functional magnetic resonance imaging (fmri) is a safe and non-invasive way to assess brain functions by using signal changes associated with brain activity. the technique has become a ubiquitous tool in basic, clinical and cognitive neuroscience. this method can measure little metabolism changes that occur in active part of the brain. we process the fmri data to be able to find the parts of br...

Algebraic Independent Component Analysis (AICA) is a new ICA algorithm that exploits algebraic operations and vectordistance measures to estimate the unknown mixing matrix in a scaled algebraic domain. AICA possesses stability and convergence properties similar to earlier proposed geometic ICA (geo-ICA) algorithms, however, the choice of the proposed algebraic measures in AICA has several advan...

A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the original data. Well-known linear transformation methods include, for example, principal componen...

The analysis of electroencephalographic (EEG) recording is important both for brain research and for medical diagnosis and treatment. Independent Component Analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from the EEG recordings. Results show that ICA is a useful technique for the evaluation of different variables in the brain activity.

We propose a mixture model for blind source separation and deconvolution with adaptive source densities. Data is modelled as a multivariate locally linear random process. We derive an expression for the asymptotic likelihood of a linear process segment, which allows us to formulate and optimize a mixture model via the EM algorithm. The mixture model is able to represent nonstationary (locally, ...

We propose a parametric version of Independent Component Analysis (ICA) via Copulas families of multivariate distributions that join univariate margins to multivariate distributions. Our procedure exploits the role for copula models in information theory and in measures of association, specifically: the use of copulae densities as parametric mutual information, and as measures of association on...

Independent component analysis (ICA) is a method for automatically identifying the underlying factors in a given data set. This rapidly evolving technique is currently finding applications in analysis of biomedical signals (e.g. ERP, EEG, fMRI, optical imaging), and in models of visual receptive fields and separation of speech signals. This article illustrates these applications, and provides a...

This article describes a relatively new research topic called independent component analysis (ICA), which is becoming very popular in the signal processing literature and amongst those working in machine learning and data mining. The primary focus of ICA is to resolve the classical problem of blind source separation (BSS), in which an unknown mixture of nonGaussian signals is decomposed into it...