Machine-Learning-Powered Neural Interfaces for Smart Prosthetics and Diagnostics
It addresses the need for more efficient and reliable neural interfaces for assistive technologies and diagnostics, but it is incremental as it reviews existing advancements rather than presenting new research.
This paper reviews recent advancements in AI-driven decoding algorithms and energy-efficient System-on-Chip platforms for miniaturized neural devices, aiming to enhance applications like smart prosthetics and diagnostics by improving scalability, reliability, and user adaptability.
Advanced neural interfaces are transforming applications ranging from neuroscience research to diagnostic tools (for mental state recognition, tremor and seizure detection) as well as prosthetic devices (for motor and communication recovery). By integrating complex functions into miniaturized neural devices, these systems unlock significant opportunities for personalized assistive technologies and adaptive therapeutic interventions. Leveraging high-density neural recordings, on-site signal processing, and machine learning (ML), these interfaces extract critical features, identify disease neuro-markers, and enable accurate, low-latency neural decoding. This integration facilitates real-time interpretation of neural signals, adaptive modulation of brain activity, and efficient control of assistive devices. Moreover, the synergy between neural interfaces and ML has paved the way for self-sufficient, ubiquitous platforms capable of operating in diverse environments with minimal hardware costs and external dependencies. In this work, we review recent advancements in AI-driven decoding algorithms and energy-efficient System-on-Chip (SoC) platforms for next-generation miniaturized neural devices. These innovations highlight the potential for developing intelligent neural interfaces, addressing critical challenges in scalability, reliability, interpretability, and user adaptability.