Independent component analysis (ICA) is a data-driven approach that has exhibited great utility for functional magnetic resonance imaging (fMRI). Standard ICA implementations, however, do not provide the number and relative importance of the resulting components. In addition, ICA algorithms utilizing gradient-based optimization give decompositions that are dependent on initialization values, wh...

We propose a new two-stage blind separation and deconvolution strategy for multiple-input multiple-output (MIMO)-FIR systems driven by colored sound sources, in which single-input multiple-output (SIMO)-model-based ICA (SIMO-ICA) and blind multichannel inverse filtering are combined. SIMO-ICA can separate the mixed signals, not into monaural source signals but into SIMO-model-based signals from...

Guided by the principles of geometric independent component analysis (ICA), we present a new approach (SOMICA) to linear geometric ICA using a self-organizing map (SOM). We observe a considerable improvement in separation quality of different distributions, albeit at high computational costs. The SOMICA algorithm is therefore primarily interesting from a theoretical point of view bringing toget...

Independent component analysis (ICA) methods have received growing attention as effective data-mining tools for microarray gene expression data. As a technique of higher-order statistical analysis, ICA is capable of extracting biologically relevant gene expression features from microarray data. Herein we have reviewed the latest applications and the extended algorithms of ICA in gene clustering...

We present a method to deal with adaptive noise cancelling based on independent component analysis (ICA). Although popular least-mean-squares (LMS) algorithm removes noise components based on second-order correlation, the proposed ICA-based algorithm can utilize higher-order statistics. Additionally, extending to transform-domain adaptive filtering (TDAF) methods, normalized ICA-based algorithm...

The over-complete case remains a difficult problem in the field of independent component analysis (ICA). In this article we combine a technique called “region of interest” (ROI) with a standard complete ICA. We show how to create a mask using ICA, then using the masked data for a second ICA. At the same time this method eliminates a commonly necessary model-based step in fMRI data analysis. We ...

Independent Component Analysis (ICA) can be described in several ways, one of which is as a technique that seeks to find a set directions (components) underlying multivariate data that are most independent of one another. While there are several ICA models and many ICA methods, in this report we focus on the most basic model and one of the most popular and simple algorithms; the One-Unit FastIC...

This study presents a modified infomax model of Independent Component Analysis (ICA) for the source separation problem of fMRI data. Functional MRI data is processed by different blind source separation techniques including Independent Component Analysis (ICA). ICA is a statistical decomposition method used for multivariate data source separation. ICA algorithm is based on independence of extra...

In this paper, a data-driven temporal processing method based on Independent Component Analysis (ICA) is proposed for obtaining a more robust speech representation. Two different schemes of dominant temporal filters based on ICA are investigated. The one is the perceptuallybased filter which always focuses on the modulation frequency range between 1 and 16 Hz and the other is the most independe...

With Independent Component Analysis (ICA) the objective is to separate multidimensional data into independent components. A well known problem in ICA is that since both the independent components and the separation matrix have to be estimated, neither the ordering nor the amplitudes of the components can be determined. One suggested method for solving these ambiguities in ICA is to measure the ...