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Test-retest reliability analysis of resting-state EEG measures and their association with long-term memory in children and adults.

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
  1. [1] § Methods › Study 1 › Data analysis › EEG power ↔ data_and_code/b00_PowerAnalysis_PRE_WholeSegm.m, lines 23–129 · score 0.94 · 14–30 Hz, 30–75 Hz, 8–14 Hz, power spectrum, linear scale, frequency band
  2. [2] § Methods › Study 1 › Data analysis › EEG power ↔ data_and_code/b01_PowerAnalysis_PRE_A.m, lines 24–140 · score 0.94 · 14–30 Hz, 30–75 Hz, 8–14 Hz, power spectrum, linear scale, frequency band
  3. [3] § Methods › Study 1 › Data analysis › Microstates ↔ data_and_code/c0_Microstates_PRE_POST.m, lines 24–113 · score 0.93 · GFP peaks, pop_CombMSMaps, pop_FindMSMaps, pop_FitMSMaps, ignoring polarity, microstate maps
  4. [4] § Methods › Study 1 › Data analysis › Microstates ↔ data_and_code/c1_Microstates_PRE_A_B.m, lines 29–116 · score 0.91 · GFP peaks, pop_CombMSMaps, pop_FindMSMaps, pop_FitMSMaps, ignoring polarity, restarts
  5. [5] § Methods › Study 1 › Materials & procedure ↔ data_and_code/a00_Preprocessing_PRE.m, lines 198–284 · score 0.79 · pop_iclabel, Independent Component, copy, eyes, muscle, rejected
  6. [6] § Methods › Study 1 › Materials & procedure ↔ data_and_code/a10_Preprocessing_POST.m, lines 198–284 · score 0.79 · pop_iclabel, Independent Component, copy, eyes, muscle, rejected

