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Alpha power increases spontaneously during a neurofeedback session.

Code ↔ Paper

4 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 4 matches
  1. [1] § Methods › Statistical analyses and hypotheses testing › EEG data ↔ statistical analyses/ANS_BFsynth_wf.R, lines 1–42 · score 0.78 · brms syntax, multilevel models, full model, sensitive, Bayesian, blocks
  2. [2] § Methods › Statistical analyses and hypotheses testing › EEG data ↔ statistical analyses/ANS_BFsynth.R, lines 1–54 · score 0.78 · brms syntax, multilevel models, full model, sensitive, Bayesian, blocks
  3. [3] § Methods › EEG online processing ↔ material/ANS_set_EEG_recording_params.m, lines 86–110 · score 0.55 · 1–20 Hz, 8–12 Hz, window, filtering, online, band
  4. [4] § Methods › Statistical analyses and hypotheses testing › Feeling of feedback control and feedback credibility ↔ statistical analyses/ANS_stats4qreps.R, lines 85–166 · score 0.53 · random feedback variations, 1–5, BF01, belief, Genuine, Sham

Paper

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The authors' code

R · 189 lines · 8 KB · no license · 1 match

  1. ############################################################## ANS_BFsynth_wf.R
  2. # So far, we computed Bayes factors from 72 Bayesian multilevel models. Each
  3. # model was specific to one from 12 DV, to one from 3 random seeds, and to one
  4. # from 2 possible sets of priors. Indeed, we use 2 possible prior effect sizes
  5. # for the effects of interest (sensitivity analysis).
  6. # These models have been executed on the neurofeedback "evaluation" blocks. Here
  7. # is the full model equation (brms syntax):
  8. # Power ~ Trial*Task + (1 + Trial | Subject)
  9. # From each model, a dataframe resuming the results in terms of BF10s for the 5
  10. # effects of interest has been written. Here, we load each of these dataframes
  11. # and synthetise the results in one single data frame for all.
  13. ### Libraries ----
  14. library(readr)
  15. library(dplyr)
  17. ### Set WD and parameters (modify the XXXX part) ----
  18. wd <- "XXXX/statistical analyses/"
  19. setwd(wd)
  21. # Number of possible DV, seeds and possible priors
  22. ndv <- 12
  23. nsim <- 3
  24. npr <- 1; bs <- c("1")
  26. # DV names
  27. dvnames <- c(
  28. "Fz_Theta","Fz_Alpha","Fz_SMR","Fz_HighBeta",
  29. "Cz_Theta","Cz_Alpha","Cz_SMR","Cz_HighBeta",
  30. "Pz_Theta","Pz_Alpha","Pz_SMR","Pz_HighBeta"
  31. )
  33. # Effects names and number
  34. effnames_inmod <- c(
  35. "Trial", "TaskcNFvsSham","TaskcShamvsPass",
  36. "Trial:TaskcNFvsSham","Trial:TaskcShamvsPass"
  37. )
  38. effnames <- c(
  39. "Trial","Task - NF vs Sham","Task - Sham vs Passive",
  40. "Trial:Task - NF vs Sham","Trial:Task - Sham vs Passive"
  41. )
  42. npar <- length(effnames)
  44. # Possible seeds
  45. seeds10 <- c(300,1280,123,526,486,1284,242,666,814,956)
  47. # Deduce total number of rows for output df
  48. rtot <- ndv*nsim*npr*npar
  49. rdv <- nsim*npar # nb rows per dv
  51. # Initiate output df
  52. BFresults <- data.frame(matrix(NA,nrow = rtot,ncol = 11))
  53. names(BFresults) <- c("Parameter","Estimate","SE","Lower","Upper","Rhat","BF10","BF10+","DV","Seed","Prior SD")
  55. ### Let's do it ----
  56. i_models <- 0
  57. for (i in 1:ndv){ # i<-1
  58. dvlab <- dvnames[i]
  60. for (j in 1:length(bs)){ # j<-1
  61. for (k in 1:nsim){ # k<-1 ; j<-1;i<-1
  62. ### Load corresponding input ----
  63. inpath <- paste0(wd,"BF_tables/eval/",bs[j],"/",dvlab,"_BFs_out",k,".csv")
  64. buf <- read_delim(inpath,";",escape_double = F, trim_ws = T,
  65. show_col_types = F,locale = locale(decimal_mark = ","))
  67. ### Deduce corresponding start and end indexes ----
  68. i_models <- i_models + 1
  69. i_end <- npar*i_models
  70. i_start <- i_end-npar+1
  72. ### Do corresponding transfer ----
  73. BFresults[i_start:i_end,1:8] <- buf
  74. BFresults[i_start:i_end,9] <- dvlab
  75. BFresults[i_start:i_end,10] <- seeds10[k]
  76. BFresults[i_start:i_end,11] <- bs[j]
  77. BFresults[i_start:i_end,1] <- effnames
  78. }
  79. }
  80. }
  81. BFresultsf <- BFresults %>%
  82. tidyr::separate(DV, into = c("Electrode","Frequency-band"), sep = "_") %>%
  83. mutate(
  84. BF10 = ifelse(BF10 > 100, "> 100", as.character(BF10)),
  85. `BF10+` = ifelse(`BF10+` > 100, "> 100", as.character(`BF10+`)),
  86. `Frequency-band` = ifelse(`Frequency-band` == "HighBeta","Beta", as.character(`Frequency-band`))
  87. ) %>%
  88. relocate(`Frequency-band`,Electrode,Parameter,Estimate,SE,Lower,Upper,Rhat,
  89. BF10,`BF10+`,Seed,`Prior SD`)
  90. write.csv2(BFresultsf, file = paste0(wd,"BF_tables/eval/All_results.csv"), row.names=FALSE)
  91. write.csv2(BFresults, file = paste0(wd,"BF_tables/eval/All_results2plot.csv"), row.names=FALSE)
  94. ### Resume results to report them ------------
