OSCR

Non-specific increase in alpha power during a neurofeedback session targeting its downregulation.

Code ↔ Paper

11 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 11 matches
  1. [1] § Method › Material and neurofeedback implementation ↔ material/IAN_gencondorder.m, lines 1–19 · score 0.90 · Latin square design, possible triplets, EEG NF session, feedback update, counterbalance, training blocks
  2. [2] § Method › EEG offline processing ↔ EEG offline analyses/pret3/IAN_1data_filter.m, lines 1–25 · score 0.83 · zero phase filtered, notch filter, high pass filter, IIR, Butterworth, offline
  3. [3] § Method › EEG offline processing ↔ EEG offline analyses/t3/IAN_5pwr.m, lines 1–25 · score 0.83 · pspectrum function, 12–15 Hz, 15–30 Hz, 8–12 Hz, spectral power, signal
  4. [4] § Method › EEG offline processing ↔ EEG offline analyses/t3/IAN_5pwr.m, lines 1–25 · score 0.81 · 12–15 Hz, 15–30 Hz, 8–12 Hz, Matlab R2023a, spectral power, 4–8 Hz
  5. [5] § Method › Statistical analyses and hypotheses testing › EEG data ↔ statistical analyses/IAN_BFsynth.R, lines 1–53 · score 0.77 · multilevel models, brms syntax, full model, Bayesian, training, blocks
  6. [6] § Method › Statistical analyses and hypotheses testing › EEG data ↔ statistical analyses/IAN_BFsynth_wf.R, lines 1–41 · score 0.76 · multilevel models, brms syntax, full model, Bayesian, blocks, Sham
  7. [7] § Method › EEG offline processing ↔ EEG offline analyses/pret3/IAN_3runICA.m, lines 1–22 · score 0.76 · eye blink, EEGLAB, ICA, Infomax, movement, Component
  8. [8] § Method › EEG online processing ↔ EEG offline analyses/pret3/IAN_1data_filter.m, lines 1–25 · score 0.72 · zero phase filtering, Matlab R2023a, IIR, Butterworth, filtfilt, window
  9. [9] § Method › EEG offline processing ↔ EEG offline analyses/t3/IAN_6zscore.m, lines 10–50 · score 0.64 · 12–15 Hz, 15–30 Hz, 8–12 Hz, 4–8 Hz, frequency bands, Offline
  10. [10] § Method › EEG offline processing ↔ EEG offline analyses/t3/IAN_6zscore.m, lines 10–50 · score 0.63 · 12–15 Hz, 15–30 Hz, 8–12 Hz, 4–8 Hz, frequency bands, beta
  11. [11] § Method › Statistical analyses and hypotheses testing › EEG data ↔ statistical analyses/IAN_mainplots.R, lines 277–327 · score 0.50 · trial repetition, transfer block, frequency bands, Alpha power, variables, electrodes

