Optimizing Session Frequency in EEG Biofeedback: A Comparative Study of Protocol Dynamics and Neuromuscular Adaptation in Elite Judo Athletes.

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Paper

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The authors' code

Python · 51 lines · 1.7 KB · CC-BY-4.0

# File: /scripts/preprocessing/clean_and_normalize.py
"""Data cleaning and normalization pipeline.
Steps performed:
1. Load raw Excel file from ../../data/raw/
2. Select final session (Sesja == 15)
3. Compute derived variables (RMS_pre/post, MF_pre/post, Delta_EMG, EEG_SUM, SILA_SUM)
4. Z‑score normalization within group
5. Save cleaned dataset to ../../data/processed/EMG_EEG_strength.csv
"""
import pandas as pd
from pathlib import Path
import numpy as np
RAW_PATH = Path('../../data/raw/Macierz_EEG_EMG_Rozszerzona_AG_MG_POPRAWIONA.xlsx')
SAVE_PATH = Path('../../data/processed/EMG_EEG_strength.csv')
df_raw = pd.read_excel(RAW_PATH)
# Filter to final session
df = df_raw[df_raw['Sesja'] == 15].copy()
# Subject ID
df['Subject'] = df['Grupa'] + '_' + df['Zawodnik'].astype(str)
# Core variables
df['RMS_pre'] = df['F4_PRE']
df['RMS_post'] = df['F4_POST']
df['MF_pre'] = df['FAI_PRE']
df['MF_post'] = df['FAI_POST']
df['Delta_EMG'] = (df['RMS_post'] - df['RMS_pre']) / df['RMS_pre'] * 100
df['EEG_SUM'] = df[['F3_PRE', 'F4_PRE']].mean(axis=1)
df['SILA_SUM'] = df[['Przysiady_55%_PRZED', 'Przysiady_85%_PRZED']].mean(axis=1)
# Keep operands
cols = ['Subject', 'Grupa', 'RMS_pre', 'RMS_post', 'MF_pre', 'MF_post',
'Delta_EMG', 'EEG_SUM', 'SILA_SUM']
df_proc = df[cols].rename(columns={'Grupa': 'Group'})
# Z‑score normalization within Group
numeric_cols = df_proc.columns.difference(['Subject', 'Group'])
df_proc[numeric_cols] = df_proc.groupby('Group')[numeric_cols].transform(
lambda x: (x - x.mean()) / x.std(ddof=0))
# Save
SAVE_PATH.parent.mkdir(parents=True, exist_ok=True)
df_proc.to_csv(SAVE_PATH, index=False)
print('Processed data saved to', SAVE_PATH)

clean_and_normalize.py, under CC-BY-4.0 · at the source

Overview

Authors: Alicja Markiel¹, Dariusz Skalski², Kinga Łosińska², Marcin Żak¹, Adam Maszczyk^1,2

ORCID iDs: Kinga Łosińska, Adam Maszczyk

Institutions: Akademii Wychowania Fizycznego im. Jerzego Kukuczki w Katowicach (Poland); Gdansk University of Physical Education and Sport (Poland)

Journal: Sensors (Basel, Switzerland), volume 26, issue 7, article 2077

Dates: received 23 February 2026; accepted 24 March 2026; published online 26 March 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.3390/s26072077 · PMID 41977863 · PMCID PMC13074261 · OpenAlex W7141718731

Open access: gold, a free copy (OpenAlex)

Status: code verified (how the code is found and verified)

Categories: EEG (modality), other (modality), human (organism), methods / tools (subfield)

Methods: Spectral & time-frequency, Preprocessing, Statistics, Smoothing, state filtering, decompositions, Connectivity, Physiology & signal measures

Keywords: dose–response relationship, cortical plasticity, motor learning consolidation, fatigue resistance, frontal alpha index, motor unit recruitment, biosensor validation, individual responder phenotypes

MeSH: Adaptation, Physiological*, Athletes*, Biofeedback, Psychology*, Electroencephalography*, Martial Arts*, Neurofeedback*, Adult, Electromyography, Humans, Male, Young Adult (* major topic)

Topic: Transcranial Magnetic Stimulation Studies (Neurology, Neuroscience), according to OpenAlex

Funding: Gdansk University of Physical Education and Sport

Citations: not cited yet (Europe PMC); 44 references in the paper

Abstract

Highlights: What are the main findings?

Daily EEG biofeedback protocols produced superior neuromuscular and cortical adaptations in elite judo athletes compared to every-other-day and every-third-day protocols, with the highest post-intervention values in Frontal Alpha Index (FAI), strength (Sth_SUM), and fatigue resistance (MF_drop = −2.15 Hz), demonstrating a clear dose–response relationship for session frequency.

EMG amplitude (RMS) magnitude alone does not reflect adaptation quality: the low-frequency group exhibited the largest pre–post RMS increase (+17.44 μV) yet demonstrated inferior strength gains, fatigue resistance, and maladaptive EMG–load dynamics (r = −0.14) compared to daily training, indicating that neuromuscular efficiency rather than signal amplitude determines functional outcomes.

What are the implications of the main findings?

For high-performance sport applications, short daily neurofeedback sessions (5 min) are preferable to less frequent protocols when targeting strength, power, and fatigue resistance, enabling practitioners to maximize return on neurotechnological investment while minimizing training time disruption in elite athletic environments.

