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Frontal-midline theta (FM-theta) neurofeedback is a promising approach for enhancing executive control, yet fundamental questions remain regarding its learning dynamics and the sources of interindividual variability, including how FM-theta self-regulation develops over time and which factors shape neurofeedback responsiveness. In this first large-scale mega-analysis of EEG-based neurofeedback, raw participant-level data from five independent international FM-theta neurofeedback studies were aggregated (N = 168). Learning trajectories were assessed using session-to-session and within-session indices across training segments shared by all studies, and first-to-last session differences capturing study-specific training gains. Analyses were conducted for standard FM-theta (4-8 Hz) and individualized FM-theta centered on participant-specific executive control theta peaks, with frequency specificity evaluated against non-theta control bands. Neurofeedback outcomes were compared with those of an active control group. Individual predictors of neurofeedback success, and exploratory responder profiles were also examined. Participants receiving neurofeedback showed significantly greater FM-theta upregulation than the active control group for both standard and individualized bands, evident at both session-averaged and within-session levels. Learning effects emerged early, stabilized across sessions, and were expressed primarily as robust within-session modulation and reliable first-to-last session increases. Individualized FM-theta effects were more heterogeneous and study-dependent than standard FM-theta effects. Predictor analyses indicated that female sex and lower educational attainment were associated with greater neurofeedback success. Exploratory responder analyses revealed substantial interindividual variability, with non-responders more frequently reporting or suspecting psychiatric disorders. Together, these findings characterize FM-theta neurofeedback learning as an early-stabilizing, within-session-driven process and provide a framework for optimizing protocol design and future work.
Enriquez-Geppert et al. (Fri,) studied this question.