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）可以直接连接人和机器，并在过去几十年中取得了成功的应用。近年来已经开发了许多新的 BCI 范式和算法。因此，有必要回顾 BCI 的新进展。本文从编码范例和解码算法的角度总结了基于 EEG 的 BCI 的进展，这是 BCI 系统的两个关键要素。编码范式按其潜在的神经机制分组，即感觉和运动相关、视觉相关、认知相关和混合范式。解码算法分为四类，即分解算法、黎曼几何、深度学习和迁移学习。这篇评论将全面概述现代主要范式和算法，