Successful single-session neural self-regulation through neurofeedback varies between features

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