Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data is commonly carried out under the assumption that each source may be represented as a spatially fixed pattern of activation, which leads to the instantaneous mixing model. To allow modeling patterns of spatiotemporal dynamics, in particular, the flow of oxygenated blood, we have developed a convolutive ICA...

Abstract— After summarizing typical approaches for solving independent component analysis (ICA) problems, advances on the ICA studies that consider hybrid sources of both subGaussians and superGaussians and the ICA extensions that consider noise and temporal dependence among observations have been overviewed from the perspective of Bayesian Ying-Yang independence learning. Not only new insights...

In this paper we study the dependencies of features found by independent component analysis (ICA) in image data by examining how the activation of one feature changes certain statistics of the data. We look at how the PCA components are affected when we know a certain ICA feature is highly active, and also study the ICA components in this situation. This can be thought of as a simple form of tw...

Data sets acquired from functional magnetic resonance imaging (fMRI) contain both spatial and temporal structures. In order to blindly extract underlying activities, the common approach however only uses either spatial or temporal independence. More convincing results can be achieved by requiring the transformed data to be as independent as possible in both domains. First introduced by Stone, s...

Regional myocardial motion analysis is used in clinical routine to inspect cardiac contraction in myocardial diseases such as infarction or hypertrophy. Physicians/radiologists can recognize abnormal cardiac motion because they have knowledge about normal heart contraction. This paper explores the potential of Independent Component Analysis (ICA) to extract local myocardial contractility patter...

The analysis and characterization of atrial fibrillation requires the previous extraction of the atrial activity from the electrocardiogram, where the independent atrial and ventricular activities are combined in addition to noise. An independent component analysis method is proposed where additional knowledge about the time and statistical structure of the sources is incorporated. Finally, a c...

In this paper we examine how the activation of one independent component analysis (ICA) feature changes first and second order statistics of other independent components in image patches. Essential for observing these dependencies is normalizing patch statistics, and selecting patches according to activation. We then estimate a model predicting the conditional statistics of a component using th...

In this paper we propose to use an instantaneous ICA method (BLUES) to separate the instruments in a real music stereo recording. We combine two strong separation techniques to segregate instruments from a mixture: ICA and binary time-frequency masking. By combining the methods, we are able to make use of the fact that the sources are differently distributed in both space, time and frequency. O...

A complex version of the Darmois–Skitovitch theorem is proved using a multivariate extension of the latter by Ghurye and Olkin. This makes it possible to calculate the indeterminacies of independent component analysis (ICA) with complex variables and coe4cients. Furthermore, the multivariate Darmois–Skitovitch theorem is used to show uniqueness of multidimensional ICA, where only groups of sour...