基于SSVEP信号的相频特性分类算法研究  

Research on phase-frequency characteristic classification algorithm based on SSVEP signals

作　　者：丛佩超 陈熙来 肖宜轩 李文彬 刘俊杰 张欣 Cong Peichao;Chen Xilai;Xiao Yixuan;Li Wenbin;Liu Junjie;Zhang Xin(School of Mechanical and Automotive Engineering,Guangxi University of Science and Technology,Liuzhou 545616,China)

机构地区：[1]广西科技大学机械与汽车工程学院,柳州545616

出　　处：《电子测量技术》2024年第5期188-198,共11页Electronic Measurement Technology

基　　金：中央引导地方科技发展专项资金项目(桂科ZY19183003);广西重点研发计划项目(桂科AB20058001)资助。

摘　　要：目前基于稳态视觉诱发电位(SSVEP)的脑-机接口在人机协作中受到广泛关注,现有面向SSVEP信号的相位与频率信息的深度学习分类方法,仍存在由于信息利用不充分导致的SSVEP信号分类效果较差等问题。而目前已出现多种分类算法用于解决上述问题。本文基于迁移学习思想提出一种用于SSVEP信号分类的深度神经网络模型,将快速傅里叶变换后的复向量作为输入,对各个导联的实、虚部向量进行卷积,学习对应的相频特性。该模型分为两部分:第一部分利用所有被试者之间的统计共性获得相位和频率信息的全局相频特征模块;第二部分利用训练好的全局相频特征模块对局部相频特征模块进行初始化,通过局部相频特征模块的进一步强化学习对训练参数进行微调,以减少每个被试者之间的个体差异。在公开数据集BETA上进行测试,在时窗长度为1.5 s时,平均准确率和平均信息传输率分别为89.98%和71.80 bit/min。实验结果表明,与其他方法相比,本文的分类算法模型取得了较为不错的分类效果,所设计的全局、局部相频特征模块能够改善个体差异因素对分类结果的影响,为深入挖掘、利用SSVEP信号中的相位和频率信息提供了全新思路。Currently,the brain-machine interface based on steady-state visual evoked potential(SSVEP)has received wide attention in human-computer collaboration,and the existing deep learning classification methods oriented to the phase and frequency information of SSVEP signals still have problems such as poor classification of SSVEP signals due to insufficient utilization of the information.And a variety of classification algorithms have appeared for solving the above problems.In this paper,a deep neural network model for SSVEP signal classification is proposed based on the idea of migration learning,which takes the complex vectors after the fast Fourier transform as inputs,and convolves the real and imaginary part vectors of each lead to learn the corresponding phase frequency characteristics.The model is divided into two parts:the first part uses the statistical commonality among all subjects to obtain the global phase-frequency feature module for phase and frequency information;the second part uses the trained global phase-frequency feature module to initialize the local phase-frequency feature module,and fine-tunes the training parameters through further reinforcement learning of the local phase-frequency feature module in order to reduce the individual differences between each subject.Tested on the public dataset BETA,the average accuracy and average information transfer rate are 89.98%and 71.80 bit/min,respectively,when the time window length is 1.5 s.The experimental results show that the classification algorithm model in this paper achieves a relatively good classification effect compared with other methods,and the designed global and local phase-frequency feature modules are able to improve the effect of individual differences on the classification results.The designed global and local phase-frequency feature module can improve the influence of individual differences on the classification results,which provides a brand new idea for the in-depth mining and utilization of phase and frequency information in SSVEP si

关 键 词：稳态视觉诱发电位 迁移学习 深度神经网络 相频特性 

分 类 号：TN911.7[电子电信—通信与信息系统]