EEMD Denoising Method for Neuronal Spike Signals

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.

Key words: spike, EEMD, wavelet-threshold method, signal-to-noise ratio

CLC Number:

TP391.9

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

References

[1] 刘新玉. 神经元锋电位干扰抑制算法研究及实现 [D]. 郑州: 郑州大学, 2012.
(Liu X Y.Research and implementation of interference suppression algorithm to neuronal spikes [D]. Zhengzhou, China: Zhengzhou University, 2012 (in Chinese))
[2] 吴丹, 封洲燕, 王静. 微电极阵列神经元锋电位信号的去噪方法[J]. 浙江大学学报: 工学版, 2010, 44(1): 104-110.
(Wu D, Feng Z Y, Wang J.Novel denoising approach to neuronal spike signals recorded by microelectrode array[J]. Journal of Zhejiang University (Engineering Science), 2010, 44(1): 104-110. (in Chinese))
[3] 王清波, 代建华, 章怀坚, 等. 基于非线性能量算子和匹配滤波的锋电位检测与分类[J]. 仪器仪表学报, 2011, 32(001): 81-86.
(Wang Q B, Dai J H, Zhang H J, et al. Spike detection and sorting based on nonlinear energy operator and matched filter[J]. Chinese Journal of Scientific Instrument, 2011, 32(1): 81-86 (in Chinese))
[4] Musial P G, Baker S N, Gerstein G L, et al. Signal-to-noise ratio improvement in multiple electrode recording[J]. Journal of neuroscience methods (S0165-0270), 2002, 115(1): 29-43.
[5] 万红, 李晓燕, 刘新玉, 等. 锋电位检测信号的多元小波去噪方法研究[J]. 系统仿真学报, 2013, 25(10): 2487-2491.
(Wan H, Li X Y, Liu X Y, et al. Multivariate wavelet denoising method for neuronal spike signals[J]. Journal of System Simulation (S1004-731X), 2013, 25(10): 2487-2491 (in Chinese))
[6] Hu M, Liang H L.Adaptive multiscale entropy analysis of multivariate neural data[J]. IEEE Transactions on Biomedical Engineering (S0018-9294), 2012, 59(1):12-15.
[7] 袁玲, 杨帮华, 马世伟. 基于HHT 和 SVM 的运动想象脑电识别[J]. 仪器仪表学报, 2010, 31(3): 649-654.
(Yuan L, Yang B H, Ma S W.Discrimination of movement imagery EEG based on HHT and SVM[J]. Chinese Journal of Scientific Instrument, 2010, 31(3): 649-654. (in Chinese))
[8] Wu Z, Huang N E.Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis (S1793-7175), 2009, 1(1): 1-41.
[9] Yang R, Ren M.Wavelet denoising using principal component analysis[J]. Expert Systems with Applications (S0957-4174), 2011, 38(1): 1073-1076.
[10] Citi L, Carpaneto J, Yoshida K, et al. On the use of wavelet denoising and spike sorting techniques to process electroneurographic signals recorded using intraneural electrodes[J]. Journal of neuroscience methods (S0165-0270), 2008, 172(2): 294-302.
[11] Wan H, Liu X Y, Niu X K, et al.The design and implementation of anti-interference system in neural electrophysiological experiments [C]// Electrical Engineering and Control. Chennai, India: Scientific Publishing Services Pvt. Ltd., 2011: 605-611.