Probing neural activations from continuous EEG in a real-world task: Time-frequency independent component analysis

2012 ◽  
Vol 209 (1) ◽  
pp. 22-34 ◽  
Author(s):  
Guofa Shou ◽  
Lei Ding ◽  
Deepika Dasari
Keyword(s):  
Independent Component Analysis ◽  
Real World ◽  
Component Analysis ◽  
Independent Component ◽  
Time Frequency ◽  
Continuous Eeg ◽  
Frequency Independent ◽  
Task Time

Separation and Reproduction of Mixed Images Using 2-D Complex Wavelet Transform

2010 ◽  
Vol 36 ◽  
pp. 466-475
Author(s):  
Tsutomu Matsuura ◽  
Amirul Faiz ◽  
Kouji Kiryu
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Complex Wavelet Transform ◽  
Time Frequency ◽  
Frequency Information ◽  
Complex Wavelet ◽  
Complex Valued

The differences method between 1-D wavelet transform and 2-D wavelet transform in image processing is discussed. Both proposed method uses the quotient of complex valued time-frequency information of observed signals to detect the number of sources. No less number of observed signals than the detected number of sources is needed to separate sources. The assumption on sources is quite general independence in the time-frequency plane, which is different from that of independent component analysis. Using the same given Algorithm and parameters for both method, the result on separated images are compared.

Image Blind Separation Based on Adaptive Multi-Resolution Independent Component Analysis

2012 ◽  
Vol 586 ◽  
pp. 365-369
Author(s):  
Jing Hui Wang ◽  
Shu Gang Tang
Keyword(s):  
Independent Component Analysis ◽  
Quadratic Function ◽  
Component Analysis ◽  
Independent Component ◽  
Frequency Resolution ◽  
Frequency Function ◽  
Experimental Result ◽  
Blind Separation ◽  
Time Frequency ◽  
Dimensional Domain

In this paper, a novel image blind separation using adaptive multi-resolution independent component analysis is presented.This method separates mixed images based on quadratic function. The quadratic function can be interpreted as the time-frequency function or time-scale function, or other. According to the signal characteristics, we can choose the frequency resolution or scale resolution. The argorithm extends the separate technology from one dimensional domain to two dimensional domain,and it’s implement by adaptive procedure. The experimental result showed the method can be effective separation of mixed images. And it shows that the method is feasible.

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