摘要
现有的稳态视觉诱发电位(SSVEP)的信号识别方法没有充分关注信号的相位特征在识别过程中的重要作用,为此提出一种扩展典型相关分析(eCCA)的改进方法。将联合频率-相位调制编码的刺激范式中的相位参数添加到由受试者训练数据所构造的参考信号,以此来实现对eCCA的相位约束,从而提升eCCA方法对SSVEP信号的识别性能。通过在公开数据集上与现有的SSVEP信号识别方法进行对比实验,表明所提方法对SSVEP信号的平均识别率提高到82.76%,信息传输速率提高至116.18 bits/min,且具有更好的稳定性。
Many existing signal recognition methods for steady-state visual evoked potential(SSVEP)do not pay sufficient attention to the importance of the phase features.In this paper,an improved extended canonical correlation analysis(eCCA)method is proposed for SSVEP signal identification.The phase parameter in the stimulus paradigm of joint frequency-phase modulation coding is added to the reference signal constructed from subjects′training data as a way to achieve phase constraints on eCCA,thus improving the recognition performance of the eCCA method for SSVEP signals.Thus the eCCA-based SSVEP signal recognition performance is improved.To verify the effectiveness of the proposed method,SSVEP signal recognition experiments are conducted on a publicly available dataset and compared with the existing signal recognition methods.The experimental results show that the average recognition rate of the proposed method is improved to 82.76%,and the information transmission rate is reached to 116.18 bits/min with better stability.
作者
芦鹏
戴凤智
尹迪
温浩康
高一婷
Lu Peng;Dai Fengzhi;Yin Di;Wen Haokang;Gao Yiting(College of Electronic Information and Automation,Tianjin University of Science and Technology,Tianjin 300222,China)
出处
《电子测量技术》
北大核心
2023年第1期78-83,共6页
Electronic Measurement Technology
基金
2021年教育部高等学校电子信息类专业教学指导委员会教改项目(2021-JG-03)
2021年天津科技大学研究生科研创新项目(YJSKC2021S09)资助
关键词
稳态视觉诱发电位
脑机接口
脑电信号
扩展典型相关分析
steady-state visual evoked potential(SSVEP)
brain computer interface(BCI)
electroencephalogram(EEG)
extended canonical correlation analysis(eCCA)
作者简介
芦鹏,硕士研究生,主要研究方向为脑机接口技术、多传感器融合。E-mail:lupeng970504@163.com;通信作者:戴凤智,博士,副教授,主要研究方向为智能制造、人工智能、机器人技术。E-mail:daifz@tust.edu.cn
