doi:10.16186/j.cnki.1673-9787.2020080018

Received:2020/08/09

Revised:2020/10/22

Published:2022/01/10

Research on feature extraction and classification of EEG signal based on DWT, MEMD and fuzzy entropy

CHEN Qianqian, XU Jian, LIU Xiuping, HUANG Lei, XI Nan

School of Electronic Information,Xi’ an Engineering University, Xi ’ an 710048 , Shaanxi, china

Abstract: Aiming at the problem that the low classification accuracy of EEG signals results in poor control stability of brain-controlled equipment,a feature extraction method was proposed based on discrete wavelet transform(DWT) ,multivariate empirical mode decomposition(MEMD) and fuzzy entropy.Firstly,the electroencephalogram(EEG) signal was decomposed into a series of narrow band signals with DWT,and then the sub-band signals was decomposed with MEMD to get a set of intrinsic mode functions,which were called intrinsic mode functions(IMFs).Secondly,the appropriate IMFs for signal reconstruction were selected.Then,the fuzzy entropy algorithm was used to extract features from the signal as an experimental feature vector.Finally,support vector machines(SVM) were used for classification.By using the BCI competition data as a verification set,the effectiveness of the algorithm was verified,and the problem of wider EMD mid-band coverage was solved.

Key words:EEG signal;discrete wavelet transform;multivariate empirical mode decomposition;fuzzy entropy

 基于DWT、MEMD和模糊熵的脑电信号特征提取与分类研究_陈倩倩.pdf