An Optimal Smooth Model based EEG Analysis Method

Chao Chen, Tianxu Shang, Abdelkader Nasreddine Belkacem, Shanting Zhang, Lin Lu, Penghai Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Aiming at the problem of unsatisfactory noise removal effect during the preprocessing of MI EEG signals, an optimal smooth model based on empirical mode decomposition is introduced. First, use empirical mode decomposition (EMD) to decompose the complex signal into a certain number of intrinsic mode functions (IMFs) and a remainder function (r), and then construct a smooth model function based on the mean square error and smoothing index, using different The function of the intrinsic mode is combined with the optimal smooth model function to reduce noise and obtain the optimal solution. The research results show that compared with several denoising methods commonly used in other literature, this method has improved SNR, RMSE, and PE three evaluation indicators. The proposed method can provide a reference for the preprocessing of motor imagery signals.

Original languageEnglish
Title of host publicationProceedings of the 40th Chinese Control Conference, CCC 2021
EditorsChen Peng, Jian Sun
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9789881563804
Publication statusPublished - Jul 26 2021
Event40th Chinese Control Conference, CCC 2021 - Shanghai, China
Duration: Jul 26 2021Jul 28 2021

Publication series

NameChinese Control Conference, CCC
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927


Conference40th Chinese Control Conference, CCC 2021


  • EEG signal denoising
  • Electroencephalogram(EEG)
  • Motor Imagery

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Systems Engineering
  • Applied Mathematics
  • Modelling and Simulation


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