Neural Prostheses for Communication Restoration: Algorithmic, Systemic, and Clinical Perspectives

Brain-computer interfaces (BCIs) are transforming the lives of individuals with severe motor and speech impairments by restoring communication or movement control through direct decoding of neural activity. This review provides a comprehensive overview of communication BCIs, covering cursor&click, typing, handwriting, speech, and emotion decoding applications from algorithmic, system-level, and clinical perspectives. Recent advances in machine learning have improved the decoding of both semantic tokens (e.g., words) and linguistic primitives (e.g., letters), enabling context-aware reconstruction of coherent text and speech. Current BCI systems, typically implemented on bulky rack-mounted or bench-top platforms, can synthesize speech, handwriting, or typing at roughly half the speed of natural conversation. Future-generation BCIs, however, are being developed as implantable systems for safe and convenient everyday use. Yet, key challenges remain for real-world deployment, including safety, reliability, portability, user-friendliness, and naturalness, along with ethical considerations and societal implications. Addressing these challenges requires careful attention to patient-centered factors, such as target populations, task paradigms, and implantation sites, which guide translational development. Looking ahead, improving model adaptability, cross-user generalization, and hardware efficiency will be essential for realizing practical, scalable, and fully embodied neural prostheses.