Independent component analysis (ICA) is a computational mehtod to solve blind source separation (BSS) problem. Different kinds of classic measure can be used for the estimation of nonGaussian sources by ICA. In this paper we review independent componenet analysis (ICA) technique based on Kurtosis contrast function. We briefly present the common independent component analysis algorithms that use...

Independent component analysis (ICA) is a technique that attempts to separate data into maximally independent groups. Achieving maximal independence in space or time yields two varieties of ICA meaningful for functional MRI (fMRI) applications: spatial ICA (SICA) and temporal ICA (TICA). SICA has so far dominated the application of ICA to fMRI. The objective of these experiments was to study IC...

This paper evaluates the performance of some major Independent Component Analysis (ICA) algorithms like Hyv ̈arinen’s fixed point algorithm, Pearson based ICA algorithm and OGWE (Optimized Generalized Weighted Estimator) ICA algorithm in a biomedical blind source separation problem. Independent signals representing Fetal ECG (FECG) and Maternal ECG (MECG) generated and then mixed linearly to sim...

This communication aims at giving some insights into the use of Independent Component Analysis (ICA) for solving biomedical problems. First the concept of ICA is reviewed and different classes of ICA methods are described. Next a survey on most encountered biomedical problems solved using ICA is detailed. Finally a comparative performance study of thirteen ICA algorithms is performed on biomedical

We propose an Iterative Nonlinear Gaussianization Algorithm (INGA) which seeks a nonlinear map from a set of dependent random variables to independent Gaussian random variables. A direct motivation of INGA is to extend principal component analysis (PCA), which transforms a set of correlated random variables into uncorrelated (independent up to second order) random variables, and Independent Com...

Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data reveals spatially independent patterns of functional activation. The purely datadriven approach of ICA makes statistical inference difficult. The purpose of this study was to develop a hybrid ICA in the frequency domain that enables statistical inference while preserving advantages of a data-driven ICA. Th...

ICA (Independent Component Analysis) is a new technique for analyzing multi-variant data. Lots of results are reported in the eld of neurobiological data analysis such as EEG (Electroencephalography), MRI (Magnetic Resonance Imaging), and MEG (Magnetoencephalography) using ICA. But there still remain problems. In most of the neurobiological data, there are a large amount of noise, and the numbe...

background: fetal electrocardiography is a developing field that provides valuable information on the fetal health during pregnancy. by early diagnosis and treatment of fetal heart problems, more survival chance is given to the infant. objective: here, we extract fetal ecg from maternal abdominal recordings and detect r-peaks in order to recognize fetal heart rate. on the next step, we find a b...

1 ICA 2 1.1 Examples of linear mixtures of independent components . . . . . 2 1.2 Basic assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 ICA from Maximum Likelihood . . . . . . . . . . . . . . . . . . . 4 1.4 PCA and ICA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.5 Minimal Mutual Information . . . . . . . . . . . . . . . . . . . . 6 1.6 Maximum Transmitte...

Independent Component Analysis (ICA) is a fundamental method for Blind Source Separation (BSS). Classical ICA takes data matrix input formed by vector data. This paper focuses on BSS with third-order tensor data, such as 2D images. Two approaches exist this problem. The first approach reshapes each into to apply classical ICA, structural information lost. second unfolds along different modes pe...