107. %
  108. % % use a custom calibration measurement (e.g., EEGLAB dataset containing a baseline recording)
  109. % eeg = clean_asr(eeg,[],[],[],[],mybaseline);
  110. %
  111. % Christian Kothe, Swartz Center for Computational Neuroscience, UCSD
  112. % 2012-10-15
  114. % Copyright (C) Christian Kothe, SCCN, 2012, [email hidden]
  115. %
  116. % This program is free software; you can redistribute it and/or modify it under the terms of the GNU
  117. % General Public License as published by the Free Software Foundation; either version 2 of the
  118. % License, or (at your option) any later version.
  119. %
  120. % This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without
  121. % even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
  122. % General Public License for more details.
  123. %
  124. % You should have received a copy of the GNU General Public License along with this program; if not,
  125. % write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307
  126. % USA
  128. if ~exist('cutoff','var') || isempty(cutoff) cutoff = 5; end
  129. if ~exist('windowlen','var') || isempty(windowlen) windowlen = max(0.5,1.5*signal.nbchan/signal.srate); end
  130. if ~exist('stepsize','var') || isempty(stepsize) stepsize = []; end
  131. if ~exist('maxdims','var') || isempty(maxdims) maxdims = 0.66; end
  132. if ~exist('ref_maxbadchannels','var') || isempty(ref_maxbadchannels) ref_maxbadchannels = 0.075; end
  133. if ~exist('ref_tolerances','var') || isempty(ref_tolerances) ref_tolerances = [-3.5 5.5]; end
  134. if ~exist('ref_wndlen','var') || isempty(ref_wndlen) ref_wndlen = 1; end
  135. if ~exist('usegpu','var') || isempty(usegpu) usegpu = false; end
  136. if ~exist('maxmem','var') || isempty(maxmem) maxmem = 64; end
  137. if ~exist('useriemannian','var') || isempty(useriemannian) useriemannian = false; end
  139. signal.data = double(signal.data);
  141. % first determine the reference (calibration) data
  142. if isnumeric(ref_maxbadchannels) && isnumeric(ref_tolerances) && isnumeric(ref_wndlen)
  143. disp('Finding a clean section of the data...');
  144. try
  145. ref_section = clean_windows(signal,ref_maxbadchannels,ref_tolerances,ref_wndlen);
  146. catch e
  147. disp('An error occurred while trying to identify a subset of clean calibration data from the recording.');
  148. disp('If this is because do not have EEGLAB loaded or no Statistics toolbox, you can generally');
  149. disp('skip this step by passing in ''off'' as the ReferenceMaxBadChannels parameter.');
  150. disp('Error details: ');
  151. hlp_handleerror(e,1);
  152. disp('Falling back to using the entire data for calibration.')
  153. ref_section = signal;
  154. end
  155. elseif strcmp(ref_maxbadchannels,'off') || strcmp(ref_tolerances,'off') || strcmp(ref_wndlen,'off')
  156. disp('Using the entire data for calibration (reference parameters set to ''off'').')
  157. ref_section = signal;
  158. elseif ischar(ref_maxbadchannels) && isvarname(ref_maxbadchannels)
  159. disp('Using a user-supplied data set in the workspace.');
  160. ref_section = evalin('base',ref_maxbadchannels);
  161. elseif all(isfield(ref_maxbadchannels,{'data','srate','chanlocs'}))
  162. disp('Using a user-supplied clean section of data.');
  163. ref_section = ref_maxbadchannels;
  164. else
  165. error('Unsupported value for argument ref_maxbadchannels.');
  166. end
  168. % calibrate on the reference data
  169. disp('Estimating calibration statistics; this may take a while...');
  170. if exist('hlp_diskcache','file')
  171. if useriemannian
  172. state = hlp_diskcache('filterdesign',@asr_calibrate_r,ref_section.data,ref_section.srate,cutoff);
  173. else
  174. state = hlp_diskcache('filterdesign',@asr_calibrate,ref_section.data,ref_section.srate,cutoff);
  175. end
  176. else
  177. if useriemannian
  178. state = asr_calibrate_r(ref_section.data,ref_section.srate,cutoff, [], [], [], [], [], [], [], maxmem);
  179. else
  180. state = asr_calibrate(ref_section.data,ref_section.srate,cutoff, [], [], [], [], [], [], [], maxmem);
  181. end
  182. end
  183. %clear ref_section;
  185. if isempty(stepsize)
  186. stepsize = floor(signal.srate*windowlen/2); end
  188. % extrapolate last few samples of the signal
  189. sig = [signal.data bsxfun(@minus,2*signal.data(:,end),signal.data(:,(end-1):-1:end-round(windowlen/2*signal.srate)))];
  190. % process signal using ASR
  191. if useriemannian
  192. [signal.data,state] = asr_process_r(sig,signal.srate,state,windowlen,windowlen/2,stepsize,maxdims,maxmem,usegpu);
  193. else
  194. [signal.data,state] = asr_process(sig,signal.srate,state,windowlen,windowlen/2,stepsize,maxdims,maxmem,usegpu);
  195. end
  196. % shift signal content back (to compensate for processing delay)
  197. signal.data(:,1:size(state.carry,2)) = [];