Paper

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

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The authors' code

MATLAB · 265 lines · 9.2 KB · no license · 2 matches

  1. %% ------------------------------------------------------ IAN_1data_filter
  2. % This script applies offline an IIR high-pass filter (Butterworth, 6th order), and
  3. % a Notch filter at 50 Hz on the raw data (four dimensions matrix for:
  4. % 1) electrod position ; 2) data sample; 3) trial; 4) block).
  5. % Each trial initially lasts 63 secondes. Each operation is done on each
  6. % trial. First the script plots each trial raw data of each channel.
  7. % Second, the script doubles the last second at the end the recording.
  8. % This is done in order to, after filtering data (3rd step), remove the
  9. % filter transients by deleting the first two and the last seconds. At the
  10. % same time, data are reorganized into one single multidimensional matrix:
  11. % 1. Electrods positions
  12. % 2. Data samples
  13. % 3. Trials
  14. % 4. Blocks
  15. % 5. Subjects
  16. % Finally, filtered data of each channel are plotted.
  17. %
  18. % NB: Using the function 'filtfilt' doubles the filter order, but avoids
  19. % the associated phase/filter delay. For more information, see the
  20. % MATLAB documentation about this function. == zero-phase filtering
  22. %----------- Anonymised for PR, last update on April 11th, 2025 ------------------
  23. % This script has been written on MATLAB R2023a on WINDOWS 10.
  25. clear;clc;close all force;
  27. %% Define parameters and filter coefficients
  29. %-- Frequency sample in Hz (250 Hz)
  30. fs = 250;
  32. %-- Cut-off frequencies for the high-pass filter in Hz
  33. fch1 = 0.5./(fs/2); % normalized for the butter function syntax
  35. %-- Filters coefs
  36. % High pass Butterworth
  37. [b, a] = butter(6,fch1,'high');
  38. % Notch filter at 50 Hz
  39. wo = 50/(fs/2);
  40. bw = wo/35;
  41. [bn,an] = iirnotch(wo,bw);
  43. %-- Electrods column codes
  44. Fp1 = 1;
  45. Fpz = 2;
  46. Fp2 = 3;
  47. Fz = 4;
  48. Cz = 5;
  49. Pz = 6;
  51. %-- Trials initial length (in secs)
  52. dur_max = 63;
  54. %-- Define initial time vector for raw data
  55. t = 1/fs:1/fs:dur_max; t = t-3;
  57. %-- Define a new time vector for filtered data
  58. tfilt = 1/fs:1/fs:dur_max-3;
  60. %-- Define matrix dimensions
  61. ne = 6; % nb of electrods
  62. ns = length(tfilt); % nb of samples
  63. n = input("What is the number of subjects so far? "); % nb of participants
  64. nt = 8; % nb of trials
  65. nc = 4; % nb of blocks
  67. %-- Load block order vector to retrieve participants' one
  68. load("XXXX\material\cond_order.mat")
  70. %-- Initiate or Load multidimensional matrix to organize raw data
  71. % Dimensions:
  72. % 1. Electrods positions
  73. % 2. Data samples
  74. % 3. Trials
  75. % 4. Blocks
  76. % 5. Subjects
  77. if exist(join(['XXXX\data\EEG processed',filesep,"data_1raw.mat"],''),"file")
  78. load("XXXX\data\EEG processed\data_1raw.mat");
