scholarly journals Fast and precise independent component analysis for high field fMRI time series tailored using prior information on spatiotemporal structure

2001 ◽  
Vol 15 (1) ◽  
pp. 54-66 ◽  
Author(s):  
Kiyotaka Suzuki ◽  
Tohru Kiryu ◽  
Tsutomu Nakada
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
High Field ◽  
Prior Information ◽  
Component Analysis ◽  
Independent Component ◽  
Spatiotemporal Structure ◽  
Fmri Time Series

scholarly journals Cortex-based independent component analysis of fMRI time series

2004 ◽  
Vol 22 (10) ◽  
pp. 1493-1504 ◽  
Author(s):  
Elia Formisano ◽  
Fabrizio Esposito ◽  
Francesco Di Salle ◽  
Rainer Goebel
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Time Series

Independent component analysis separated multiple task components in one fMRI time series

NeuroImage ◽  
2000 ◽  
Vol 11 (5) ◽  
pp. S660
Author(s):  
Kayako Matsuo ◽  
Shigeru Muraki ◽  
Tomohisa Okada ◽  
Tetsuo Moriya ◽  
Toshihara Nakai
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Time Series ◽  
Multiple Task

scholarly journals Cortex-based independent component analysis of fMRI time-series

NeuroImage ◽  
2001 ◽  
Vol 13 (6) ◽  
pp. 119 ◽  
Author(s):  
Elia Formisano ◽  
Fabrizio Esposito ◽  
Francesco Di Salle ◽  
Rainer Goebel
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Fmri Time Series

Outliers Detection in Multivariate Time Series by Independent Component Analysis

2007 ◽  
Vol 19 (7) ◽  
pp. 1962-1984 ◽  
Author(s):  
Roberto Baragona ◽  
Francesco Battaglia
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Model Building ◽  
Multivariate Time Series ◽  
Component Analysis ◽  
Independent Component ◽  
Level Shifts ◽  
The Common ◽  
Identification Techniques ◽  
Pattern Occurrences

In multivariate time series, outlying data may be often observed that do not fit the common pattern. Occurrences of outliers are unpredictable events that may severely distort the analysis of the multivariate time series. For instance, model building, seasonality assessment, and forecasting may be seriously affected by undetected outliers. The structure dependence of the multivariate time series gives rise to the well-known smearing and masking phenomena that prevent using most outliers' identification techniques. It may be noticed, however, that a convenient way for representing multiple outliers consists of superimposing a deterministic disturbance to a gaussian multivariate time series. Then outliers may be modeled as nongaussian time series components. Independent component analysis is a recently developed tool that is likely to be able to extract possible outlier patterns. In practice, independent component analysis may be used to analyze multivariate observable time series and separate regular and outlying unobservable components. In the factor models framework too, it is shown that independent component analysis is a useful tool for detection of outliers in multivariate time series. Some algorithms that perform independent component analysis are compared. It has been found that all algorithms are effective in detecting various types of outliers, such as patches, level shifts, and isolated outliers, even at the beginning or the end of the stretch of observations. Also, there is no appreciable difference in the ability of different algorithms to display the outlying observations pattern.

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