Atrial fibrillation (AF) is the most common arrhythmia encountered in clinical practice. Adaptive noise canceller (ANC) and independent component analysis (ICA) are powerful tools for separating signals from their mixtures. For better understanding of the mechanism and characteristics of AF, the artifacts were eliminated in the measured signals from atrial. We use ANC to subtract the influences...

A recent paper by Daubechies et al. claims that two independent component analysis (ICA) algorithms, Infomax and FastICA, which are widely used for functional magnetic resonance imaging (fMRI) analysis, select for sparsity rather than independence. The argument was supported by a series of experiments on synthetic data. We show that these experiments fall short of proving this claim and that th...

In this paper we propose a new hybrid unsupervised / supervised learning scheme that integrates Independent Component Analysis (ICA) with the SupportVector Machine (SVM) approach and apply this new learning scheme to the face detection problem. In low-level feature extraction, ICA produces independent image bases that emphasize edge information in the image data. In high-level classification, S...

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.

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...

Independent component analysis (ICA), which separates fMRI data into spatially independent patterns of activity, has recently been shown to be a suitable method for exploratory fMRI analysis. The validity of the assumptions of ICA, mainly that the underlying components are spatially independent and add linearly, was explored with a representative fMRI data set by calculating the log-likelihood ...

Chemometric methods like Principal Component Analysis (PCA) and Evolving Factor Analysis (EFA) have been applied to improve peaks separation, especially in HPLC UV-DAD analysis. In this work, the Independent Component Analysis (ICA) was adopted for the separation of overlapping aromatic peaks and the simultaneous determination of the underlying spectra. The application of the method on middle p...

Speech is the fundamental means of communication among humans. Speech production is the process of converting a linguistic message to the acoustic waveform. Separating various linearly mixed speech signals is often modelled by famous cocktail party problem and can be achieved by a technique known as Independent Component Analysis (ICA). ICA is similar to PCA and Factor analysis but it works on ...

Conventional remote sensing classification techniques that model the data in each class with a multivariate Gaussian distribution are inefficient, as this assumption is generally not valid in practice. We present a novel, Independent Component Analysis (ICA) based approach for unsupervised classification of hyperspectral imagery. ICA, employed for a mixture model, estimates the data density in ...

Full-duplex communications systems that transmit and receive simultaneously suffer self-interference due to the mixing of transmitted signal weaker received at same node. The problem becomes compounded in Multi-Input Multi-Output (MIMO) systems, where considerable overhead is dedicated training. In this article, we discuss using blind source separation techniques, namely Independent Component A...