Using Generative AI for Neurofeedback Content Personalization

This paper presents a comprehensive framework for integrating Large Language Models (LLMs) into neurofeedback systems. By leveraging real-time electroencephalogram (EEG) data processing, advanced machine learning, and multimodal feedback mechanisms, the framework offers a transformative approach to personalized neurofeedback. Technical validation and statistical analysis highlight system scalability and empirical reproducibility, addressing critical performance metrics such as latency, accuracy, and resource efficiency. The framework emphasizes the convergence of traditional neurofeedback principles with modern AI capabilities, demonstrating significant potential for applications in mental health, cognitive enhancement, and neurological rehabilitation.