  95. results_report <- BFresults %>%
  96. dplyr::select(-Rhat,-SE) %>%
  97. mutate(across(c(Estimate,Lower,Upper,BF10,`BF10+`),as.numeric)) %>%
  98. group_by(DV,Parameter,`Prior SD`) %>%
  99. # Synthetize results
  100. summarize(
  101. Estimate = mean(Estimate),
  102. Lower = min(Lower),
  103. Upper = max(Upper),
  104. `Maximal BF10` = max(BF10),
  105. `Minimal BF10` = min(BF10),
  106. BF10 = mean(BF10),
  107. `Maximal BF10+` = max(`BF10+`),
  108. `Minimal BF10+` = min(`BF10+`),
  109. `BF10+` = mean(`BF10+`)
  110. ) %>% ungroup() %>%
  111. mutate(across(where(is.numeric), \(x) round(x,digits=3))) %>%
  112. tidyr::separate(DV, into = c("Electrode","Frequency-band"), sep = "_") %>%
  113. mutate(
  114. BF10 = ifelse(BF10 > 100, "> 100", as.character(BF10)),
  115. `BF10+` = ifelse(`BF10+` > 100, "> 100", as.character(`BF10+`)),
  116. `Minimal BF10` = ifelse(`Minimal BF10` > 100,"> 100", as.character(`Minimal BF10`)),
  117. `Maximal BF10` = ifelse(`Maximal BF10` > 100,"> 100", as.character(`Maximal BF10`)),
  118. `Minimal BF10+`= ifelse(`Minimal BF10+` > 100,"> 100", as.character(`Minimal BF10+`)),
  119. `Maximal BF10+`= ifelse(`Maximal BF10+` > 100,"> 100", as.character(`Maximal BF10+`)),
  120. `Frequency-band` = ifelse(`Frequency-band` == "HighBeta","Beta", as.character(`Frequency-band`))
  121. ) %>%
  122. relocate(c(`Frequency-band`,Electrode, Parameter,Estimate,Lower,Upper,BF10,
  123. `Minimal BF10`,`Maximal BF10`,`BF10+`,`Minimal BF10+`,`Maximal BF10+`,`Prior SD`))
  124. results_report$`Frequency-band` <- factor(results_report$`Frequency-band`, levels = c(
  125. "Theta","Alpha","SMR","Beta"
  126. ))
  127. results_report$Electrode <- factor(results_report$Electrode, levels = c("Fz","Cz","Pz"))
  128. results_report$Parameter <- factor(results_report$Parameter, levels = effnames)
  129. results_report <- results_report %>% arrange(Electrode,`Frequency-band`, Parameter) %>% ungroup()
  130. write.csv2(results_report, file = paste0(wd,"BF_tables/eval/Resume_results.csv"),
  131. row.names=FALSE)
  133. ### Extract results for Trial effect -----------
  134. results_trial <- results_report %>%
  135. filter(Parameter == "Trial") %>% select(-Parameter)
  136. write.csv2(results_trial, file = paste0(wd,"BF_tables/eval/Resume_results_trial.csv"),
  137. row.names=FALSE)
  139. ### Extract results for Task effect (NF vs. Sham) -----------
  140. results_TcNFvsSham <- results_report %>%
  141. filter(Parameter == "Task - NF vs Sham") %>% select(-Parameter)
  142. write.csv2(results_TcNFvsSham, file = paste0(wd,"BF_tables/eval/Resume_results_TcNFvsSham.csv"),
  143. row.names=FALSE)
  145. ### Extract results for Task effect (Sham vs. Passive) -----------
  146. results_TcShamvsPass <- results_report %>%
  147. filter(Parameter == "Task - Sham vs Passive") %>% select(-Parameter)
  148. write.csv2(results_TcShamvsPass, file = paste0(wd,"BF_tables/eval/Resume_results_TcShamvsPass.csv"),
  149. row.names=FALSE)
  151. ### Extract results for Trial x Task (NF vs. Sham) interaction effect -----------
  152. results_trialxTcNFvsSham <- results_report %>%
  153. filter(Parameter == "Trial:Task - NF vs Sham") %>% select(-Parameter)
  154. write.csv2(results_trialxTcNFvsSham, file = paste0(wd,"BF_tables/eval/Resume_results_trialxTcNFvsSham.csv"),
  155. row.names=FALSE)
  157. ### Extract results for Trial x Task (Sham vs. Passive) interaction effect -----------
  158. results_trialxTcShamvsPass <- results_report %>%
  159. filter(Parameter == "Trial:Task - Sham vs Passive") %>% select(-Parameter)
  160. write.csv2(results_trialxTcShamvsPass, file = paste0(wd,"BF_tables/eval/Resume_results_trialxTcShamvsPass.csv"),
  161. row.names=FALSE)
  164. ### Extract results for Theta band -------------------
  165. results_theta <- results_report %>%
  166. filter(`Frequency-band` == "Theta") %>%
  167. select(-`Frequency-band`)
  168. write.csv2(results_theta,file = paste0(wd,"BF_tables/eval/Resume_results1band_theta.csv"))
  170. ### Extract results for Alpha band -------------------
  171. results_alpha <- results_report %>%
  172. filter(`Frequency-band` == "Alpha") %>%
  173. select(-`Frequency-band`)
  174. write.csv2(results_alpha,file = paste0(wd,"BF_tables/eval/Resume_results1band_alpha.csv"))
  177. ### Extract results for SMR -------------------
  178. results_SMR <- results_report %>%
  179. filter(`Frequency-band` == "SMR") %>%
  180. select(-`Frequency-band`)
  181. write.csv2(results_SMR,file = paste0(wd,"BF_tables/eval/Resume_results1band_SMR.csv"))
  184. ### Extract results for Beta band -------------------
  185. results_beta <- results_report %>%
  186. filter(`Frequency-band` == "Beta") %>%
  187. select(-`Frequency-band`)
  188. write.csv2(results_beta,file = paste0(wd,"BF_tables/eval/Resume_results1band_beta.csv"))

