计算机应用与软件2025，Vol.42Issue(9)：1-8,17,9.DOI:10.3969/j.issn.1000-386x.2025.09.001
基于机器学习的稳态视觉诱发电位识别研究综述
A REVIEW OF RESEARCHES ON MACHINE LEARNING OF STEADY STATE VISUAL EVOKED POTENTIAL
摘要
Abstract
Steady state visual evoked potential(SSVEP)has become one of the major paradigms in BCI research due to its high signal-to-noise ratio and high information transfer rate.Using algorithm to recognize and extract the features of SSVEP signal is the key of SSVEP system research.However,the current researches lack of SSVEP algorithm review.For this problem,this paper focused on the progress of SSVEP machine learning in recent years.From the perspective of machine learning,algorithms were divided into supervised learning and unsupervised learning.This paper explained the fundamental such as canonical correlation analysis and convolutional neural networks.This paper summarized the shortcomings of current SSVEP algorithm in practical application and discussed the opportunities and challenges faced by SSVEP.关键词
脑机接口/稳态视觉诱发电位/典型相关分析/深度学习Key words
Brain control interface/Steady state visual evoked potential/Canonical correlation analysis/Deep learning分类
信息技术与安全科学引用本文复制引用
毕文龙,魏笑,李亚男,谭草,赵彦峻..基于机器学习的稳态视觉诱发电位识别研究综述[J].计算机应用与软件,2025,42(9):1-8,17,9.基金项目
国家自然科学基金项目(51905319) （51905319）
国家自然科学基金青年科学基金项目(51505263) （51505263）
山东省高等学校科技计划项目(J15LB08). （J15LB08）