Paper

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

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The authors' code

MATLAB · 129 lines · 4.6 KB · no license · 1 match

  1. % PowerAnalysis of the resting state EEG at T1 (= PRE) "pre" intervention
  2. % with app. Power calculated from whole segment
  4. clear all
  5. %machine = getenv('COMPUTERNAME')
  7. % Get the full path of the currently running script
  8. scriptFullPath = matlab.desktop.editor.getActiveFilename;
  10. % Extract the folder path
  11. currentScriptFolder = fileparts(scriptFullPath);
  13. outputPath = fullfile(currentScriptFolder, "OutputPreprocPRE");
  15. cd(outputPath)
  17. %% Add eeglab to path and launch
  18. addpath(fullfile(currentScriptFolder,'eeglab2023.0', filesep))
  20. % start EEGlab
  21. eeglab;
  22. % close;
  23. %% Power calculation
  25. % Define the list of EEG file identifiers
  26. MigrNr = string(1:126);
  28. % Initialize EEGLAB
  29. [ALLEEG, EEG, CURRENTSET, ALLCOM] = eeglab;
  31. % Prepare the header for the CSV file
  32. header = {'MigrNr', 'd_index', 'Delta_ABS', 'Delta_REL', 'Theta_ABS', 'Theta_REL', ...
  33. 'Alpha_ABS', 'Alpha_REL', 'Beta_ABS', 'Beta_REL', ...
  34. 'Gamma_ABS', 'Gamma_REL'};
  36. % Open the CSV file for writing
  37. filename = 'PowerWholeBrainPre.csv';
  38. fid = fopen(filename, 'w');
  39. fprintf(fid, '%s,', header{:});
  40. fprintf(fid, '\n');
  43. % Iterate over each entry in the list
  44. for i = 1:length(MigrNr) %length(MigrNr)
  45. fileID = MigrNr(i);
  46. fileName = strcat(fileID, '.set'); % Construct the file name
  48. % Load the preprocessed EEG file
  49. EEG = pop_loadset('filename', char(fileName));
  51. % (Optional) Update ALLEEG structure to keep track of the dataset
  52. [ALLEEG, EEG, CURRENTSET] = eeg_store(ALLEEG, EEG, 0);
  54. % Frequency bands
  55. deltaBand = [1 4];
  56. thetaBand = [4 8];
  57. alphaBand = [8 14];
  58. betaBand = [14 30];
  59. gammaBand = [30 75];
  61. % Initialize variables to store power values for each electrode
  62. numElectrodes = EEG.nbchan;
  63. deltaPower = zeros(numElectrodes, 1);
  64. thetaPower = zeros(numElectrodes, 1);
  65. alphaPower = zeros(numElectrodes, 1);
  66. betaPower = zeros(numElectrodes, 1);
  67. gammaPower = zeros(numElectrodes, 1);
  68. totalPower = zeros(numElectrodes, 1);
  70. % Compute power for each electrode
  71. for chan = 1:numElectrodes
  72. % Compute the power spectrum using spectopo
  73. [spectra, freqs] = spectopo(EEG.data(chan, :), 0, EEG.srate, ...
  74. 'winsize', 2 * EEG.srate, ... % cuts the data into 2s windows
  75. 'overlap', floor(0.80 * 2 * EEG.srate), ...
  76. 'plot', 'off');
  78. % Convert spectra from dB to linear scale
  79. spectra = 10.^(spectra / 10);
  81. % Define a function to integrate power within a band
  82. integrate_bandpower = @(spectra, freqs, band) trapz(freqs(freqs >= band(1) & freqs <= band(2)), spectra(freqs >= band(1) & freqs <= band(2)));
  84. % Compute the total power in each frequency band for this electrode
  85. deltaPower(chan) = integrate_bandpower(spectra, freqs, deltaBand);
  86. thetaPower(chan) = integrate_bandpower(spectra, freqs, thetaBand);
  87. alphaPower(chan) = integrate_bandpower(spectra, freqs, alphaBand);
  88. betaPower(chan) = integrate_bandpower(spectra, freqs, betaBand);
  89. gammaPower(chan) = integrate_bandpower(spectra, freqs, gammaBand);
  91. % Compute total power across all frequencies for this electrode
  92. totalPower(chan) = trapz(freqs, spectra);
  93. end
  95. % Compute relative power for each frequency band for all channels
  96. relativeDeltaPower = deltaPower ./ totalPower;
  97. relativeThetaPower = thetaPower ./ totalPower;
  98. relativeAlphaPower = alphaPower ./ totalPower;
  99. relativeBetaPower = betaPower ./ totalPower;
  100. relativeGammaPower = gammaPower ./ totalPower;
  102. % Compute average power across all electrodes
  103. avgDeltaPower = mean(deltaPower);
  104. avgThetaPower = mean(thetaPower);
  105. avgAlphaPower = mean(alphaPower);
  106. avgBetaPower = mean(betaPower);
  107. avgGammaPower = mean(gammaPower);
  108. avgTotalPower = mean(totalPower);
  110. % Compute average relative power for each frequency band
  111. avgRelativeDeltaPower = mean(relativeDeltaPower);
  112. avgRelativeThetaPower = mean(relativeThetaPower);
  113. avgRelativeAlphaPower = mean(relativeAlphaPower);
  114. avgRelativeBetaPower = mean(relativeBetaPower);
  115. avgRelativeGammaPower = mean(relativeGammaPower);
  117. % Prepare the data to be saved
  118. data = {fileID, i, avgDeltaPower, avgRelativeDeltaPower, avgThetaPower, avgRelativeThetaPower, ...
  119. avgAlphaPower, avgRelativeAlphaPower, avgBetaPower, avgRelativeBetaPower, ...
  120. avgGammaPower, avgRelativeGammaPower};
  122. % Write the data to the CSV file
  123. fprintf(fid, '%s,%d,', data{1}, data{2});
  124. fprintf(fid, '%f,', data{3:end});
  125. fprintf(fid, '\n');
  126. end
  128. % Close the CSV file
  129. fclose(fid);

b00_PowerAnalysis_PRE_WholeSegm.m, no license · at the source

Overview

Authors: Anastasios Ziogas1, Simon Ruch1, Nicole H. Skieresz1,2,3, Sandy C. Marca1,4, Nicolas Rothen1, Thomas P. Reber1,5
ORCID iDs: Anastasios Ziogas, Simon Ruch, Nicole H. Skieresz, Sandy C. Marca, Nicolas Rothen, Thomas P. Reber
  1. Faculty of Psychology, UniDistance Suisse, Brig, Switzerland
  2. The LINE (Laboratory for Investigative Neurophysiology), Department of Diagnostic and Interventional Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
  3. The Sense Innovation and Research Center, Lausanne and Sion, Switzerland
  4. Faculty of Psychology, University of Geneva, Geneva, Switzerland
  5. Department of Epileptology, University of Bonn Medical Center, Bonn, Germany
Institutions: UniDistance Suisse (Switzerland); Centre Hospitalier Universitaire Vaudois (Switzerland); The Sense Innovation and Research Center (Switzerland); University of Lausanne (Switzerland); University of Geneva (Switzerland); University of Bonn (Germany); University Hospital Bonn (Germany)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1224
Dates: received 4 July 2025; accepted 10 April 2026; published online 29 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1224 · PMID 42077507 · PMCID PMC13130526 · OpenAlex W4413011400
Open access: diamond, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.08.04.668389
Status: code verified
Categories: EEG (modality), human (organism), cognitive (subfield)
Methods: Spectral & time-frequency, Statistics, Machine learning, Smoothing, state filtering, decompositions, Preprocessing, Evoked potentials, Connectivity
Keywords: resting-state EEG, long-term memory, alpha power, second language learning
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Service of higher education of the canton of Valais
Citations: cited by 1 paper (Europe PMC); 99 references in the paper