Integrated wireless EEG-EMG biosensor systems with active electrode technology, high temporal synchronization (±2 ms precision), and real-time signal quality monitoring (SNR > 18 dB, artifact rejection < 10%) are technically feasible for elite athletic populations, providing a validated methodological framework for next-generation wearable performance monitoring platforms requiring multi-modal sensor fusion, motion artifact suppression, and field-deployable architectures.

Abstract: Background: The optimal frequency of EEG biofeedback sessions for elite athletes remains unclear, despite growing adoption of neurofeedback in high-performance sport. Methods: This randomized, controlled study compared three EEG biofeedback protocols (daily, every-other-day, every-third-day) in 24 national-level male judo athletes stratified into three phenotypic groups. Each protocol comprised 15 standardized sessions. Pre- and post-intervention assessments included functional indices (strength, power) and neurophysiological measures (Frontal Alpha Index, EMG amplitude/RMS, corrected strength sum). Biosensor performance was validated via signal quality metrics. Results: Daily EEG biofeedback produced superior improvements in strength, FAI, and fatigue resistance. Although LRG showed the largest pre–post RMS increase (+17.44 μV vs. +16.54 μV in HRG), HRG maintained the highest post-intervention RMS values and best fatigue resistance (MF_drop = −2.15 Hz). Significant group × time interactions were observed for FAI (p = 0.027) and RMS (p = 0.019). Every-other-day protocols yielded moderate gains, while every-third-day protocols produced minimal or maladaptive EMG–load dynamics. A robust dose–response relationship was evident. Conclusions: Session frequency is critical for optimizing neurofeedback interventions in elite athletes. Daily EEG biofeedback confers superior adaptation compared to less frequent dosing.

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

Repository

Its files are read in the Code ↔ Paper reader above.

Zenodo 15879449

License: CC-BY-4.0

State: the link answers, verified on 30 September 2026

Evidence: files inventoried

Languages: Python (2), R (2), SPSS (1)

Size: 16 files, 5 scripts

Software Heritage: not checked

Found in: the text, “2.6. Data Processing and Quality Control”

Holds: README, environment (requirements.txt)

Not found: license file, CITATION.cff, tests, continuous integration, documentation

Tools: pandas (2 files), emmeans (1 file), lme4 (1 file), NumPy (1 file), scikit-learn (1 file), tidyverse (1 file)

Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)

30 September 2026: the link answers (HTTP 200)

6 files

clean_and_normalize.py, Python, 51 lines
lme_RMS.R, R, 14 lines
pca_clustering.py, Python, 34 lines
raincloud_RMS.R, R, 21 lines
repeated_measures_anova.sps, SPSS, 7 lines
README.md, Text, 23 lines

At the source:

The paper's code and data availability statement is in the Data section.

Tracing map

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Data

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Data Availability Statement

The raw data, analytic scripts, and Supplementary Materials are publicly available in an open-access repository (Zenodo, DOI: 10.5281/zenodo.15879450, accessed on 22 February 2026), ensuring full transparency and reproducibility in accordance with best practices for high-impact experimental research. The code and detailed analysis pipeline, including R and Python scripts, are version-controlled and documented to facilitate replication and extension by future researchers. Due to ethical restrictions and participant confidentiality obligations, the complete raw dataset containing individual athlete identifiers and sensitive health information is not publicly available but is accessible upon reasonable request from the corresponding authors, subject to institutional data governance protocols and institutional review board approval.

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

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 8 keywords, 11 MeSH terms, 1 funder, 44 references.

Cite

This paper

Markiel, A., Skalski, D., Łosińska, K., Żak, M., & Maszczyk, A. (2026). Optimizing Session Frequency in EEG Biofeedback: A Comparative Study of Protocol Dynamics and Neuromuscular Adaptation in Elite Judo Athletes. Sensors (Basel, Switzerland), 26(7), 2077. https://doi.org/10.3390/s26072077

BibTeX

@article{markiel2026optimizing, author = {Markiel, Alicja and Skalski, Dariusz and Łosińska, Kinga and Żak, Marcin and Maszczyk, Adam}, title = {{Optimizing Session Frequency in EEG Biofeedback: A Comparative Study of Protocol Dynamics and Neuromuscular Adaptation in Elite Judo Athletes}}, journal = {Sensors (Basel, Switzerland)}, year = {2026}, month = mar, volume = {26}, number = {7}, pages = {2077}, publisher = {Multidisciplinary Digital Publishing Institute (MDPI)}, issn = {1424-8220}, doi = {10.3390/s26072077}, url = {https://doi.org/10.3390/s26072077}, pmid = {41977863}, pmcid = {PMC13074261} }

RIS

TY - JOUR AU - Markiel, Alicja AU - Skalski, Dariusz AU - Łosińska, Kinga AU - Żak, Marcin AU - Maszczyk, Adam TI - Optimizing Session Frequency in EEG Biofeedback: A Comparative Study of Protocol Dynamics and Neuromuscular Adaptation in Elite Judo Athletes T2 - Sensors (Basel, Switzerland) J2 - Sensors (Basel) PY - 2026 DA - 2026/03/26 VL - 26 IS - 7 SP - 2077 SN - 1424-8220 PB - Multidisciplinary Digital Publishing Institute (MDPI) DO - 10.3390/s26072077 UR - https://doi.org/10.3390/s26072077 LA - en ER -

CSL-JSON

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