EEMD Denoising Method for Neuronal Spike Signals

Authors

Wan Hong, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China;
Guan Lei, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China;
Xinyu Liu, School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, China;

Abstract

Abstract: Spikes which are the basis of the research of brain information are sensitive to noise because they are broadband and small amplitude signal. Based on the fact that spikes are intermittent and nonstationary signals, EMD’s improved algorithm EEMD was adopted to remove noise from neuronal spike signals with wavelet-threshold method. EEMD can solve EMD’s model mixing by separating the intermittent composition in the signal effectively. Comparing with EMD with wavelet-threshold and Multivariate Wavelet, the result of simulation and real data shows that this method can not only improve SNR but also reduce spike waveform distortion. Among the three denoising methods, EEMD is the most effective by improving an average of 4.177 2 db in SNR. It is important for the detection and the next step analysis research of spike.

Recommended Citation

Hong, Wan; Lei, Guan; and Liu, Xinyu (2020) "EEMD Denoising Method for Neuronal Spike Signals," Journal of System Simulation: Vol. 27: Iss. 1, Article 16.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss1/16

First Page

118

Revised Date

2014-03-13

DOI Link

https://doi.org/

Last Page

124

CLC

TP391.9

Recommended Citation

Wan Hong, Guan Lei, Liu Xinyu. EEMD Denoising Method for Neuronal Spike Signals[J]. Journal of System Simulation, 2015, 27(1): 118-124.