scholarly journals Application of Independent Component Analysis With Adaptive Density Model to Complex-Valued fMRI Data

2011 ◽  
Vol 58 (10) ◽  
pp. 2794-2803 ◽  
Author(s):  
Hualiang Li ◽  
N. M. Correa ◽  
P. A. Rodriguez ◽  
V. D. Calhoun ◽  
T. Adali
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Density Model ◽  
Fmri Data ◽  
Complex Valued

Strategies for reducing large fMRI data sets for independent component analysis

2006 ◽  
Vol 24 (5) ◽  
pp. 591-596 ◽  
Author(s):  
Ze Wang ◽  
Jiongjiong Wang ◽  
Vince Calhoun ◽  
Hengyi Rao ◽  
John A. Detre ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Data Sets

scholarly journals Parallel group independent component analysis for massive fMRI data sets

PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0173496 ◽  
Author(s):  
Shaojie Chen ◽  
Lei Huang ◽  
Huitong Qiu ◽  
Mary Beth Nebel ◽  
Stewart H. Mostofsky ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Data Sets ◽  
Parallel Group ◽  
Group Independent Component Analysis

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.

Localization of Latent Epileptic Activities Using Spatio-Temporal Independent Component Analysis of fMRI Data

2006 ◽  
Vol 19 (1-2) ◽  
pp. 21-28 ◽  
Author(s):  
Huafu Chen ◽  
Dezhong Yao ◽  
Guangming Lu ◽  
Zhiqiang Zhang ◽  
Qiaoli Hu
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Spatio Temporal

scholarly journals Localization of Latent Epileptic Activities Using Spatio-Temporal Independent Component Analysis of fMRI Data

2007 ◽  
Vol 19 (4) ◽  
pp. 223-223
Author(s):  
Huafu Chen ◽  
Dezhong Yao ◽  
Guangming Lu ◽  
Zhiqiang Zhang ◽  
Qiaoli Hu
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Data ◽  
Spatio Temporal
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