Full‐Stack Architectures for Intelligent Brain‐Computer Interfaces

ABSTRACT Brain–computer interfaces (BCIs) have made consistent advances in supporting motor and communication functions; nevertheless, their adoption in everyday environments remains constrained by enduring challenges, including chronic instability at the electrode–tissue interface, motion‐induced artifacts, inter‐user variability, and strict power and bandwidth limitations. To address these issues, recent work has increasingly focused on system‐level innovations encompassing electrode design, wireless communication strategies, and neural decoding algorithms. At the interface level, enhancements in electrochemical performance and mechanical compliance improve long‐term electrode–tissue coupling and help maintain signal integrity during naturalistic movement. For signal acquisition and transmission, miniaturized front‐end electronics and energy‐efficient telemetry architectures enable higher channel counts while minimizing power consumption and optimizing bandwidth utilization. In parallel, decoding approaches have evolved from static, feature‐based pipelines toward adaptive machine‐learning and deep‐learning methods that are more resilient to nonstationary neural signals and capable of supporting low‐latency, closed‐loop operation. This review consolidates findings from contemporary preclinical and human studies to provide a comprehensive perspective on system‐level engineering strategies for practical BCI technologies, emphasizing neural interface architecture and system‐design approaches that enhance signal stability and real‐world usability, while also identifying emerging design paradigms that may facilitate next‐generation BCIs with improved scalability and broader practical impact.