Synergizing Brain-Computer Interfaces and AI-Driven Image Segmentation for Precision Neurosurgery

Abstract

BCI and AI-driven image segmentation are revolutionizing precision neurosurgery by enhancing surgical accuracy, reducing human error, and improving patient outcomes. This review explores the integration of AI techniques, particularly DL and CNNs, with neuroimaging modalities for automated brain mapping and tissue classification. We analyze existing approaches for real-time neural signal processing, automated segmentation, and surgical robotics, highlighting their strengths, limitations, and clinical applications. The integration of hybrid BCI models with AI enhances neurorehabilitation by providing adaptive feedback for motor recovery and cognitive therapy. However, challenges such as signal reliability, computational latency, and ethical concerns regarding patient autonomy and data privacy persist. Furthermore, we discuss the role of AI in improving decision-making, intraoperative guidance, and post-surgical assessments. By synthesizing recent advancements in medical image processing, BCI technology, and AI-driven neurosurgical interventions, this paper provides a comprehensive overview of current trends, challenges, and future research directions in this rapidly evolving field.