Xu Lichao, Xu Minpeng, Jung Tzyy-Ping, Ming Dong
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.
Cogn Neurodyn. 2021 Aug;15(4):569-584. doi: 10.1007/s11571-021-09676-z. Epub 2021 Apr 10.
A brain-computer interface (BCI) can connect humans and machines directly and has achieved successful applications in the past few decades. Many new BCI paradigms and algorithms have been developed in recent years. Therefore, it is necessary to review new progress in BCIs. This paper summarizes progress for EEG-based BCIs from the perspective of encoding paradigms and decoding algorithms, which are two key elements of BCI systems. Encoding paradigms are grouped by their underlying neural meachanisms, namely sensory- and motor-related, vision-related, cognition-related and hybrid paradigms. Decoding algorithms are reviewed in four categories, namely decomposition algorithms, Riemannian geometry, deep learning and transfer learning. This review will provide a comprehensive overview of both modern primary paradigms and algorithms, making it helpful for those who are developing BCI systems.
脑机接口（BCI）能够直接连接人类与机器，并且在过去几十年中已取得成功应用。近年来，许多新的BCI范式和算法不断涌现。因此，有必要回顾一下BCIs的新进展。本文从编码范式和解码算法这两个BCI系统的关键要素角度，总结了基于脑电图（EEG）的BCIs的进展。编码范式按其潜在的神经机制进行分类，即与感觉和运动相关、与视觉相关、与认知相关以及混合范式。解码算法分为四类进行综述，即分解算法、黎曼几何、深度学习和迁移学习。本综述将全面概述现代主要范式和算法，有助于BCI系统开发者。
