OSCR

Spontaneous Modulation of Alpha Power During a Neurofeedback Session Without Instructions.

Code ↔ Paper

10 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 10 matches
  1. [1] § Method › EEG Offline Processing ↔ EEG analysis scripts/INF_5pwr.m, lines 1–26 · score 0.90 · pspectrum function, frequency resolution, 12–15 Hz, 15–30 Hz, beta frequency bands, 8–12 Hz
  2. [2] § Method › EEG Offline Processing ↔ EEG analysis scripts/INF_1data_filter.m, lines 1–25 · score 0.83 · zero phase filtering, notch filter, high pass filter, IIR, Butterworth, filtfilt
  3. [3] § Method › EEG Offline Processing ↔ EEG analysis scripts/INF_5pwr.m, lines 1–26 · score 0.82 · extract spectral power, 12–15 Hz, 15–30 Hz, 8–12 Hz, zero, 4–8 Hz
  4. [4] § Method › Statistical Analyses and Hypotheses Testing ↔ statistical analyses/BFsynth.R, lines 1–49 · score 0.77 · multilevel modeling, full model, Offline alpha, brms, syntax, Bayesian
  5. [5] § Method › Statistical Analyses and Hypotheses Testing ↔ statistical analyses/BFsynth_trials_only.R, lines 1–40 · score 0.71 · multilevel modeling, full model, brms, syntax, Bayesian, variables
  6. [6] § Method › EEG Online Processing ↔ EEG analysis scripts/INF_1data_filter.m, lines 1–25 · score 0.65 · zero phase filtering, IIR, Butterworth, filtfilt, Matlab, window
  7. [7] § Method › Material and Feedback Implementation ↔ material/INF_generate_cond_order_partialLS.m, lines 1–16 · score 0.59 · Latin square design, counterbalance, Matlab
  8. [8] § Results › No Effect of the Continuous Update of Feedback, nor of the Frequency and of the Source of Feedback Update ↔ statistical analyses/mainplots.R, lines 533–603 · score 0.52 · confidence intervals, Offline alpha, Online alpha, feedback update, 15 Hz, beta
  9. [9] § Results › Alpha Increase Is Maintained Throughout the Session no Matter the Source of Feedback Update ↔ statistical analyses/mainplots.R, lines 533–603 · score 0.52 · confidence intervals, Offline alpha, Online alpha, feedback update, 15 Hz, beta
  10. [10] § Method › EEG Recording ↔ EEG analysis scripts/INF_2filt2EEGLAB.m, lines 1–16 · score 0.51 · Matlab R2023a, electrodes positioned, matrixes, EEG

