In this contribution, we propose approaches to independent component analysis (ICA) when the measured signals are contaminated by additive noise. We extend existing adaptive algorithms with equivariant properties in order to considerably reduce the bias in the demixing matrix caused by measurement noise. Moreover, we describe a novel recurrent dynamic neural network for simultaneous estimation ...

Measures of independence (and dependence) are fundamental in many areas of engineering and signal processing. Shannon introduced the idea of Information Entropy which has a sound theoretical foundation but sometimes is not easy to implement in engineering applications. In this paper, Renyi’s Entropy is used and a novel independence measure is proposed. When integrated with a nonparametric estim...

Biomedical signals can arise from one or many sources including heart, brains and endocrine systems. Multiple sources poses challenge to researchers which may have contaminated with artifacts and noise. The Biomedical time series signal like electroencephalogram (EEG), electrocardiogram (ECG), etc. The morphology of the cardiac signal is very important in most of diagnostics based on the ECG. T...

This discussion paper proposes to generalize the notion of Independent Component Analysis (ICA) to the notion of Multidimensional Independent Component Analysis (MICA). We start from the ICA or blind source separation (BSS) model and show that it can be uniquely identified provided it is properly parameterized in terms of one-dimensional subspaces. From this standpoint, the BSS/ICA model is gen...

Independent Component Analysis (ICA) and its mathematical ideas are presented for the problem of Blind Signal Separation (BSS) and multichannel blind deconvolution of independent source signals. BSS and ICA are emerging techniques that aspire to recover unobserved signals or sources from the observed mixtures. The aims of this paper are to review some new approaches and implement some new and u...

In many daily-life scenarios, acoustic sources recorded in an enclosure can only be observed with other interfering sources. Hence, convolutive Blind Source Separation (BSS) is a central problem audio signal processing. Methods based on Independent Component Analysis (ICA) are especially important this field as they require few and weak assumptions allow for blindness regarding the original sou...

In solving the problem of noiseless independent component analysis (ICA) in which sources of superand sub-Gaussian coexist in an unknown manner, one can be lead to a feasible solution using the natural gradient learning algorithm with a kind of switching criterion for the model probability distribution densities to be selected as superor sub-Gaussians appropriately during the iterations. In thi...

Independent component analysis (ICA) is a promising analysis method that is being increasingly applied to fMRI data. A principal advantage of this approach is its applicability to cognitive paradigms for which detailed models of brain activity are not available. Independent component analysis has been successfully utilized to analyze single-subject fMRI data sets, and an extension of this work ...

Problem statement: Because of the distance between the skull and the brain and their different resistivity’s, Electroencephalogram (EEG) recordings on a machine is usually mixed with the activities generated within the area called noise. EEG signals have been used to diagnose major brain diseases such as Epilepsy, narcolepsy and dementia. The presence of these noises however can result in misdi...

introduction: the accuracy of analyzing functional mri (fmri) data is usually decreases in the presence of noise and artifact sources. a common solution in for analyzing fmri data having high noise is to use suitable preprocessing methods with the aim of data denoising. some effects of preprocessing methods on the parametric methods such as general linear model (glm) have previously been evalua...