scholarly journals Spatial independent component analysis of functional MRI time-series: To what extent do results depend on the algorithm used?

2002 ◽  
Vol 16 (3) ◽  
pp. 146-157 ◽  
Author(s):  
Fabrizio Esposito ◽  
Elia Formisano ◽  
Erich Seifritz ◽  
Rainer Goebel ◽  
Renato Morrone ◽  
...  
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Functional Mri ◽  
Component Analysis ◽  
Independent Component

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.

scholarly journals Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers

NeuroImage ◽  
2014 ◽  
Vol 90 ◽  
pp. 449-468 ◽  
Author(s):  
Gholamreza Salimi-Khorshidi ◽  
Gwenaëlle Douaud ◽  
Christian F. Beckmann ◽  
Matthew F. Glasser ◽  
Ludovica Griffanti ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Functional Mri ◽  
Component Analysis ◽  
Independent Component ◽  
Fusion Of Classifiers
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