  80. %-- Determine from which subject to begin filtering
  81. i_start = size(data_1raw,5)+1;
  82. if i_start > n
  83. i_start = n;
  84. end
  86. %-- Preallocate more length to dimension subject
  87. for i = i_start:n
  88. data_1raw(:,:,:,:,i) = zeros(ne,length(t),nt,nc); %#ok<SAGROW>
  89. end
  90. else
  91. data_1raw = zeros(ne,length(t),nt,nc,n);
  92. i_start = 1;
  93. end
  95. %-- Initiate or Load multidimensional matrix to store filtered data
  96. % Dimensions:
  97. % 1. Electrods positions
  98. % 2. Data samples
  99. % 3. Trials
  100. % 4. Blocks
  101. % 5. Subjects
  102. if i_start == 1
  103. data_2filt = zeros(ne,ns,nt,nc,n);
  104. else
  105. load("XXXX\data\EEG processed\data_2filt.mat");
  106. end
  108. %% Visualize raw data in the time domain
  110. % %----- Electrode level loop
  111. % for e = 1:ne
  112. % %----- Subject level loop
  113. % for i = 25:n
  114. %
  115. % %-- Get participant block order
  116. % g = cond_order(:,i);
  117. %
  118. % %-- Load corresponding data
  119. % if i < 10
  120. % load(join(["data_1raw",filesep,"IAN0",i,"_",join(num2str(g)',''),".mat"],''));
  121. % else
  122. % load(join(["data_1raw",filesep,"IAN",i,"_",join(num2str(g)',''),".mat"],''));
  123. % end
  124. %
  125. % %-- Create a figure for each electrod
  126. % if e == Fz
  127. % f1 = figure('NumberTitle','off','Name',join(['S' string(i) ' data at Fz'],''));
  128. % f1.WindowState = "maximized";
  129. % elseif e == Fp1
  130. % f1 = figure('NumberTitle','off','Name',join(['S' string(i) ' data at Fp1'],''));
  131. % f1.WindowState = "maximized";
  132. % elseif e == Fpz
  133. % f1 = figure('NumberTitle','off','Name',join(['S' string(i) ' data at Fpz'],''));
  134. % f1.WindowState = "maximized";
  135. % elseif e == Fp2
  136. % f1 = figure('NumberTitle','off','Name',join(['S' string(i) ' data at Fp2'],''));
  137. % f1.WindowState = "maximized";
  138. % elseif e == Cz
  139. % f1 = figure('NumberTitle','off','Name',join(['S' string(i) ' data at Cz'],''));
  140. % f1.WindowState = "maximized";
  141. % else
  142. % f1 = figure('NumberTitle','off','Name',join(['S' string(i) ' data at Pz'],''));
  143. % f1.WindowState = "maximized";
  144. % end
  145. % %----- Block level loop
  146. % for j = 1:nc
  147. % %----- Trial level loop
  148. % for k = 1:nt
  149. % subplot(nc,nt,j*nt-nt+k)
  150. % plot(t,eeg_data(e,:,k,j))
  151. % xlim([min(t) max(t)])
  152. % xlabel("Time (sec)")
  153. % ylabel("Amplitude (mV)")
  154. % if j == nc
  155. % title(join(['Block 4 Trial ' string(k)],''))
  156. % else
  157. % title(join(['Block ' string(g(j)) ' Trial ' string(k)],''))
  158. % end
  159. % end
  160. % end
  161. % end
  162. % end
  164. %% Apply filter on each channel and store results in multidimensional matrix
  166. %-- Initate matrix to contain 64-sec trials (in order to transfert first
  167. % sec to the end)
  168. % (dimensions are inversed to match filtfilt function input)
  169. D = zeros(fs*(dur_max+1),ne);
  171. %------ Subject Level Loop
  172. for i = i_start:n