RFT_clean_asr.m at commit 8c46a2d, under CC0-1.0 · at the source

Overview

Authors: Trinh Nguyen1,2,3, Félix Bigand1, Susanne Reisner2,4, Atesh Koul1, Roberta Bianco1,5, Gabriela Markova6, Stefanie Hoehl2, Giacomo Novembre1
ORCID iDs: Trinh Nguyen, Félix Bigand, Susanne Reisner, Atesh Koul, Roberta Bianco, Stefanie Hoehl, Giacomo Novembre
  1. Neuroscience of Perception and Action Lab, Italian Institute of Technology Rome Italy
  2. Department of Developmental and Educational Psychology, University of Vienna Vienna Austria
  3. Department of Developmental and Biological Psychology, Heidelberg University Heidelberg Germany
  4. Doctoral School Cognition, Behavior and Neuroscience, University of Vienna Vienna Austria
  5. Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa Pisa Italy
  6. Institute for Early Life Care, Paracelsus Medical University Salzburg Austria
Institutions: Italian Institute of Technology (Italy); University of Vienna (Austria); Heidelberg University (Germany); University of Pisa (Italy); Paracelsus Medical University (Austria)
Journal: eLife, volume 14, article RP107088
Dates: published online 7 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.107088 · PMID 42411060 · PMCID PMC13341111 · OpenAlex W4412396860
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism), developmental (subfield)
Methods: Spectral & time-frequency, Statistics, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials
Keywords: infancy, music, movement, EEG, development, pitch, Human
MeSH: Auditory Perception*, Child Development*, Music*, Acoustic Stimulation, Biomechanical Phenomena, Electroencephalography, Female, Humans, Infant, Male, Movement, Pitch Perception (* major topic)
Journal subjects: Neuroscience
Topic: Neuroscience and Music Perception (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Research Council (10.3030/948186); Austrian Science Fund (10.55776/W1262); Italian Institute of Technology (Brain and Machines Flagship Programme); European Commission (10.3030/101105726, 10.3030/101064334)
Citations: cited by 1 paper (Europe PMC); 124 references in the paper

Abstract

Humans across cultures not only share the ability to recognise music but also respond to it through movement. While the sensory encoding of music is well-studied, when and how infants naturally start moving to music is largely unexplored. This study simultaneously investigates infants’ neural (auditory) responses and spontaneous movements to music during the first postnatal year. Neural activity (EEG) and body kinematics (markerless pose estimation) were recorded from 79 infants (aged 3, 6, and 12 months) listening to refrains of children’s music, along with shuffled, high-pitched, and low-pitched versions of the same songs. Neural data revealed that, across all ages, infants exhibit enhanced auditory responses to music compared to shuffled music, indicating that auditory encoding of music emerges early in development. Movement data revealed a different outcome. While coarse auditory-motor coupling is present at all ages, more complex structured movement patterns emerge in response to music only by 12 months. Notably, no age group demonstrated evidence of coordinated movements to music. Additionally, enhanced auditory responses to high vs low pitch were only evident at 6 months, while infants’ movements were better predicted by high-pitched compared to low-pitched music at all ages. This study provides initial insights into how the developing brain gradually transforms music into spontaneous movements of increasing complexity.

