标题： 基于机器学习的神经接口在智能假肢和诊断中的应用 显示英文标题

标题： Machine-Learning-Powered Neural Interfaces for Smart Prosthetics and Diagnostics

摘要： 先进的神经接口正在改变从神经科学研究到诊断工具（用于心理状态识别、震颤和癫痫发作检测）以及假肢设备（用于运动和交流恢复）的应用。 通过将复杂功能集成到微型神经装置中，这些系统为个性化辅助技术和适应性治疗干预解锁了重大机会。 利用高密度神经记录、现场信号处理和机器学习（ML），这些接口提取关键特征，识别疾病神经标志物，并实现准确、低延迟的神经解码。 这种集成促进了神经信号的实时解读、大脑活动的自适应调节以及辅助设备的有效控制。 此外，神经接口与机器学习之间的协同作用为自主、无处不在的平台铺平了道路，这些平台能够在不同环境中以最低的硬件成本和外部依赖运行。 在这项工作中，我们回顾了人工智能驱动的解码算法和面向下一代微型神经装置的节能片上系统（SoC）平台的最新进展。 这些创新突显了开发智能神经接口的潜力，解决了可扩展性、可靠性、可解释性和用户适应性的关键挑战。 显示英文摘要

摘要： 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.