Paper

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

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The authors' code

MATLAB · 163 lines · 4.7 KB · no license · 2 matches

  1. %% INF_5pwr.m
  2. % This script executes a spectral analysis on data previously filtered and
  3. % corrected for eye and clearly identified muscular artifacts. The aim is
  4. % to extract the spectral power of theta (4-8 Hz), alpha (8-12 Hz), SMR
  5. % (12-15 Hz), and high-beta (15-30 Hz) frequency-bands, for each trial and
  6. % participants.
  8. % The script is composed of the following steps:
  9. % 1. Load multidimensional input matrix
  10. % 2. Execute power analysis on each trial and participant
  11. % 3. Extract spectral power of theta, alpha and beta frequency-bands
  12. % 4. Store all data in a new multidimensional matrix (add a new
  13. % dimension)
  14. % 5. Save results
  15. % 6. Display graphs for visualization
  17. % NB: Each signal of interest has 15000 samples digitalized at 250 Hz,
  18. % lasting 60 secondes. The frequency resolution of spectral analyses
  19. % for the 'pspectrum' function is obtained by dividing the half of
  20. % frequency sample by 4096, i.e., 125/4096 (~0.0305).
  21. % Function zero-pad inputs to reach this frequency resolution.
  23. %----------- XXXXXX, last update on November 27th, 2024 ------------------
  24. % This script has been written on MATLAB R2023a on WINDOWS 10.
  26. clear;clc; close all force;
  28. %% Set parameters and load input data
  30. %-- Electrods column codes
  31. Fz = 1;
  32. Cz = 2;
  33. Pz = 3;
  35. %-- Nb of participant
  36. n = 32;
  38. %-- Nb of conditions
  39. nc = 4;
  41. %-- Nb of trials
  42. nt = 8;
  44. %-- Nb of frequency-bands of interest
  45. band = 3;
  47. %-- Load input data (MODIFY the XXXX part)
  48. load("XXXX\data\EEG processed\data_3ICrm.mat"); input = data;clear data;
  49. % Dimensions:
  50. % 1. Data samples
  51. % 2. Trials
  52. % 3. Conditions
  53. % 4. Electrods
  54. % 5. Subjects
  56. %-- Frequency sample
  57. fs = 250;
  59. %% Execute spectral analysis
  61. %-- Initiate matrix
  62. data = zeros(4096,nt,nc,Pz,n);
  64. %-- Use pspectrum function and convert results in dB
  65. % Participant level for loop
  66. for i = 1:n
  67. % Electrod level for loop
  68. for j = Fz:Pz
  69. % Condition level for loop
  70. for k = 1:nc
  71. [p,f] = pspectrum(input(:,:,k,j,i),fs);
  72. p = 10*log10(p);
  74. %-- Transfer absolute power data into corresponding matrix
  75. data(:,:,k,j,i) = p;
  76. end
  77. end
  78. end
  80. %-- Save results (MODIFY the XXXX part)
  81. save("XXXX\data\EEG processed\data_4spec.mat","data")
  82. save("XXXX\data\EEG processed\data_4spec_f.mat","f")
  84. %% Extract bands power values
  86. %-- Extract the corresponding power values index for each frequency band of interest
  87. i_theta = find((4 < f) & (f < 8));
  88. i_alpha = find((8 < f) & (f < 12));
  89. i_beta = find((12 < f) & (f < 30));
  90. i_smr = find((12 < f) & (f < 15));
  91. i_hbeta = find((15 < f) & (f < 30));
  93. %-- Initiate multidim matrixes
  94. % Dims:
  95. % 1. Band power estimates
  96. % 2. Trials
  97. % 3. Conditions
  98. % 4. Electrods
  99. % 5. Subjects
  100. the = zeros(length(i_theta),nt,nc,Pz,n);
  101. alp = zeros(length(i_alpha),nt,nc,Pz,n);
  102. bet = zeros(length(i_beta),nt,nc,Pz,n);
  103. smr = zeros(length(i_smr),nt,nc,Pz,n);
  104. hbet = zeros(length(i_hbeta),nt,nc,Pz,n);
  106. %-- Extract bands power values
  107. % Theta band loop
  108. for i = 1:length(i_theta)
  109. the(i,:,:,:,:) = data(i_theta(i),:,:,:,:);
  110. end
  111. % Alpha band loop
  112. for i = 1:length(i_alpha)
  113. alp(i,:,:,:,:) = data(i_alpha(i),:,:,:,:);
  114. end
  115. % Beta1 band loop
  116. for i = 1:length(i_beta)
  117. bet(i,:,:,:,:) = data(i_beta(i),:,:,:,:);
  118. end
  119. % SMR loop
  120. for i = 1:length(i_smr)
  121. smr(i,:,:,:,:) = data(i_smr(i),:,:,:,:);
  122. end
  123. % High Beta band loop
  124. for i = 1:length(i_hbeta)
  125. hbet(i,:,:,:,:) = data(i_hbeta(i),:,:,:,:);
  126. end
  128. %-- Save results (MODIFY the XXXX part)
  129. % save("XXXX\data\EEG processed\data_4bthe.mat","the")
  130. % save("XXXX\data\EEG processed\data_4balp.mat","alp")
  131. % save("XXXX\data\EEG processed\data_4bbet.mat","bet")
  132. % save("XXXX\data\EEG processed\data_4bsmr.mat","smr")
  133. % save("XXXX\data\EEG processed\data_4bhbet.mat","hbet")
  135. %% Obtain an average band power by subject
  136. % The aim is to get a final data matrix following the dimensions:
  137. % 1. 32 Subjects
  138. % 2. 8 Trials
  139. % 3. 4 Conditions
  140. % 4. 3 Electrods
  141. % 5. 5 Bands
  143. %-- Initiate matrix
  144. data = zeros(n,nt,nc,Pz,band);
  146. %-- Average each band power estimates by subject
  147. mthe = mean(the,1);
  148. malp = mean(alp,1);
  149. mbet = mean(bet,1);
  150. msmr = mean(smr,1);
  151. mhbet = mean(hbet,1);
  153. %-- Store results in one matrix
  154. for i = 1:n
  155. data(i,:,:,:,1) = mthe(:,:,:,:,i);
  156. data(i,:,:,:,2) = malp(:,:,:,:,i);
  157. data(i,:,:,:,3) = mbet(:,:,:,:,i);
  158. data(i,:,:,:,4) = msmr(:,:,:,:,i);
  159. data(i,:,:,:,5) = mhbet(:,:,:,:,i);
  160. end
  162. %-- Save results (MODIFY the XXXX part)
  163. save("XXXX\data\EEG processed\data_5mpwr.mat","data")