Abstract

EEG resting-state measures, such as spectral power and microstates, have been associated with human long-term memory (LTM) performance. However, findings across studies are inconsistent and sometimes contradictory, likely due to a low reliability of the measures employed. These inconsistencies limit the interpretability and generalizability of results, emphasizing the need for a systematic evaluation of measure reliability. In this study, we addressed this gap by identifying the most reliable EEG resting-state measures and evaluating their predictive value for LTM performance in a second-language (L2) vocabulary learning paradigm. A group of children (N = 36) and adults (N = 90) participated in two studies on second-language vocabulary learning. Participants completed a test on L2 vocabulary and a resting-state EEG recording (180 seconds eyes open) before and after learning a second language. We used Intraclass Correlation Coefficients (ICC) to identify resting-state EEG measures with satisfying test-retest reliability (ICC > = 0.75) and then assessed how these reliable measures are associated with L2 vocabulary learning representing LTM performance. Highest ICC values were found for oscillatory power in the alpha range and in the frequency of occurrences, duration, and coverages of microstates. Calculations yielded ICC values of 0.84/0.86 (children/adults) for alpha power and 0.88/0.80 for microstate measures. Of these measures, only alpha power showed a positive correlation with LTM performance, but only in the adult population (r = 0.38, p < .01). No other measures were associated with LTM (all p > .05). Alpha power could thus serve as a stable and reliable marker of the neural mechanisms accounting for high LTM performance in the fully developed adult brain.

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.

OSF 5znyk

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: MATLAB (11), R (9)
Size: 52 files, 20 scripts
Software Heritage: not checked
Found in: “Data and Code Availability”
Holds: README, 9 notebooks
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: EEGLAB (11 files), tidyverse (9 files), psych (6 files), broom (5 files), ICLabel (2 files), ggplot2 (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
20 files
At the source: osf.io/5znyk/

The paper's code and data availability statement is in the Data section.

Tracing map

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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;
  • 20 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 and Code Availability

All data and analysis scripts used in this study are publicly available at the Open Science Framework (OSF): https://osf.io/5znyk/, DOI: https://doi.org/10.17605/OSF.IO/5ZNYK.

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 4 keywords, 1 funder, 97 references.

Cite

This paper

Ziogas, A., Ruch, S., Skieresz, N. H., Marca, S. C., Rothen, N., & Reber, T. P. (2026). Test-retest reliability analysis of resting-state EEG measures and their association with long-term memory in children and adults. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1224. https://doi.org/10.1162/imag.a.1224

BibTeX

@article{ziogas2026test,
author = {Ziogas, Anastasios and Ruch, Simon and Skieresz, Nicole H. and Marca, Sandy C. and Rothen, Nicolas and Reber, Thomas P.},
title = {{Test-retest reliability analysis of resting-state EEG measures and their association with long-term memory in children and adults}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = apr,
volume = {4},
pages = {IMAG.a.1224},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1224},
url = {https://doi.org/10.1162/imag.a.1224},
pmid = {42077507},
pmcid = {PMC13130526}
}

RIS

TY - JOUR
AU - Ziogas, Anastasios
AU - Ruch, Simon
AU - Skieresz, Nicole H.
AU - Marca, Sandy C.
AU - Rothen, Nicolas
AU - Reber, Thomas P.
TI - Test-retest reliability analysis of resting-state EEG measures and their association with long-term memory in children and adults
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/04/29
VL - 4
SP - IMAG.a.1224
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1224
UR - https://doi.org/10.1162/imag.a.1224
LA - en
ER -

CSL-JSON

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