引用本文:刘朕,朱炳宇,张景祥.EEG信号识别中可调优化Q因子小波变换的多特征融合算法[J].控制理论与应用,2022,39(12):2302~2312.[点击复制]
LIU Zhen,ZHU Bing-yu,ZHANG Jing-xiang.Multi-feature fusion algorithm for adaptive-tunable Q-factor wavelet transform in EEG signal recognition[J].Control Theory and Technology,2022,39(12):2302~2312.[点击复制]
EEG信号识别中可调优化Q因子小波变换的多特征融合算法
Multi-feature fusion algorithm for adaptive-tunable Q-factor wavelet transform in EEG signal recognition
摘要点击 1451  全文点击 409  投稿时间：2021-08-09  修订日期：2022-10-31
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DOI编号  10.7641/CTA.2022.10725
  2022,39(12):2302-2312
中文关键词  EEG信号  可调Q因子小波变换  能量香农熵比  特征选择
英文关键词  electroencephalogram  tunable Q-factor wavelet transform  energy Shannon entropy ratio  feature subset selection
基金项目  国家自然科学基金项目(61772013)资助.
作者单位E-mail
刘朕 江南大学理学院 1481723747@qq.com 
朱炳宇 江南大学理学院  
张景祥* 江南大学理学院 zhangjingxiang@jiangnan.edu.cn 
中文摘要
      EEG信号对脑部疾病诊断具有重要意义, 但其特征选择对信号识别准确率影响较大. 针对这个问题, 本文基于Q因子小波变换, 提出一种可调优化Q因子小波变换融合多维特征的脑电信号识别(Ad-TQWT MF)算法. 该算法首先根据小波分解后的子带信号定义能量香农熵比, 用其作为可调Q因子小波的优化评价标准; 再融合变换后信号的时域, 频域和非线性特征, 通过自适应特征选择方法构建特征子空间; 最后在该特征子空间下对脑电信号进行识别. 在BCI脑电竞赛数据集Dataset III, O3VR, X11b和S4b进行了实验, 实验结果表明: Ad-TQWT MF算法在LDA分类器中精度为89.2%, 81.2%, 83.2%和85.6%, 相比于原Q因子小波变换, 冗余特征减少10%30%, 相较于Haar和Db 4小波精度提高3%5%, 证明了Ad-TQWT MF算法的有效性.
英文摘要
      EEG signals are of great significance for the diagnosis of brain diseases. However, the recognition accuracy is greatly affected by the feature selection and redundant features. To solve this problem, an adaptive-tunable Q-factor wavelet transform multi-feature algorithm (Ad-TQWT MF) is proposed based on the Q-factor wavelet transform in this paper. First, the energy Shannon entropy ratio based on the decomposed subbands is defined, and it is set to the optimization evaluation standard of the adjustable Q factor wavelet. Then, the time domain, frequency domain and nonlinear features of the transformed EEG are fused to construct a useful characteristic subspace through the adaptive feature selection. Finally, the characteristic subspace of EEG is identified by the Ad-TQWT MF. Some experiments are carried out on the EEG datasets of first and second BCI competitions including Dataset III, O3VR, X11b and S4b. The experimental results show that the accuracy of the proposed algorithm in the linear discriminant analysis classifier are 89.2%, 81.2%, 83.2% and 85.6%. Compared to Haar and Db 4, the feature redundancy rate of proposed algorithm drops by 10%30% and the recognition accuracy of EEG increases 3%5%. The experimental results strongly prove the effectiveness of the Ad-TQWT algorithm.