Independent Component Analysis has been recently used to solve Blind Source Separation-style problems. In this paper, the implementation of a fixed-point ICA algorithm similar to FastICA is described, and its performance is evaluated. The algorithm is used to perform denoising and source separation on signals recorded by a distributed wireless sensor network performing vehicle tracking in a mil...

In this paper we present a technique to speed up ICA based on the idea of reducing the dimensionality of the data set preserving the quality of the results. In particular we refer to FastICA algorithm which uses the Kurtosis as statistical property to be maximized. By performing a particular Johnson-Lindenstrauss like projection of the data set, we find the minimum dimensionality reduction rate...

In this work we show how temporal structures in time series can be used in the framework of independent component analysis assuming the signals arise from Markov chains with finite order. Taking into account the past of the underlying processes, by using time embedding vectors, not only instantaneous independent but also uncoupled sources can be found. As a result signals which are gaussian dis...

FastICA is a popular method for Independent Component Analysis used for separation of linearly mixed independent sources. The separation proceeds through optimization of a contrast function that is based on kurtosis or other entropy approximations using a nonlinear function. The EFICA algorithm is a recently proposed version of this algorithm that is asymptotically efficient when all source dis...

Acquisitions of body surface potentials require in some studies that, patients perform exercise on cycle ergometer to increase their heart rate. Under this condition recordings are highly affected by the muscular activity derived from the pedalling (motion artefact). To reject this interference we propose the use of independent component analysis (ICA) by applying two different algorithms (Fast...

An extension of the FastICA algorithm is proposed for the blind separation of both Q-proper and Q-improper quaternionvalued signals. This is achieved by maximising a negentropy-based cost function, and is implemented using the Newton method in the augmented quaternion statistics framework. It is shown that the use of augmented statistics and the associated widely linear modeling provide theoret...

We derive new fixed-point algorithms for the blind separation of complex-valued mixtures of independent, noncircularly symmetric, and non-Gaussian source signals. Leveraging recently developed results on the separability of complex-valued signal mixtures, we systematically construct iterative procedures on a kurtosis-based contrast whose evolutionary characteristics are identical to those of th...

introduction: the accuracy of analyzing functional mri (fmri) data is usually decreases in the presence of noise and artifact sources. a common solution in for analyzing fmri data having high noise is to use suitable preprocessing methods with the aim of data denoising. some effects of preprocessing methods on the parametric methods such as general linear model (glm) have previously been evalua...

We present the application of the Fast Independent Component Analysis (FastICA) technique for blind component separation to polarised astrophysical emission. We study how the Cosmic Microwave Background (CMB) polarised signal, consisting of E and B modes, can be extracted from maps affected by substantial contamination from diffuse Galactic foregrounds and instrumental noise. We perform the ana...

We present the application of the fast independent component analysis (FASTICA) technique for blind component separation to polarized astrophysical emission. We study how the cosmic microwave background (CMB) polarized signal, consisting of E and B modes, can be extracted from maps affected by substantial contamination from diffuse Galactic foreground emission and instrumental noise. We impleme...