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作者:

Li, Mingai (Li, Mingai.) (学者:李明爱) | Wang, Ruotu (Wang, Ruotu.) | Xu, Dongqin (Xu, Dongqin.)

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摘要:

Motor Imagery Electroencephalography (MI-EEG) has shown good prospects in neurorehabilitation, and the entropy-based nonlinear dynamic methods have been successfully applied to feature extraction of MI-EEG. Especially based on Multiscale Fuzzy Entropy (MFE), the fuzzy entropies of the tau coarse-grained sequences in tau scale are calculated and averaged to develop the Composite MFE (CMFE) with more feature information. However, the coarse-grained process fails to match the nonstationary characteristic of MI-EEG by a mean filtering algorithm. In this paper, CMFE is improved by assigning the different weight factors to the different sample points in the coarse-grained process, i.e., using the weighted mean filters instead of the original mean filters, which is conductive to signal filtering and feature extraction, and the resulting personalized Weighted CMFE (WCMFE) is more suitable to represent the nonstationary MI-EEG for different subjects. All the WCMFEs of multi-channel MI-EEG are fused in serial to construct the feature vector, which is evaluated by a back-propagation neural network. Based on a public dataset, extensive experiments are conducted, yielding a relatively higher classification accuracy by WCMFE, and the statistical significance is examined by two-sample t-test. The results suggest that WCMFE is superior to the other entropy-based and traditional feature extraction methods.

关键词:

feature extraction motor imagery electroencephalography weighted composite multiscale fuzzy entropy weight factors

作者机构:

  • [ 1 ] [Li, Mingai]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Wang, Ruotu]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Xu, Dongqin]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Li, Mingai]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China
  • [ 5 ] [Li, Mingai]Minist Educ, Engn Res Ctr Digital Community, Beijing 100124, Peoples R China

通讯作者信息:

  • 李明爱

    [Li, Mingai]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China;;[Li, Mingai]Beijing Key Lab Computat Intelligence & Intellige, Beijing 100124, Peoples R China;;[Li, Mingai]Minist Educ, Engn Res Ctr Digital Community, Beijing 100124, Peoples R China

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来源 :

ENTROPY

年份: 2020

期: 12

卷: 22

2 . 7 0 0

JCR@2022

ESI学科: PHYSICS;

ESI高被引阀值:100

被引次数:

WoS核心集被引频次: 14

SCOPUS被引频次: 14

ESI高被引论文在榜: 0 展开所有

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