用于稳态视觉诱发电位特征频率提取的同步压缩短时傅里叶变换方法

张黎明 1; 张小栋 1; 2; 陆竹风 1; 李睿 1; 2

doi:10.7652/xjtuxb201702004

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用于稳态视觉诱发电位特征频率提取的同步压缩短时傅里叶变换方法

更新时间：2025-07-09

- 用于稳态视觉诱发电位特征频率提取的同步压缩短时傅里叶变换方法
- A Synchrosqueezing Short-Time Fourier Transform Method for Characteristic Frequency Extraction of Steady State Visual Evoked Potential
- 西安交通大学学报 2017年51卷第2期页码：20-26+46
- 作者机构：
  
  西安交通大学机械工程学院,西安,710049
- 作者简介：
- 基金信息：
  
  : 2016-05-21。作者简介: 张黎明(1994—),男,硕士生;张小栋(通信作者),男,教授,博士生导师。基金项目: 国家自然科学基金资助项目(51275388)
- DOI：10.7652/xjtuxb201702004
  中图分类号： TP23
- 网络首发：2017-02-10，
  
  纸质出版：2017
- 稿件说明：
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张黎明 1, 张小栋 1, 2, 等. 用于稳态视觉诱发电位特征频率提取的同步压缩短时傅里叶变换方法[J]. 西安交通大学学报, 2017,51(2):20-26+46.

A Synchrosqueezing Short-Time Fourier Transform Method for Characteristic Frequency Extraction of Steady State Visual Evoked Potential[J]. 2017, 51(2): 20-26+46.
张黎明 1, 张小栋 1, 2, 等. 用于稳态视觉诱发电位特征频率提取的同步压缩短时傅里叶变换方法[J]. 西安交通大学学报, 2017,51(2):20-26+46. DOI： 10.7652/xjtuxb201702004.

A Synchrosqueezing Short-Time Fourier Transform Method for Characteristic Frequency Extraction of Steady State Visual Evoked Potential[J]. 2017, 51(2): 20-26+46. DOI： 10.7652/xjtuxb201702004.

摘要

针对稳态视觉诱发电位(steady state visual evoked potential

SSVEP)范式下脑电信号(electroencephalograph

EEG)信噪比低、限制其识别正确率提高及脑-机接口应用等问题

根据EEG随机性、近似平稳的特点

提出了用于SSVEP特征频率提取的同步压缩短时傅里叶变换方法。该方法利用短时傅里叶变换对EEG进行时频分析

并通过同步压缩变换对时频平面的能量在频率方向进行重新分配

获得频率曲线更加集中的时频表达; 同时为提高EEG信噪比

提取SSVEP脑电中特征频率附近信号进行重构

并利用典型相关分析进行分类识别

有效提高了最终识别正确率。仿真和实验结果表明

该方法极大地提高了信号的信噪比

具有良好的抗噪声性能和信号提取精度

且与传统的经验模态分解和常规滤波方法相比

该方法平均识别正确率最多分别提高了9.98%和4.38%

平均信息传输率最多分别提高了7.57 bit/min和2.69 bit/min

有效提高了SSVEP范式下脑-机接口的工作性能。

Abstract

A new method using the synchrosqueezing short-time Fourier transform for extracting frequency information of SSVEP(steady-state visual evoked potential)is proposed to solve the problem that due to the low SNR of EEG signals it is difficult to improve the recognition accuracy of SSVEP and enhance the brain-computer interface(BCI)performance. The method considers the randomness and approximate stationarity of SSVEP-based EEG signals

uses the short-time Fourier transform to analyze properties of the EEG signals' time-frequency

and adopts the synchrosqueezing transform to reassign the energy distribution of TF plain in the frequency direction

so that more attention is given to the expression of frequency curve of TF plain. The signal near characteristic frequency of SSVEP is reconstructed to improve EEG signals' SNR

and canonical correlation analysis(CCA)is used to obtain the SSVEP classification result. Simulations and experiments show that the proposed method greatly improves the SNR

and has good anti-noise performance and signal extraction accuracy. Comparisons with the empirical mode decomposition(EMD)and the conventional filter method show that the average recognition accuracy of the proposed method increases by 9.98% and 4.38%

respectively

and the information transfer rate increases by 7.57 bit/min and 2.69 bit/min

respectively. It is concluded that the method can effectively improve the working performance of SSVEP-based BCI.

关键词

Keywords

references

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