Effective Preprocess Methods to Improve Precision of Empirical Mode Decomposition for EEG
Advances in technology make us easier to get the electroencephalogram(EEG)and to explore the mystery in our brain.Empirical Mode Decomposition(EMD)can decompose the signal into a limited number of intrinsic mode function(IMF),through the analysis for IMFs,we can get some information which we wanted to know behind in the signal.However,the EEG data we got from the sensor often corrupted by the surrounding or electromyography(EMG)noise caused by the muscle activity,especially eye-blinking signal.It will make a strong impact in the decompositions of EMD.This article presents some preprocess methods,the pre-estimation and the reduction of scale,to suppress noises.The IMFs which was produced from the processed EEG data will be more accurate.Experimental results show that the proposed preprocess methods is effective to suppress the interference of noise,in the segment mixed with eye-blinking noise,it is obvious to see that the reduction of error decomposition by EMD.
electroencephalogram (EEG) Empirical Mode Decomposition (EMD) Hilbert-Huang transform (HHT) Eye-Blink
Wei-Tse Lo Tsung-Ying Sun
Department of Electrical Engineering National Dong Hwa University Hualien,97401,Taiwan,R.O.C.
国际会议
The 2014 ICME International Conference on Complex Medical Engineering (CME2014)ICME复合医学工程国际会议
台北
英文
328-331,154
2014-06-26（万方平台首次上网日期，不代表论文的发表时间）