  173. %-- Check participant's Blocks order
  174. g = cond_order(:,i);
  176. %-- Initiate multidim matrix for subject i
  177. dataPT_filt = zeros(ne,ns,nt,nc);
  179. %-- Load subject i data
  180. if i < 10
  181. load(join(["XXXX\data\raw",filesep,"IAN0",i,"_",join(num2str(g)',''),".mat"],''));
  182. else
  183. load(join(["XXXX\data\raw",filesep,"IAN",i,"_",join(num2str(g)',''),".mat"],''));
  184. end
  186. %-- Reorganize individual datasets into one multidimensional matrix
  187. data_1raw(:,:,:,:,i) = eeg_data; %#ok<SAGROW>
  189. %----- Block Level Loop
  190. for j = 1:nc
  191. %-- Retrieve which Block was done in the j-th position
  192. if j == nc
  193. cond = nc;
  194. else
  195. cond = g(j);
  196. end
  198. %----- Trial level loop
  199. for k = 1:nt
  200. %-- Buffer corresponding dataset
  201. D(1:length(t),:) = eeg_data(:,:,k,j)';
  203. %-- Copy the last second at the end
  204. D((end-fs+1):end,:) = D((end-fs*2+1):end-fs,:);
  206. %-- Apply filter
  207. Dfilt = filtfilt(bn,an,filtfilt(b,a,D));
  209. %-- Delete transients
  210. Dfilt = Dfilt((fs*3+1):(end-fs),:)';
  212. %-- Save the data in the corresponding dimension of matrix
  213. data_2filt(:,:,k,cond,i) = Dfilt; % note that Blocks dim is always in the following order : 1) 1 Hz; 2) 5 Hz; 3) 10 Hz; and 4) Without Neurofeedback
  214. end
  215. end
  216. end
  218. %-- Save multidim raw matrix
  219. save("XXXX\data\EEG processed\data_1raw.mat","data_1raw")
  221. % %-- Save filtered data
  222. save("XXXX\data\EEG processed\data_2filt.mat","data_2filt")
  224. % %-- Save new time vector
  225. save("XXXX\data\EEG processed\data_2filt_tfilt.mat","tfilt")
  227. %% Visualize the filter effect
  229. %----- Electrod level loop
  230. for e = 1:ne
  231. %----- Subject level loop
  232. for i = i_start:n
  233. if e == Fz
  234. f1 = figure('NumberTitle','off','Name',join(['S' string(i) ' data at Fz'],''));
  235. f1.WindowState = "maximized";
  236. elseif e == Fp1
  237. f1 = figure('NumberTitle','off','Name',join(['S' string(i) ' data at Fp1'],''));
  238. f1.WindowState = "maximized";
  239. elseif e == Fpz
  240. f1 = figure('NumberTitle','off','Name',join(['S' string(i) ' data at Fpz'],''));
  241. f1.WindowState = "maximized";
  242. elseif e == Fp2
  243. f1 = figure('NumberTitle','off','Name',join(['S' string(i) ' data at Fp2'],''));
  244. f1.WindowState = "maximized";
  245. elseif e == Cz
  246. f1 = figure('NumberTitle','off','Name',join(['S' string(i) ' data at Cz'],''));
  247. f1.WindowState = "maximized";
  248. else
  249. f1 = figure('NumberTitle','off','Name',join(['S' string(i) ' data at Pz'],''));
  250. f1.WindowState = "maximized";
  251. end
  252. %----- Block level loop
  253. for j = 1:nc
  254. %----- Trial level loop
  255. for k = 1:nt
  256. subplot(nc,nt,j*nt-nt+k)
  257. plot(tfilt,data_2filt(e,:,k,j,i))
  258. xlim([min(tfilt) max(tfilt)])
  259. xlabel("Time (sec)")
  260. ylabel("Amplitude (mV)")
  261. title(join(['Block ' string(j) ' Trial ' string(k)],''))
  262. end
  263. end
  264. end
  265. end

