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| EEG De-noising Method Based on Double-Density Discrete Wavelet Transform Using Neighbor-Dependency Thresholding |
| LUO Zhi-Zeng,ZHOU Ying,GAO Yun-Yuan |
| Robot and Intelligent Control Research Institute, Hangzhou Dianzi University, Hangzhou 310018 |
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Abstract To eliminate the noise mixed in Electroencephalogram (EEG), an EEG de-noising method is proposed based on double-density discrete wavelet transform using neighbor-dependency thresholding. Firstly, high frequency coefficients of multilayer signals are obtained by double-density discrete wavelet decomposition. Then, the wavelet coefficients are shrunk with neighbor-dependency thresholding algorithm, which takes the statistical dependencies of the wavelet coefficients into account. Finally, the de-noising signal is obtained by reconstructing shrunk wavelet coefficients. The simulation results of the de-noising experiments on standard noise-adding signal and real EEG show that compared to the first generation discrete wavelet algorithm and traditional soft threshold methods, the proposed de-noising algorithm has the benefits of higher SNR, lower RMSE and Errmax.
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Received: 29 March 2013
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[1] Martinez-Vargas J D, Avendano-Valencia L D, Giraldo E, et al. Comparative Analysis of Time Frequency Representations for Discrimination of Epileptic Activity in EEG Signals // Proc of the 5th IEEE/EMBS Conference on Neural Engineering. Cancun, Mexico, 2011: 148-151
[2] Millán J, Mourio J. Asynchronous BCI and Local Neural Classifiers: An Overview of the Adaptive Brain Interface Project. IEEE Trans on Neural Systems and Rehabilitation Engineering, 2003, 11(2): 159-161
[3] Liu Q J, Ye W W, Du L P. Application of Wavelet Transform De-noising for Neural Cell Based Biosensor. Chinese Journal of Sensors and Actuators, 2009, 22(11): 1586-1590 (in Chinese)
(刘清君,叶伟伟,杜立萍,等.小波变换在神经细胞传感器信号去噪中的应用.传感技术学报, 2009, 22(11): 1586-1590)
[4] Selesnick I W. Smooth Wavelet Tight Frames with Zero Moments. Applied and Computational Harmonic Analysis, 2001, 10(2): 163-181
[5] Cao S C, Zhang G X. Adaptive Power Quality Signal De-noising Based on Double-Density Discrete Wavelet Transform. Computer Engineering and Applications, 2012, 48(13): 244-248 (in Chinese)
(曹世超,张国勋.双密度小波变换的自适应电能质量信号去噪.计算机工程与应用, 2012, 48(13): 244-248)
[6] Gopi V P, Palanisamy P. Endoscopic Image Compression Based on Double Density Discrete Wavelet Transform and SPIHT Coding // Proc of the IEEE International Conference on Control System, Computing and Engineering. Penang, Malaysia, 2011: 466-471
[7] Liu J, Moulin P. Information-Theoretic Analysis of Interscale and Intrascale Dependencies between Image Wavelet Coefficients. IEEE Trans on Image Processing, 2001, 10(11): 1647-1658
[8] Chen G Y, Bui T D. Multiwavelets De-noising Using Neighboring Coefficients. IEEE Signal Processing Letters, 2003, 10(7): 211-214
[9] Wang Y X, He Z J, Zi Y Y. Enhancement of Signal Denoising and Multiple Fault Signatures Detecting in Rotating Machinery Using Dual-tree Complex wavelet Transform. Mechanical Systems and Signal Processing, 2010, 24(1): 119-137
[10] Donoho D L. De-Noising by Soft-Thrshdding. IEEE Trans on Information Theory, 1995, 41(3): 613-627
[11]Cho D, Bui T D. Multivariate Statistical Modeling for Image De-noising Using Wavelet Transforms. Signal Processing: Image Communication, 2005, 20(1): 77-89
[12]Yuan F L, Luo Z Z. The EEG De-noising Research Based on Wavelet and Hilbert Transform Method // Proc of the International Conference on Computer Science and Electronics Engineering. Hangzhou, China, 2012: 361-365 |
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2014, Vol. 27 
