Computer Science ›› 2017, Vol. 44 ›› Issue (5): 66-70.doi: 10.11896/j.issn.1002-137X.2017.05.012

Previous Articles     Next Articles

Improved EEMD Algorithm Based on EEG Signal

HUANG Li-ya, DA Cheng-lu, YANG Chen, CHEN Zhi-yang and WANG Hao   

  • Online:2018-11-13 Published:2018-11-13

Abstract: In order to effectively improve the mode mixing problem for EEG study,a modified EEMD algorithm was proposed to adapt to the brain signal research.Firstly,We screened out the EMD results based on correlation,then adaptively predicted the EEG signal characteristics from the original brain signals,and fused the property of white Gaussian noise to generate new noise brain signal.Finally,based on the new noise,the EEMD was performed.The experimental results show that the new brain signal is not only adaptive,but also can solve the mode mixing problem of brain signals in EMD better,proving the theory and the application value of the improved algorithm in EEG study field.

Key words: EEMD,Mode mixing,Assisted noise,Signal estimation

[1] HUANG N E,SHEN Z,LONG S R,et al.The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis[J].Proceedings of the Royal Society A Mathematical Physical & Engineering Sciences,1998,4(1971):903-995.
[2] SUN Y Z.The Time-frequency Analysis Theory and AppliedResearch of EMD[D].Chengdu:University of Electronic Technology of China,2007.(in Chinese) 孙艳争.EMD时频分析理论与应用研究[D].成都:电子科技大学,2007.
[3] GAO Y C,GE G T,SHENG Z Y,et al.Analysis and Solution to the Mode mixing phenomenon in EMD[C]∥Congress on Image and Signal Processing,2008.WashingtonDC:IEEE Computer Society,2008:223-227.
[4] WU Z H,HUANG N E.Ensemble Empirical Mode Decomposition:A Noise Assisted Data Analysis Method[J].Advances in Adaptive Data Analysis,2009,1(1):1-41.
[5] WU Z H,HUANG N E,CHEN X Y.The Multi-Dimensional ensemble empirical mode[J].Advances in Adaptive Data Analysis,2009,1(3):339-372.
[6] WANG G,CHEN X Y,QIAO F L,et al.On intrinsic mode function[J].Advances in Adaptive Data Analysis,2010,2(3):277-293.
[7] KOPISINIS Y,MCLAUGHLIN S.Development of EMD-baseddenoising method Inspired by Wavelet Thresholding[J].IEEE Transactions on signals Processing,2009,57(4):1351-1362.
[8] YANG G L,LIU Y Y,WANG Y Y,et al.EMD interval threshol-ding denoising based on similarity measure to select relevant modes[J].Signal Processing,2015,9(4):95-109.
[9] JIANG B B,LI H G.Fault Diagnoses of Cracked Rotor and Rub-impact Rotor Based DEMD Method[J].Noise and Vibration Control,2009(5):66-69.(in Chinese) 景蓓蓓,李鸿光.基于微分的经验模式方法在转子裂纹和碰摩故障诊断中的应用[J].噪声与振动控制,2009(5):66-69.
[10] ZHANG S C,HAN Y L,ZHANG Y J.Threshold Estimation Method for EMD-IT denoising algorithm[J].Computer Engineering and Design,2014,5(12):4386-4389.(in Chinese) 张守成,韩元良,张玉洁.EMD-IT去噪算法的阈值估计方法[J].计算机工程与设计,2014,35(12):4386-4389.
[11] DU L,LI M.EMD denoising with multiple Clear Iterative-Thresholding and Its Application[J].Journal of Civil Aviation University of China,2013,31(5):5-8.(in Chinese) 杜丽,李猛.多层清除重复间隔阈值的EMD去噪及其应用[J].中国民航大学学报,2013,31(5):5-8.
[12] QU Z Y,LI H G.An Improved EEMD-based Denoising Method[J].Noise and Vibration Control,2014,34(5):171-176.(in Chinese) 屈中阳,李鸿光.一种改进的集合平均经验模态分解去噪方法[J].噪声与振动控制,2014,34(5):171-176.
[13] WANG Y T,JUNG T P.Visual stimulus design for high-rate SSVEP BCI[J].Electronics Letters,2010,6(15):1057-1058.
[14] HUANG L Y,HUANG X X,WANG Y T,et al.Empirical mode decomposition improves detection of SSVEP[C]∥International Conference of the IEEE Engineering in Medicine & Biology Society.Osaka,Japan,2013:3901-3904.

No related articles found!
Full text



No Suggested Reading articles found!