Zhang Yangsong, Xia Min, Chen Ke, Xu Peng, Yao Dezhong
School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, Sichuan 621010, P. R. China.
MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2022 Feb 25;39(1):192-197. doi: 10.7507/1001-5515.202102031.
Steady-state visual evoked potential (SSVEP) is one of the commonly used control signals in brain-computer interface (BCI) systems. The SSVEP-based BCI has the advantages of high information transmission rate and short training time, which has become an important branch of BCI research field. In this review paper, the main progress on frequency recognition algorithm for SSVEP in past five years are summarized from three aspects, i.e., unsupervised learning algorithms, supervised learning algorithms and deep learning algorithms. Finally, some frontier topics and potential directions are explored.
稳态视觉诱发电位（SSVEP）是脑机接口（BCI）系统中常用的控制信号之一。基于SSVEP的BCI具有信息传输速率高和训练时间短的优点，已成为BCI研究领域的一个重要分支。在这篇综述论文中，从无监督学习算法、监督学习算法和深度学习算法三个方面总结了过去五年中SSVEP频率识别算法的主要进展。最后，探讨了一些前沿课题和潜在方向。
