Brain-computer interface (BCI) systems based on steady-state visual evoked potential (SSVEP) have become one of the major paradigms in BCI research due to their high signal-to-noise ratio and short training time required by users. Fast and accurate decoding of SSVEP features is a crucial step in SSVEP-BCI research. However, the current researches lack a systematic overview of SSVEP decoding algorithms and analyses of the connections and differences between them, so it is difficult for researchers to choose the optimum algorithm under different situations. To address this problem, this paper focuses on the progress of SSVEP decoding algorithms in recent years and divides them into two categories-trained and non-trained-based on whether training data are needed. This paper also explains the fundamental theories and application scopes of decoding algorithms such as canonical correlation analysis (CCA), task-related component analysis (TRCA) and the extended algorithms, concludes the commonly used strategies for processing decoding algorithms, and discusses the challenges and opportunities in this field in the end.
基于稳态视觉诱发电位（SSVEP）的脑-机接口（BCI）系统具有信噪比高、用户所需训练时间短等优势，已成为主流范式之一。对SSVEP特征快速精准解码是SSVEP-BCI系统研究的关键步骤。然而，当前研究中缺少对SSVEP解码算法系统的梳理，以及对算法间联系与差异的分析，使研究者难以在特定情况下选择最优的算法。针对此问题，本文总结了近年来SSVEP解码算法的研究进展，分为无训练和有训练算法两大类，介绍了典型相关分析（CCA）和任务相关成分分析（TRCA）等解码算法及其改进算法的基本原理和适用范围，接着介绍了解码算法中常用的处理设计策略，最后讨论了SSVEP解码算法的机遇与挑战。.
Keywords: Brain-computer interface; Decoding algorithm; Feature extraction; Pattern recognition; Steady-state visual evoked potentials.