INF_5pwr.m, no license · at the source

Overview

Authors: Jacob Maaz1,2,3, Véronique Paban1,2, Laurent Waroquier4, Arnaud Rey1,3
ORCID iDs: Jacob Maaz, Véronique Paban, Laurent Waroquier, Arnaud Rey
  1. Aix Marseille Univ, CNRS CRPN Marseille France
  2. Institute Neuro‐Marseille Aix Marseille Univ Marseille France
  3. Institute of Language, Communication and the Brain Aix Marseille Univ Marseille France
  4. PSYCLE Aix Marseille Univ Marseille France
Institutions: Centre National de la Recherche Scientifique (France); Aix-Marseille Université (France); Centre de Recherche en Psychologie et Neurosciences (France); Recherche en Psychologie de la Connaissance, du Langage et de l'Émotion (France)
Journal: Psychophysiology, volume 63, issue 3, article e70285
Dates: received 21 October 2025; accepted 12 March 2026; published online 24 March 2026; in print March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1111/psyp.70285 · PMID 41874341 · PMCID PMC13011910 · OpenAlex W4415314171
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), human (organism)
Methods: Spectral & time-frequency, Preprocessing, Smoothing, state filtering, decompositions, Physiology & signal measures, Statistics
Keywords: alpha activity, EEG, neurofeedback, neuromodulation, spectral power
MeSH: Alpha Rhythm*, Neurofeedback*, Adult, Electroencephalography, Female, Humans, Male, Young Adult (* major topic)
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: French Ministry of Higher Education, Research and Innovation; Fond de dotation JANSSEN HORIZON (CNRS SPV/MB/214535); Agence Nationale de la Recherche (French National Research Agency) (ANR‐16‐CONV‐0002, ANR‐23‐CE28‐0008)
Citations: cited by 2 papers (Europe PMC); 118 references in the paper

Abstract

Electroencephalographic neurofeedback (EEG‐NF) enables individuals to self‐regulate specific EEG features with real‐time sensory feedback. Despite clinical and cognitive‐enhancement applications, the mechanisms underlying the EEG modulation remain poorly understood. Particularly, alpha activity (8–12 Hz) upregulation may occur independently of the participants' volitional control. We previously reported spontaneous increases in alpha power during a passive neurofeedback‐like task using pre‐recorded EEG feedback. In the present study, we replicated this protocol while implementing an EEG‐NF procedure using real‐time alpha power. Thirty‐two healthy adults observed a gray circle whose size was either fixed (control) or continuously updated at 1, 5 or 10 Hz (experimental conditions). Importantly, participants were not informed of the nature of the feedback and received no instructions for self‐regulation. We evaluated the effects of (i) trial repetition, (ii) the presence (control vs. experimental conditions), (iii) the frequency (1, 5 or 10 Hz) and (iv) the source (online vs. offline alpha) of feedback update on EEG features classically targeted by EEG‐NF. Importantly, we observed robust increases in alpha power over time independently of the presence, frequency and source of feedback update. The presence, frequency and source of feedback update did not influence the EEG features considered. These findings suggest that alpha EEG‐NF upregulation may arise from spontaneous dynamics, such as time‐on‐task effects, rather than the hypothesized self‐regulation mechanism. The assumption that observed alpha increases reflect successful neurofeedback learning is thus called into question. More broadly, the present study highlights the importance of including control conditions and accounting for non‐specific effects when evaluating EEG‐NF outcomes.

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 10 matches between paragraphs and lines of code.

OSF yp8fw

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: MATLAB (14), R (6)
Size: 67 files, 20 scripts
Software Heritage: not checked
Found in: “Data Availability Statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Signal Processing Toolbox (5 files), tidyverse (5 files), Parallel Computing Toolbox (4 files), easystats (3 files), patchwork (3 files), Psychtoolbox (3 files), brms (2 files), Statistics and Machine Learning Toolbox (2 files), Stan (2 files), afex (1 file), cowplot (1 file), EEGLAB (1 file), ggpubr (1 file), ICLabel (1 file), reshape2 (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/yp8fw

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;
  • 20 scripts, each with its path and the digest of its content;
  • 10 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 Statement

All materials, data, and analysis codes are available via the Open Science Framework: https://osf.io/yp8fw.

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, issue, pages, dates, 4 authors, 5 keywords, 8 MeSH terms, 3 funders, 112 references.

Cite

This paper

Maaz, J., Paban, V., Waroquier, L., & Rey, A. (2026). Spontaneous Modulation of Alpha Power During a Neurofeedback Session Without Instructions. Psychophysiology, 63(3), e70285. https://doi.org/10.1111/psyp.70285

BibTeX

@article{maaz2026spontaneous,
author = {Maaz, Jacob and Paban, Véronique and Waroquier, Laurent and Rey, Arnaud},
title = {{Spontaneous Modulation of Alpha Power During a Neurofeedback Session Without Instructions}},
journal = {Psychophysiology},
year = {2026},
month = mar,
volume = {63},
number = {3},
pages = {e70285},
publisher = {Wiley},
issn = {0048-5772},
doi = {10.1111/psyp.70285},
url = {https://doi.org/10.1111/psyp.70285},
pmid = {41874341},
pmcid = {PMC13011910}
}

RIS

TY - JOUR
AU - Maaz, Jacob
AU - Paban, Véronique
AU - Waroquier, Laurent
AU - Rey, Arnaud
TI - Spontaneous Modulation of Alpha Power During a Neurofeedback Session Without Instructions
T2 - Psychophysiology
J2 - Psychophysiology
PY - 2026
DA - 2026/03/01
VL - 63
IS - 3
SP - e70285
SN - 0048-5772
PB - Wiley
DO - 10.1111/psyp.70285
UR - https://doi.org/10.1111/psyp.70285
LA - en
ER -

CSL-JSON

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