基于脑电的运动想象脑机接口算法与技术：综述
EEG-Based Motor Imagery BCI Algorithms and Technologies: A Review
- Sharif University of Technology（谢里夫理工大学）
- KTH Royal Institute of Technology（KTH皇家理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文综述了基于脑电的运动想象脑机接口算法与硬件技术，涵盖AI方法及新兴融合技术，旨在推动高性能实时MI-BCI系统创新。
AI中文摘要：
脑机接口（BCI）已成为一种变革性技术，能够实现大脑与外部设备之间的直接通信。在各种BCI范式中，基于脑电（EEG）的运动想象（MI）因其简单性、非侵入性以及恢复运动功能和促进运动障碍患者康复的潜力而备受关注。本文全面综述了过去十年中用于解码大脑感觉运动皮层信号的最实用处理算法。具体而言，本文详细讨论了基于人工智能（AI）的算法，特别是机器学习和深度学习技术的集成，及其对提高MI-BCI系统性能和效率的贡献。此外，本文回顾了最先进的硬件平台和新兴融合技术，包括片上系统（SoC）架构、专用集成电路（ASIC）、现场可编程门阵列（FPGA）、可穿戴设备、物联网（IoT）以及增强现实/虚拟现实（AR/VR），并讨论了它们与先进信号处理算法的集成，以实现下一代MI-BCI系统。通过强调基于EEG的MI-BCI技术当前取得的成就，并预测能够进一步增强实时能力的未来研究方向，本文旨在为研究人员和从业者提供有价值的见解，促进高性能基于EEG的MI-BCI系统的创新。
英文摘要：
Brain-computer interfaces (BCIs) have emerged as transformative technologies that enable direct communication between the brain and external devices. Among various BCI paradigms, EEG-based motor imagery (MI) has gained prominence due to its simplicity, non-invasiveness, and potential to restore motor function and facilitate rehabilitation for patients with motor impairments. This paper presents a comprehensive review of the most practical processing algorithms developed over the past decade for decoding brain sensorimotor cortex signals. Specifically, this paper discusses the integration of artificial intelligence (AI)-based algorithms, particularly machine learning and deep learning techniques, and their contributions to improving the performance and efficiency of MI-BCI systems in detail. Furthermore, the paper reviews state-of-the-art hardware platforms and emerging converging technologies, including system-on-chip (SoC) architectures, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), wearable devices, the Internet of Things (IoT), and augmented/virtual reality (AR/VR), and discusses their integration with advanced signal processing algorithms to enable next-generation MI-BCI systems. By highlighting current achievements of EEG-based MI-BCI technology and predicting future research directions that could further enhance real-time capabilities, this paper aims to provide valuable insights for researchers and practitioners, fostering innovation in high-performance EEG-based MI-BCI systems.
