Cognitive and brain function enhancement in Gen X group after personalized, AI supervised EEG-neurofeedback training

Abstract Background Interventions supporting medical care and enhancing quality of life in neurodegenerative or age-related cognitive decline are strongly needed. Electroencephalographic (EEG) neurofeedback can enable users to modulate their brain activity through real-time feedback. However, evidence for its clinical effectiveness remains inconclusive, partly due to limited personalization and insufficient task relevance in existing protocols. Objective We tested whether personalized EEG neurofeedback supervised by deep neural networks (DNNs) can enhance cognitive performance in older adults. Methods Fifty-seven healthy adults aged 41–64 (31 women), including a sham-feedback control group, completed a personalized neurofeedback protocol with DNNs fine-tuned to individual EEG patterns. The procedure included pre- and post-training assessments using a transitive reasoning task, three diagnostic sessions to adapt the DNN to each participant, and 10–11 neurofeedback sessions based on a gamified delayed-match-to-sample paradigm. Results The training group showed robust gains across all three variants of the reasoning task (each p < .01), whereas the sham group improved only on the easiest variant. Groups did not differ at pretest; however, at posttest the training group outperformed the sham group on all task conditions (each p < .03), showing also a larger neural effort (lower alpha band power) and increased beta and gamma band connectivity (higher phase lag index). Conclusion Personalized, task-oriented neurofeedback guided by individually fine-tuned DNNs can produce cognitive enhancement after relatively few sessions. The proposed Task-Pretrained, Subject-Finetuned Neurofeedback (TPSF-NF) framework is scalable to other cognitive domains in future research.