Independent Component Analysis (ICA) is intended to recover the mutually independent sources from their linear mixtures, and F astICA one of most successful ICA algorithms. Although it seems reasonable improve performance by introducing more nonlinear functions negentropy estimation, original fixed-point method (approximate Newton method) in degenerates under this circumstance. To alleviate pro...

Principal component analysis (PCA) is used to reduce dimensionality of electrocardiogram (ECG) data prior to performing independent component analysis (ICA). A newly developed PCA variance estimator by the author has been applied for detecting true, actual and false peaks of ECG data files. In this paper, it is felt that the ability of ICA is also checked for parameterization of ECG signals, wh...

Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decom...

Nowadays, Blind Source Separation (BSS) techniques are very common and useful in signal processing. In the field of multichannel recording, there are many techniques of BSS that work accurately, but in the single channel measurement, only a few methods are existed. One of the much popular algorithms of BSS is Independent Component Analysis (ICA). This technique is applied to separate the indepe...

Analysis and modeling of speaker variability, such as gender, accent, age, speech rate, and phones realizations, are important issues in speech recognition. It is known that existing feature representations describing speaker variations can be of very high dimension. In this paper, we introduce two powerful multivariate statistical analysis methods, namely, principal component analysis (PCA) an...

The problem of extracting a blood vessel-related component from dynamic brain PET images is similar to the ICA analysis of fMRI data. Unique characteristics of this problem are: (1) the spatial distribution of vessels can be acquired by PET, and therefore the property of the probability distribution of the vessel component is known; and (2) independent maps and the mixing matrix are all nonnega...

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...

Cocktail-party Problems (increasing generality): • Independent component analysis (ICA) [1, 2]: onedimensional sound sources. • Independent subspace analysis (ISA) [3]: independent groups of people. • Blind source deconvolution (BSD) [4]: one-dimensional sound sources and echoic room. • Blind subspace deconvolution (BSSD) [5]: independent source groups and echoes. Separation Theorem: • ISA ([3]...

Independent component analysis (ICA) has become a standard data analysis technique applied to an array of problems in signal processing and machine learning. This tutorial provides an introduction to ICA based on linear algebra formulating an intuition for ICA from first principles. The goal of this tutorial is to provide a solid foundation on this advanced topic so that one might learn the mot...

Independent component analysis (ICA) is a data-driven approach to study functional magnetic resonance imaging (fMRI) data. Particularly, for group analysis on multiple subjects, temporally concatenation group ICA (TC-GICA) is intensively used. However, due to the usually limited computational capability, data reduction with principal component analysis (PCA: a standard preprocessing step of ICA...