Individual neural dynamics of successful Gamma neuromodulation through EEG-neurofeedback in the aging brain

Gamma-band synchronization is a key mechanism for healthy cognitive function, yet it tends to decrease with age. EEG-based Neurofeedback (EEG-NF) is a promising tool enabling subjects to modulate their brain activity. However, its efficacy at the individual level remains unclear, which may partly explain the heterogeneity of neurofeedback outcomes. The primary objective of this study was to investigate individual neural dynamics of Gamma-band synchronization through EEG-NF training. We analyzed data from a double-blind, placebo-controlled trial using an EEG-based brain-computer interface, involving healthy older adults with subjective memory complaints, randomly assigned to a neurofeedback or a sham feedback group. Specifically, we employed a two-step unsupervised machine learning framework: first, epoch-based Agglomerative Hierarchical Clustering to identify individual-level response patterns, then Spectral Bi-Clustering to uncover higher-order structure at the population level. Results revealed a subgroup of individuals within the real neurofeedback condition who successfully enhanced their Gamma-band synchronization, with effects extending across the broader frequency spectrum. In contrast, the remaining participants in the neurofeedback group exhibited neural responses comparable to those observed in the sham group. This randomized controlled trial offers novel insights into the individual neural dynamics underlying successful Gamma EEG-NF training, highlighting its potential to promote healthy brain aging.