ADVANCEMENTS IN BRAIN-COMPUTER INTERFACES: THE IMPACT OF AI AND GENERATIVE MODELS ON NEURAL DECODING AND APPLICATION

Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices, offering transformative potential for individuals with neurological disorders and beyond. Recent advancements in artificial intelligence (AI), particularly in generative models, have significantly improved the accuracy and efficiency of neural decoding in BCIs. This paper provides a comprehensive overview of these advancements, focusing on the role of AI and generative models in enhancing BCI performance. I discuss the contributions of leading companies like Neuralink, Synchron, and Paradromics, and analyze key research findings on signal processing, machine learning algorithms, and ethical considerations. My synthesis of current literature and industry insights highlights the transformative potential of AI-driven BCIs while addressing the technical and ethical challenges that need to be overcome to fully realize their potential. I emphasize the importance of continued research in refining generative AI models, enhancing real-time processing capabilities, and establishing ethical frameworks to guide the development and application of BCIs.