Abstract：To eliminate the noises mixed in the EEG effectively for completing the EEG analysis, a comparative research is made about the EEG de-noising effects of wavelet and Hilbert transform.In the HHT denoising method, using empirical mode decomposition algorithm to have 8 scales of decomposition for the EEG, and get the combination of components of intrinsic mode functions, then reconstruct the filtered signal, obtain the EEG after denoising finallyExperimental results show that HHT method can properly remove the noises which contained in the EEGThe denoising effects of HHT method and the wavelet transform method are compared by using the evaluation indexes.It finds that HHT method is better than the traditional wavelet transform in the EEG de-nosing and the efficiency of the HHT method is higher.
罗志增,袁飞龙, 高云园. 小波和希尔伯特变换在脑电信号消噪中的对比研究[J]. 计量学报, 2013, 34(6): 567-572.
LUO Zhi-zeng,YUAN Fei-long,GAO Yun-yuan. The Comparative Research on Wavelet and Hilbert Transform in the EEG De-noising. Acta Metrologica Sinica, 2013, 34(6): 567-572.
［1］Duru D A, Akdeniz G, Kara E. Epileptic source localizations based on EEG and SDE measurements［C］//the 15th National Biomedical Engineering Meeting, Antalya, 2010.
［2］Millan J R, Mourino J. Asynchronous BCI and local neural classifiers: An overview of the adaptive brain interface porject ［J］. IEEE Transactions on Neural Systerms and Rehabilitation Engineering, 2003,11(2):159-161.
［3］Liao F, Wang F, Zhou P. Deriving respiratory signals from ECG by filtering method［C］//3rd International Conference on Computer Research and Development, Shanghai, 2011.
［4］Liu C S, Tang Y, Tang J F. Removal of ocular artifact from EEG based on ICA ［J］. Computer Engineering and Applications,2007,43(17):230-232.
［5］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］. J Proc R Soc Londn A, 1998, 454(1971):903-995.
［6］Sharabaty H, Jammes B, Esteve D. EEG analysis using HHT: One step toward automatic drowsiness scoring ［C］//Proceedings of the 22nd International Conference on Advanced Information Networking and Applications Workshops, Gino Wan, Okinawa, Japan, 2008.
［7］Cheng J L, Zou D H S, Sun X Y, et al. An improved method on reducing measurement noise based on Hilbert-Huang Transform ［C］//IEEE International Conference on Intelligent Computing and Intelligent Systems, Shanghai, 2009, 627-630.
［8］戴桂平,刘彬． 基于小波去噪和 EMD的信号瞬时参数提取［J］．计量学报,2007,28(2):158-162．
［9］曲天书,戴逸松,王树勋． 基于SURE无偏估计的自适应小波阈值去噪［J］．电子学报,2002,30(2):266-268．
［10］罗志增,李亚飞,孟明． 一种基于二代小波变换与盲信号分离的脑电信号处理方法［J］．航天医学与医学工程,2010,23(2):137-140．
2013, Vol. 34 
