Brain-computer interface (BCI) provides a direct communicating and controlling approach between the brain and surrounding environment, which attracts a wide range of interest in the fields of brain science and artificial intelligence. It is a core to decode the electroencephalogram (EEG) feature in the BCI system. The decoding efficiency highly depends on the feature extraction and feature classification algorithms. In this paper, we first introduce the commonly-used EEG features in the BCI system. Then we introduce the basic classical algorithms and their advanced versions used in the BCI system. Finally, we present some new BCI algorithms proposed in recent years. We hope this paper can spark fresh thinking for the research and development of high-performance BCI system.
脑-机接口（BCI）作为一种可实现人脑与外界信息交流和控制的人机交互方式，已经受到脑科学、人工智能等研究领域的广泛关注。脑电特征解码是BCI系统的核心步骤。高效特征解码取决于“特征”和所使用的特征解码算法。本文首先介绍了脑电信号的主要特征描述方式，紧接着介绍BCI相关研究中使用的经典解码算法的基本原理、适用范围、存在问题以及改进方法，最后介绍了近年来提出的多种新算法及理论框架，并展望了未来脑电特征解码算法的新动向，希冀为研究开发高性能BCI提供新思路。.
Keywords: brain-computer interface; electroencephalogram; feature extraction; pattern recognition.