Data and code from: Successful single-session neural self-regulation through neurofeedback varies between features

Dataset DOI: 10.5281/zenodo.18266174

Dataset Overview

This repository contains the pre-processed EEG data supporting the findings of the pre-print "Successful single-session neural self-regulation through neurofeedback varies between features" published open access at Human Brain Mapping with the doi: 10.1002/hbm.70611 and as a pre-print with the doi:  10.64898/2026.01.07.698228. The dataset is organized at the single-subject level, provided in MATLAB (.mat) format. Each subject file contains time-frequency resolved data separated by feature, allowing for modular analysis. The data has been pre-processed and downsampled to 100 Hz, resulting in a three-dimensional array structure of 40 × 62 × 19,100 per feature. In addition, we share supporting information, and the code used to reproduce the results in the manuscript.

Description of the data and file structure

Data and computer code for "Successful single-session neural self-regulation through neurofeedback varies between features"

Files and variables

File: feedback_sessions.zip

Description: The main data file (see below for the complete archived repository including data overviews and computer code).

Variables:

The feedback_sessions.zip file contains individual neurofeedback files for each participant and session following this formatS(N) + Feature + feedbackPower.mat each file contains the following variables:

• feedbackPowerASRdB: A three dimensional matrix of the size 40 x 62 x 19,100 corresponding to frequencies 1-40, 62 channels, and, 19,100 samples.
• nftChanlocs: MATLAB structure that contains the channel names and locations.
• nftChannels: Vector with indices to the channels which feedback was based on.
• nftFreqs: Vector with indices to the frequencies which feedback was based on.

File: demographics.csv

Description: Demographic information about the participants, with the variables: ID, age, gender, handedness, and education.

Various files: aggregated_data.zip

Description: For convenience, we also provide some of the aggregated data generated by the shared code files in the repository.

Software

MATLAB (version R2022b) was used for pre-processing of raw EEG data. Analyses were performed with R (version 4.2.1), JOPS package (version 0.1.2), dtwclust package (version 5.5.11), eegUtils package (version 0.4.6), and mgcv package (version 1.9-1).

Code

Pre-process the raw EEG files

finalPreprocessNFT.m

• Inputs
  • Feature.mat Raw EEG files
• Outputs
  • S + ID + Feature + avgFeedbackPowerdBASR.mat
  • S + ID + Feature + avgFeedbackPower.mat
• Dependencies
  • fExtractFeedbackPowerWaveletCompletedB.m
  • extractFeedbackSamples.m #
  • blocks2samples.m # Blocks to sample indices
  • laplacian_perrinX.m # Spatial filter

Export averaged EEG power to a text file for further analysis

export_EEG_PowerforAnalysis.m

• Inputs
  • S + ID + Feature + avgFeedbackPowerdBASR.mat
• Outputs
  • newPipelineAvgPowerdB + Feature + N20ASR.txt

Fit B-spline models with V-curve smoothing of power time-series data and compute Bayesian CIs for each participants and feature, save results, especially coefficients, for further analysis

smoothV-CurvSplineClustering.R

• Inputs
  • newPipelineAvgPowerdB + Feature + N20ASR.txt
• Outputs
  • Feature + CoeffsSmoothV127.rds
  • Feature + FitSmoothV127.rds

Cluster the learners

clusteringLearners.R

• Inputs
  • Feature + CoeffsSmoothV127.rds
  • Feature + FitSmoothV127.rds
• Outputs
  • Feature + Clusters.csv

Export averaged EEG power (per feature) for all EEG channels to a text file for further analysis

exportAvgNFTbyChannel.m

• Inputs
  • S + ID + Feature + feedbackPower.mat
• Outputs
  • S + ID + Feature + NFTbyChannel.txt

Model coefficients by cluster with mgcv package (bam), save data for plots with topoplot and trendlines

smoothModelsForClustersCoeffsAndToposFINAL.R

• Inputs
  • Feature + Clusters.csv
  • Feature + NFTbyChannel.txt
  • chanlocs.txt
• Outputs
  • Feature + CoeffsbyClusters2.csv
  • Feature + ToposbyBlockandClusters2.csv
• Dependencies
  • flipSMRAmplitudesLaterallyAndEvaluate.R

Create plots for each feature displaying topoplots and trendlines for the clusters

plotCoeffsandTopoplotsByClusteredData.m

• Inputs
  • Feature + NFTsettings.mat
  • Feature + Clusters.csv
  • Feature + CoeffsbyClusters2.csv
  • Feature + ToposbyBlockandClusters2.csv
• Outputs
  • Figure3 + Feature + .png
  • Figure3 + Feature + .svg

Create plots for each feature displaying change between first and last block for each frequency band in the controlled channels

figureChangeLastblockVsThreshold.R

• Inputs
  • S + ID + Feature + NFTbyChannelFrequency.txt
  • Feature + Clusters.csv
• Outputs
  • Feature + FreqBandChangeFINAL2.svg