A New HOS-Based Blind Source Extraction Method to Extract μ Rhythms from EEG Signals

Author(s):  
Kun Cai ◽  
Shengli Xie
2010 ◽  
Vol 26-28 ◽  
pp. 5-8
Author(s):  
Yong Jian Zhao ◽  
Bio Qiang Liu

Biomedical signals are a rich source of information about physiological processes, but they are often contaminated by noise. In order to separate biomedical signals from mixtures effectually, we propose a novel blind source extraction method via independent component analysis (ICA). The robustness with respect to noise of this method lies in two-fold: on the one hand, the method does not lead to biassed estimates and, on the other hand, it minimizes the amount of signal and noise interference on the estimated sources. Preliminary results tested with ECG signals have demonstrated that the proposed method may be promising for blindly separating biomedical signals in the presence of noise and further decompose the mixed signals into subcomponents.


2014 ◽  
Vol 989-994 ◽  
pp. 3609-3612
Author(s):  
Yong Jian Zhao

Blind source extraction (BSE) is a promising technique to solve signal mixture problems while only one or a few source signals are desired. In biomedical applications, one often knows certain prior information about a desired source signal in advance. In this paper, we explore specific prior information as a constrained condition so as to develop a flexible BSE algorithm. One can extract a desired source signal while its normalized kurtosis range is known in advance. Computer simulations on biomedical signals confirm the validity of the proposed algorithm.


2016 ◽  
Vol 28 (11) ◽  
pp. 3153-3161 ◽  
Author(s):  
Yong Zhang ◽  
Xiaomin Ji ◽  
Bo Liu ◽  
Dan Huang ◽  
Fuding Xie ◽  
...  

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