Machine Learning Identification of EEG Predictors of Load-Specific Strength Gains Following Alpha Neurofeedback in Elite Judokas

This dataset accompanies the study "Machine Learning Identification of EEG Predictors of Load-Specific Strength Gains Following Alpha Neurofeedback in Elite Judokas", which investigated whether baseline alpha-band EEG metrics can predict individual responsiveness to neurofeedback in the context of strength adaptation. Conducted on 24 elite male judokas, the study implemented a 15-session alpha neurofeedback intervention targeting frontal alpha asymmetry (FAI) with EEG recorded from F3 and F4 electrode sites. Strength outcomes were assessed pre- and post-intervention across five loading intensities (35–100% 1RM) using a standardized back squat protocol. The research was structured in two analytical stages. First, multivariate linear regression models identified load-specific EEG predictors (FAI, F3, F4) of strength gain. Principal Component Analysis (PCA) supported the dimensionality reduction of EEG data prior to modeling. Second, supervised machine learning classifiers Random Forest (RF) and Multi-Layer Perceptron (MLP), were trained to categorize athletes as responders or non-responders based on baseline EEG features and training status. Both classifiers demonstrated above-chance accuracy, with F4_PRE and FAI_PRE emerging as the most influential features. The dataset, titled "EEG Alpha Neurofeedback and Strength Adaptation – Judokas (2024)", contains preprocessed and standardized EEG metrics (F3_PRE, F4_PRE, FAI_PRE), delta strength outcomes (ΔReps per %RM), classification labels, and all R scripts used for regression, PCA, and machine learning analysis. EEG preprocessing included ICA artifact removal, z-score normalization, and amplitude thresholding (±75 µV). The entire analysis pipeline follows open science and FAIR principles. This is the first study to integrate PCA-regression and machine learning to derive EEG-based predictive markers of neuromuscular adaptation in elite strength athletes. The results support the application of individualized neurofeedback strategies guided by neurophysiological traits and establish a foundation for precision brain–performance interfaces in sport science.