ANS_BFsynth_wf.R, no license · at the source

Overview

Authors: Jacob Maaz1,2,3, Laurent Waroquier4, Alexandra Dia1,2,3, Véronique Paban1,2, Arnaud Rey1,3
ORCID iDs: Jacob Maaz, Alexandra Dia
  1. Aix Marseille Univ, CNRS, CRPN,Marseille, France
  2. Institute Neuro-Marseille, NeuroSchool, 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: Aix-Marseille Université (France)
Journal: Communications psychology, volume 4, issue 1, article 75
Dates: received 17 September 2025; accepted 19 February 2026; published online 12 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s44271-026-00431-w · PMID 41820645 · PMCID PMC13125214 · OpenAlex W7135028699
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: EEG (modality), cognitive (subfield)
Methods: Spectral & time-frequency, Preprocessing, Statistics, Smoothing, state filtering, decompositions, Physiology & signal measures
Keywords: Cognitive neuroscience, Psychology, Electroencephalography - EEG
Topic: Neural and Behavioral Psychology Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Agence Nationale de la Recherche (French National Research Agency) (ANR-16-CONV-0002, HEBBIAN ANR-23-CE28-0008); Doctoral fellowship of the French Ministry of Higher Education, Research, and Innovation; Fond de dotation JANSSEN HORIZON (grant numbers CNRS SPV/MB/214535)
Citations: cited by 1 paper (Europe PMC); 88 references in the paper

