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

Area of Science:

• Neuroscience
• Cognitive Science
• Biomedical Engineering

Background:

• Neurodegenerative and age-related cognitive decline necessitate effective interventions.
• Electroencephalographic (EEG) neurofeedback offers potential for modulating brain activity.
• Current neurofeedback methods lack personalization and task relevance, limiting clinical effectiveness.

Purpose of the Study:

• To evaluate the efficacy of personalized EEG neurofeedback, supervised by deep neural networks (DNNs), in enhancing cognitive performance in older adults.
• To investigate the impact of individually tailored neurofeedback protocols on cognitive function and neural activity.

Main Methods:

• Fifty-seven healthy adults (aged 41-64) participated in a personalized neurofeedback protocol using DNNs fine-tuned to individual EEG patterns.
• A control group received sham feedback.
• Cognitive performance was assessed using a transitive reasoning task before and after training, alongside EEG data analysis.

Main Results:

• The personalized neurofeedback group demonstrated significant improvements in all variants of the reasoning task (p < .01).
• The training group outperformed the sham group on all task conditions post-training (p < .03).
• Enhanced neural effort (reduced alpha power) and increased beta/gamma band connectivity were observed in the training group.

Conclusions:

• Individually fine-tuned DNN-guided neurofeedback can achieve cognitive enhancement in older adults with minimal sessions.
• The Task-Pretrained, Subject-Finetuned Neurofeedback (TPSF-NF) framework shows promise for scalability to other cognitive domains.