稳态视觉诱发电位频率识别算法研究进展  

Progresses and prospects on frequency recognition methods for steady-state visual evoked potential

作　　者：张杨松 夏敏[1] 陈科 徐鹏(综述)[2] 尧德中(审校)[2,3] ZHANG Yangsong;XIA Min;CHEN Ke;XU Peng;YAO Dezhong(School of Computer Science and Technology,Southwest University of Science and Technology,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;Sichuan Institute for Brain Science and Brain-Inspired Intelligence,Chengdu 611731,P.R.China)

机构地区：[1]西南科技大学计算机科学与技术学院,四川绵阳621010 [2]电子科技大学生命科学与技术学院神经信息教育部重点实验室,成都611731 [3]四川省脑科学与类脑智能研究院,成都611731

出　　处：《生物医学工程学杂志》2022年第1期192-197,共6页Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(62076209)。

摘　　要：稳态视觉诱发电位是脑-机接口系统中常用的控制信号之一。基于稳态视觉诱发电位的脑-机接口具有高信息传输率、训练时间短等优点,已成为脑-机接口研究领域的一个重要分支。本文从无监督学习算法、有监督学习算法和深度学习算法三个方面,归纳了近五年稳态视觉诱发电位频率识别算法研究的主要进展,并展望了值得关注的若干前沿问题和研究方向。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.

关 键 词：稳态视觉诱发电位 脑电图 频率识别 机器学习 深度学习 

分 类 号：R318[医药卫生—生物医学工程]