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 6 matches between paragraphs and lines of code.

tnguyen1992/tinydancer

License: CC0-1.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 8c46a2ded4fc6bea25e14964a8e11b6a91deba43, 16 June 2026
Languages: MATLAB (53), R (8), Python (2)
Size: 68 files, 63 scripts
Software Heritage: archived
Found in: the references
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: FieldTrip (34 files), car (8 files), emmeans (8 files), ggplot2 (8 files), lme4 (8 files), tidyverse (8 files), EEGLAB (3 files), Signal Processing Toolbox (3 files), reshape2 (3 files), ggpubr (2 files), glmmTMB (2 files), Statistics and Machine Learning Toolbox (2 files), easystats (1 file), ICLabel (1 file), Wavelet Toolbox (1 file), Matplotlib (1 file), MNE-Python (1 file), NumPy (1 file), pandas (1 file), scikit-learn (1 file), SciPy (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
65 files
At the source: archive.softwareheritage.org/swh:1:dir:a35d245c82aeef93b846e1170fb22199ef27d959;origin=https://github.com/tnguyen1992/tinydancer;visit=swh:1:snp:a2fcc0db5ec53c0dd5df14fbb0b23b89cc793743;anchor=swh:1:rev:8bf6034eb98345dfd87bf93b26890f39599fa1cb

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;
  • 63 scripts, each with its path and the digest of its content;
  • 6 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

No dataset and no data link were found in the paper.

Data availability

The data and stimuli reported in this manuscript are available in the following repository: https://doi.org/10.48557/DCSCFO. The codes used for analyses and figures are available on GitHub: https://github.com/tnguyen1992/tinydancer (copy archived at Nguyen, 2026).

The following dataset was generated:

NguyenT BigandF ReisnerS KoulA BiancoR MarkovaG HoehlS NovembreG 2025Replication data for: Development of Auditory and Spontaneous Movement Responses to Music over the First Postnatal YearIIT Dataverse10.48557/DCSCFOPMC1334111142411060

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 7 keywords, 12 MeSH terms, 4 funders, 122 references.

Cite

This paper

Nguyen, T., Bigand, F., Reisner, S., Koul, A., Bianco, R., Markova, G., Hoehl, S., & Novembre, G. (2026). Development of auditory and spontaneous movement responses to music over the first postnatal year. eLife, 14, RP107088. https://doi.org/10.7554/elife.107088

BibTeX

@article{nguyen2026development,
author = {Nguyen, Trinh and Bigand, Félix and Reisner, Susanne and Koul, Atesh and Bianco, Roberta and Markova, Gabriela and Hoehl, Stefanie and Novembre, Giacomo},
title = {{Development of auditory and spontaneous movement responses to music over the first postnatal year}},
journal = {eLife},
year = {2026},
month = jul,
volume = {14},
pages = {RP107088},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.107088},
url = {https://doi.org/10.7554/elife.107088},
pmid = {42411060},
pmcid = {PMC13341111}
}

RIS

TY - JOUR
AU - Nguyen, Trinh
AU - Bigand, Félix
AU - Reisner, Susanne
AU - Koul, Atesh
AU - Bianco, Roberta
AU - Markova, Gabriela
AU - Hoehl, Stefanie
AU - Novembre, Giacomo
TI - Development of auditory and spontaneous movement responses to music over the first postnatal year
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/07/07
VL - 14
SP - RP107088
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.107088
UR - https://doi.org/10.7554/elife.107088
LA - en
ER -

CSL-JSON

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"type": "article-journal",
"title": "Development of auditory and spontaneous movement responses to music over the first postnatal year",
"container-title": "eLife",
"author": [
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"family": "Nguyen",
"given": "Trinh"
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{
"family": "Bigand",
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{
"family": "Reisner",
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{
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{
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"container-title-short": "eLife",
"volume": "14",
"page": "RP107088",
"DOI": "10.7554/elife.107088",
"PMID": "42411060",
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"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.107088",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
7
]
]
}
}

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