Machine Learning in Brain-Computer Interfaces: A Pathway to Next-Generation Neuroprosthetics

Abstract

Brain-Computer Interfaces (BCIs) are revolutionizing the field of neuroprosthetics by providing direct communication pathways between the brain and external devices. The integration of Machine Learning (ML) techniques is critical for enhancing BCI performance, enabling more accurate signal interpretation, and improving user experience. This paper explores the role of ML in BCIs, the current challenges, advancements in technology, and the potential for next-generation neuroprosthetics. By addressing the intersection of neuroscience and artificial intelligence, this research highlights a promising avenue for restoring lost functions in individuals with neurological impairments.