IAN_1data_filter.m, no license · at the source

Overview

Authors: Jacob Maaz1,2,3, Alexandra Dia1,2, Laurent Waroquier4, Véronique Paban1,2, Arnaud Rey1,3
ORCID iDs: Jacob Maaz, 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. Aix Marseille Univ, PSYCLE, Aix-en-Provence, France
Institutions: Centre National de la Recherche Scientifique (France); Aix-Marseille Université (France); Recherche en Psychologie de la Connaissance, du Langage et de l'Émotion (France)
Journal: Imaging neuroscience (Cambridge, Mass.), volume 4, article IMAG.a.1258
Dates: received 20 October 2025; accepted 1 May 2026; published online 29 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1162/imag.a.1258 · PMID 42232075 · PMCID PMC13224316 · OpenAlex W7160404595
Open access: diamond, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), systems (subfield)
Methods: Spectral & time-frequency, Preprocessing, Smoothing, state filtering, decompositions, Physiology & signal measures, Statistics
Keywords: EEG, neurofeedback, non-specific influences, alpha activity
Topic: Mind wandering and attention (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Agence Nationale de la Recherche (ANR-23-CE28-0008, ANR-16-CONV-0002); Ministère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche
Citations: not cited yet (Europe PMC); 97 references in the paper

Abstract

Electroencephalographic neurofeedback is often assumed to provide volitional control over neural oscillations, with the alpha rhythm regarded as a particularly accessible target. Yet, evidence supporting single-session alpha modulation remains controversial, largely because of insufficient controls for non-specific influences such as time-on-task effects. This study examined whether individuals can downregulate alpha parietal-scalp power during a single neurofeedback session while controlling for feedback veracity and targeted modulation direction. Healthy individuals completed three training blocks in which the size of a visual stimulus reflected either their real-time alpha power (Alpha-Down group), alpha power in the opposite direction (Alpha-Up group), or prerecorded trajectories (Sham group), followed by a transfer block without feedback. The frequency of feedback update was varied across training blocks (1, 5, or 10 Hz). Results showed robust within-session increases in alpha power across all groups, independent of targeted direction, feedback veracity, or update frequency. Theta and sensorimotor rhythm bands also demonstrated an independent increase, while beta remained stable. These findings indicate that apparent alpha modulation in single-session protocols reflects spontaneous, non-specific increases in oscillatory activity rather than genuine volitional control. The results generalise previous evidence on alpha upregulation to its attempted downregulation, reinforcing that time-dependent dynamics such as arousal, fatigue, or mind wandering may dominate single-session outcomes. This work highlights the need for stringent methodological controls when evaluating the specificity of neurofeedback interventions.

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 11 matches between paragraphs and lines of code.

OSF 6bn4v

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Languages: MATLAB (16), R (5)
Size: 30 files, 21 scripts
Software Heritage: not checked
Found in: “Data and Code Availability”
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), Psychtoolbox (4 files), patchwork (3 files), easystats (2 files), Statistics and Machine Learning Toolbox (2 files), afex (1 file), BayesFactor (1 file), brms (1 file), broom (1 file), cowplot (1 file), EEGLAB (1 file), ggplot2 (1 file), ggpubr (1 file), ICLabel (1 file), reshape2 (1 file), Stan (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
21 files
At the source: osf.io/6bn4v

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;
  • 21 scripts, each with its path and the digest of its content;
  • 11 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

Supplementary Table S10 (https://doi.org/10.1162/IMAG.a.1258#supplementary-data) presents the checklist of the Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf). All materials, data, and analysis codes are available via the Open Science Framework: https://osf.io/6bn4v.

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

  • Funding: added Agence Nationale de la Recherche: ANR-23-CE28-0008, ANR-16-CONV-0002; Ministère de l'Enseignement Supérieur et de la Recherche

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 5 authors, 4 keywords, 93 references.

Cite

This paper

Maaz, J., Dia, A., Waroquier, L., Paban, V., & Rey, A. (2026). Non-specific increase in alpha power during a neurofeedback session targeting its downregulation. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1258. https://doi.org/10.1162/imag.a.1258

BibTeX

@article{maaz2026non,
author = {Maaz, Jacob and Dia, Alexandra and Waroquier, Laurent and Paban, Véronique and Rey, Arnaud},
title = {{Non-specific increase in alpha power during a neurofeedback session targeting its downregulation}},
journal = {Imaging neuroscience (Cambridge, Mass.)},
year = {2026},
month = may,
volume = {4},
pages = {IMAG.a.1258},
publisher = {MIT Press},
issn = {2837-6056},
doi = {10.1162/imag.a.1258},
url = {https://doi.org/10.1162/imag.a.1258},
pmid = {42232075},
pmcid = {PMC13224316}
}

RIS

TY - JOUR
AU - Maaz, Jacob
AU - Dia, Alexandra
AU - Waroquier, Laurent
AU - Paban, Véronique
AU - Rey, Arnaud
TI - Non-specific increase in alpha power during a neurofeedback session targeting its downregulation
T2 - Imaging neuroscience (Cambridge, Mass.)
J2 - Imaging Neurosci (Camb)
PY - 2026
DA - 2026/05/29
VL - 4
SP - IMAG.a.1258
SN - 2837-6056
PB - MIT Press
DO - 10.1162/imag.a.1258
UR - https://doi.org/10.1162/imag.a.1258
LA - en
ER -

CSL-JSON

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