Published October 29, 2025 | Version V1.0
Dataset Open

Optimal Inter-Session Intervals in Neurofeedback Training: A Randomized Trial of Retention and Individual Response Patterns in Elite Judo Athletes

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Description

This repository contains de-identified data and analysis code from a randomized controlled trial in elite judo athletes comparing two neurofeedback (NFB) session spacings (48 h vs 72 h). The study evaluates (i) EEG dynamics focused on the Frontal Alpha Index (FAI) and individual alpha frequency (IAF), (ii) lower-limb strength performance across multiple relative loads (%1RM), and (iii) short-term retention of training effects. The dataset supports analyses of group-level effects, heterogeneity of individual responses (responder phenotypes), and growth/retention modeling.

Keywords: neurofeedback, EEG, alpha, FAI, IAF, motor performance, retention, training spacing, elite athletes, randomized trial.

Contents

  • /data/

    • EEG_timeseries_F3_F4_IAF.csv — session-wise EEG summaries (F3/F4 power around IAF), FAI (log-ratio), session indices, and pre/post/retention flags.

    • Strength_Squat_35_55_70_85_100.csv — strength outcomes (e.g., repetitions or derived performance metrics) at 35–100% 1RM measured pre/post within sessions.

    • Participants_Metadata.csv — pseudonymous IDs, group allocation (48 h / 72 h / control), training history, and basic anthropometrics.

    • Retention_Assessments.csv — retention measurements collected ~48 h or ~72 h after the last NFB exposure (EEG and strength).

  • /code/

    • 01_preprocessing_EEG.m — EEGLAB preprocessing pipeline.

    • 02_PSD_FAI_calc.m — power spectral density estimation and FAI computation.

    • 03_stats_LMM.R — mixed-effects models (group × session/time), planned contrasts, multiplicity control.

    • 04_growth_models.R — nonlinear growth/learning trajectories with model diagnostics.

    • 05_retention_models.R — decay/retention modeling and sensitivity analyses.

    • 06_figures.R — figure generation scripts (publication-grade output).

  • /docs/

    • README_variables.pdf — variable definitions, coding, and units.

    • DataDictionary.xlsx — machine-readable data dictionary.

    • CONSORT_flow.pdf — participant flow diagram.

    • Supplementary_Tables.xlsx — supplementary descriptive and model outputs.

 

Methods 

  • Participants. Elite judo athletes meeting high-performance training criteria. All data are de-identified and pseudonymized.

  • Design. Randomized allocation to two NFB spacing conditions (48 h vs 72 h) with an active or usual-practice control. Up to 15 sessions per participant; pre/post assessments embedded within sessions; terminal retention assessment after the final exposure.

  • EEG Protocol. Eyes-open/eyes-closed blocks per protocol; F3/F4 channels summarized around IAF; FAI computed as log-ratio of right/left frontal alpha power. Signal quality checks include artifact rejection and standardized PSD procedures.

  • Strength Testing. Lower-limb strength/performance assessed at multiple relative loads (%1RM). Standardized warm-up and familiarization; consistent testing order across sessions.

  • Statistical Analysis. Linear mixed-effects models for repeated measures; growth curves for within-participant learning; retention/decay models post-training; responder phenotyping; multiple-comparison adjustments (e.g., Holm/FDR). Model assumptions and diagnostics reported in scripts.

 

Variables 

  • Identifiers: participant_id (pseudonym), group (48h / 72h / control), session (1–15), timepoint (pre / post / retention).

  • EEG: IAF (Hz), alpha_power_F3, alpha_power_F4 (µV² within IAF±band), FAI = log(alpha_F4) − log(alpha_F3).

  • Strength: squat_metric_%1RM for 35/55/70/85/100 (see DataDictionary for exact units/definitions).

  • Compliance & Safety: adherence (%), adverse_event (0/1) with optional narrative field.

A complete, canonical specification is provided in DataDictionary.xlsx.

 

Quality Control

  • EEG preprocessing relies on reproducible EEGLAB/Matlab routines with fixed parameter seeds where applicable.

  • Outlier handling and exclusion rules (e.g., excessive artifacts, protocol deviations) are documented in the analysis scripts.

  • All inferential outputs include model diagnostics; sensitivity analyses are provided for key assumptions.

Files

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Additional details

Identifiers

Other
Institutional Bioethical Committee of the Academy of Physical Education in Katowice, Poland (ethics approval number: KB/11/2021).

Related works

Is supplement to
Dataset: 10.5281/zenodo.15911199 (DOI)

Dates

Collected
2024-11-05/2025-03-28
Date of data collection
Created
2025-04-02/2025-07-15
Data curation, de-identification, and analysis code development (preprocessing, PSD/FAI, LMM, growth/retention models).

Software

Programming language
Python , R , MATLAB

References

  • Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics. Journal of Neuroscience Methods, 134(1), 9–21. https://doi.org/10.1016/j.jneumeth.2003.10.009 Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 Ben-Shachar, M. S., Lüdecke, D., & Makowski, D. (2020). effectsize: Estimation of effect size indices and standardized parameters. Journal of Open Source Software, 5(56), 2815. https://doi.org/10.21105/joss.02815 Lenth, R. V. (2024). emmeans: Estimated Marginal Means, aka Least-Squares Means (R package version X.Y.Z). https://CRAN.R-project.org/package=emmeans R Core Team. (2025). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. The MathWorks, Inc. (2023). MATLAB R2023a. Natick, Massachusetts: The MathWorks, Inc. Wickham, H., Averick, M., Bryan, J., et al. (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686. https://doi.org/10.21105/joss.01686 Moher, D., Hopewell, S., Schulz, K. F., et al. (2010). CONSORT 2010 Explanation and Elaboration. BMJ, 340, c869. https://doi.org/10.1136/bmj.c869 Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B, 57(1), 289–300. Klimesch, W. (1999). EEG alpha and theta oscillations reflect cognitive and memory performance: A review and analysis. Brain Research Reviews, 29(2–3), 169–195. https://doi.org/10.1016/S0165-0173(98)00056-3 Coan, J. A., & Allen, J. J. B. (2004). Frontal EEG asymmetry as a moderator and mediator of emotion. Psychophysiology, 41(4), 453–464. Schulz, K. F., Altman, D. G., & Moher, D. (2010). CONSORT 2010 Statement: Updated guidelines for reporting parallel group randomized trials. BMJ, 340, c332. https://doi.org/10.1136/bmj.c332