Abstract

Electroencephalographic neurofeedback (EEG-NF) has been proposed as a promising technique to modulate brain activity through real-time EEG-based feedback. Alpha neurofeedback in particular is believed to induce rapid self-regulation of brain rhythms, with applications in cognitive enhancement and clinical treatment. However, whether this modulation reflects specific volitional control or non-specific influences remains unresolved. In a preregistered, double-blind, sham-controlled study, we evaluated alpha upregulation in healthy participants receiving either genuine (n = 30) or sham (n = 30) EEG-NF during a single-session design. A third arm composed of a passive control group (n = 32) was also included to differentiate between non-specific influences related or not to the active engagement in EEG-NF. Throughout the session, alpha power increased robustly, yet independently of feedback veracity, engagement in self-regulation, or feedback update frequency. Parallel increases in theta and sensorimotor rhythms further suggest broadband non-specific modulation. Importantly, these results challenge the foundational assumption of EEG-NF: that feedback enables volitional EEG control. Instead, they point to spontaneous repetition-related processes as primary drivers, calling for a critical reassessment of neurofeedback efficacy and its underlying mechanisms.

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 4 matches between paragraphs and lines of code.

OSF wevtz

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: MATLAB (8), R (5)
Size: 25 files, 13 scripts
Software Heritage: not checked
Found in: “Code availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (5 files), Parallel Computing Toolbox (4 files), Psychtoolbox (4 files), Signal Processing Toolbox (3 files), patchwork (3 files), easystats (2 files), afex (1 file), BayesFactor (1 file), brms (1 file), broom (1 file), cowplot (1 file), ggplot2 (1 file), ggpubr (1 file), reshape2 (1 file), Stan (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
13 files
At the source: osf.io/wevtz

Code availability

All material and analysis codes are available via the OSF at: https://osf.io/wevtz.

Reproduced under the paper's license (CC BY), from the paper cited above.

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;
  • 13 scripts, each with its path and the digest of its content;
  • 4 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.

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Data availability

All data are available via the OSF at: https://osf.io/wevtz.

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, 5 authors, 3 keywords, 3 funders, 81 references.

Cite

This paper

Maaz, J., Waroquier, L., Dia, A., Paban, V., & Rey, A. (2026). Alpha power increases spontaneously during a neurofeedback session. Communications psychology, 4(1), 75. https://doi.org/10.1038/s44271-026-00431-w

BibTeX

@article{maaz2026alpha,
author = {Maaz, Jacob and Waroquier, Laurent and Dia, Alexandra and Paban, Véronique and Rey, Arnaud},
title = {{Alpha power increases spontaneously during a neurofeedback session}},
journal = {Communications psychology},
year = {2026},
month = mar,
volume = {4},
number = {1},
pages = {75},
publisher = {Nature Publishing Group},
issn = {2731-9121},
doi = {10.1038/s44271-026-00431-w},
url = {https://doi.org/10.1038/s44271-026-00431-w},
pmid = {41820645},
pmcid = {PMC13125214}
}

RIS

TY - JOUR
AU - Maaz, Jacob
AU - Waroquier, Laurent
AU - Dia, Alexandra
AU - Paban, Véronique
AU - Rey, Arnaud
TI - Alpha power increases spontaneously during a neurofeedback session
T2 - Communications psychology
J2 - Commun Psychol
PY - 2026
DA - 2026/03/12
VL - 4
IS - 1
SP - 75
SN - 2731-9121
PB - Nature Publishing Group
DO - 10.1038/s44271-026-00431-w
UR - https://doi.org/10.1038/s44271-026-00431-w
LA - en
ER -

CSL-JSON

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