Combined Empirical Pattern Decomposition and Chaos Theory for Steady-State Visual Evoked Potential Electroencephalogram Recognition

GUO Xiaobing; XU Guanghua; LI Hui; XIE Jieren; JIANG Hanli; ZHANG Sicong

doi:10.7652/xjtuxb202406004

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Combined Empirical Pattern Decomposition and Chaos Theory for Steady-State Visual Evoked Potential Electroencephalogram Recognition

更新时间：2025-07-28

- Combined Empirical Pattern Decomposition and Chaos Theory for Steady-State Visual Evoked Potential Electroencephalogram Recognition
- Vol. 58, Issue 6, Pages: 34-42(2024)
- 作者机构：
  
  1. 西安交通大学机械工程学院,西安,710049
  2. 西安交通大学精密微纳制造技术全国重点实验室,西安,710049
  3. 西安交通大学第一附属医院,西安,710061
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202406004
  CLC： TP391.4
- Online First：10 June 2024，
  
  Published：2024
- 稿件说明：
移动端阅览
GUO Xiaobing, XU Guanghua, LI Hui, et al. Combined Empirical Pattern Decomposition and Chaos Theory for Steady-State Visual Evoked Potential Electroencephalogram Recognition[J]. 2024, 58(6): 34-42.
DOI：

GUO Xiaobing, XU Guanghua, LI Hui, et al. Combined Empirical Pattern Decomposition and Chaos Theory for Steady-State Visual Evoked Potential Electroencephalogram Recognition[J]. 2024, 58(6): 34-42. DOI： 10.7652/xjtuxb202406004.

摘要

针对多目标稳态视觉诱发电位(SSVEP)信号识别准确率低、线性识别方法抑制噪声的同时也会抑制信号本身特征等问题

基于经验模式分解对信号的降噪特性、达芬混沌系统对微弱周期信号的敏感性及噪声的免疫特性

提出了一种非线性多目标SSVEP信号识别算法。首先

采用共平均参考算法将多通道SSVEP信号融合成单通道信号

通过傅里叶变换求得SSVEP信号的相位谱

为达芬混沌系统周期策动力添加相位; 接着

采用经验模式分解降噪

将获得的第一个本征模函数输入到达芬混沌系统中

利用基于频谱差异的混沌系统状态判别方法

求解各目标的刺激频率幅值; 最后

根据最大刺激频率幅值确定刺激目标

实现了对多目标SSVEP信号的识别。研究结果表明:相较于典型相关分析法

所提非线性信号处理方法的平均识别准确率提高了7.3%

平均信息传输速率提高了3.84 bit/min。该研究为探究非线性SSVEP信号解码算法提供了新方向。

Abstract

Aiming at the problems of low recognition accuracy of multi-target SSVEP signals and suppression of the characteristics of signal while the linear recognition method suppresses noise

this paper proposes a nonlinear multi-objective SSVEP signal recognition algorithm based on the noise reduction characteristics of empirical mode decomposition

Duffing chaotic system's sensitivity to weak periodic signals and the immune characteristics of noise. First

multi-channel SSVEP signals are integrated into single-channel signals by using the co-average reference algorithm. Then

the phase spectrum of SSVEP signal is obtained by Fourier transform

and the phase is added to the periodic dynamics of Duffing chaotic system. After empirical mode decomposition noise reduction

the first eigenmode function obtained is input into Duffing chaotic system

and the amplitude of each target stimulus frequency is solved by using the chaotic system state discrimination method based on spectral difference. Finally

the stimulus target was determined according to the amplitude of the maximum stimulus frequency and multi-target SSVEP signals are recognized. The results show that the average recognition accuracy of the proposed nonlinear signal processing method is 7.3% higher than that of typical correlation analysis. The information transmission rate increases by 3.84 bit/min. The method provides a new direction for exploring nonlinear SSVEP signal decoding algorithms.

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