A spike denoising method combined principal component analysis with wavelet and ensemble empirical mode decomposition.
10.7507/1001-5515.201906048
- Author:
Yijun ZHOU
1
;
Yifan HU
1
;
Mengmeng LI
2
,
3
;
Lifang YANG
2
,
3
;
Zhigang SHANG
2
,
3
Author Information
1. School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, P.R.China.
2. School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, P.R.China
3. Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou University, Zhengzhou 450001, P.R.China.
- Publication Type:Journal Article
- Keywords:
ensemble empirical mode decomposition;
principal component analysis;
signal-to-noise ratio;
spike;
wavelet-threshold denoising
- MeSH:
Algorithms;
Microelectrodes;
Principal Component Analysis;
Reproducibility of Results;
Signal Processing, Computer-Assisted;
Signal-To-Noise Ratio;
Wavelet Analysis
- From:
Journal of Biomedical Engineering
2020;37(2):271-279
- CountryChina
- Language:Chinese
-
Abstract:
Spike recorded by multi-channel microelectrode array is very weak and susceptible to interference, whose noisy characteristic affects the accuracy of spike detection. Aiming at the independent white noise, correlation noise and colored noise in the process of spike detection, combining principal component analysis (PCA), wavelet analysis and adaptive time-frequency analysis, a new denoising method (PCWE) that combines PCA-wavelet (PCAW) and ensemble empirical mode decomposition is proposed. Firstly, the principal component was extracted and removed as correlation noise using PCA. Then the wavelet-threshold method was used to remove the independent white noise. Finally, EEMD was used to decompose the noise into the intrinsic modal function of each layer and remove the colored noise. The simulation results showed that PCWE can increase the signal-to-noise ratio by about 2.67 dB and decrease the standard deviation by about 0.4 μV, which apparently improved the accuracy of spike detection. The results of measured data showed that PCWE can increase the signal-to-noise ratio by about 1.33 dB and reduce the standard deviation by about 18.33 μV, which showed its good denoising performance. The results of this study suggests that PCWE can improve the reliability of spike signal and provide an accurate and effective spike denoising new method for the encoding and decoding of neural signal.
