Abstract. Here, a Separation Theorem about K-Independent Subspace Analysis (K ∈ {R,C} real or complex), a generalization of K-Independent Component Analysis (K-ICA) is proven. According to the theorem, K-ISA estimation can be executed in two steps under certain conditions. In the first step, 1-dimensional K-ICA estimation is executed. In the second step, optimal permutation of the K-ICA element...

Independent component analysis (ICA) has proved to be a highly useful tool for modeling brain data and in particular electroencephalographic (EEG) data. In this paper, a new method is presented that may better capture the underlying source dynamics than ICA algorithms hereto employed for brain signal analysis. We suppose that a brief, impulse-like activation of an effective signal source elicit...

Blind source separation (BSS) technique using independent component analysis (ICA) for acoustic signals has been developed over the last decade. This technique assumes that the source signals are mutually independent, and can estimate the source signals from the mixed signals without a priori information. Thus, this technique is highly applicable in high-quality hands-free telecommunication sys...

Blind source separation (BSS) or Independent component analysis (ICA) is a statistical analysis technique for expressing hidden components of random variables or signals. ICA is a generative model for the observed multivariate data. In this model the source signals are assumed to be nongaussian and mutually independent, and they are called the independent components of the observed data. The mi...

Independent component analysis (ICA) in the frequency domain is used for solving the problem of blind source separation (BSS). However, this method has some problems. For example, a general ICA algorithm cannot determine the permutation of signals which is important in the frequency domain ICA. In this paper, we propose an approach to the solution for a permutation problem. The idea is to effec...

Here, a separation theorem about Independent Subspace Analysis (ISA), a generalization of Independent Component Analysis (ICA) is proven. According to the theorem, ISA estimation can be executed in two steps under certain conditions. In the first step, 1-dimensional ICA estimation is executed. In the second step, optimal permutation of the ICA elements is searched for. We present sufficient con...

There is an increasing interest in analyzing brain images from various imaging modalities, that record the brain activity during functional task, for understanding how the brain functions as well as for the diagnosis and treatment of brain disease. Independent Component Analysis (ICA), an exploratory and unsupervised technique, separates various signal sources mixed in brain imaging signals suc...

INTRODUCTION (1)H-MRS signals from brain tissues capture information on in vivo brain metabolism and neuronal biomarkers. This study aims to advance the use of independent component analysis (ICA) for spectroscopy data by objectively comparing the performance of ICA and LCModel in analyzing realistic data that mimics many of the known properties of in vivo data. METHODS This work identifies k...

Convolution is a linear operation, and, consequently, can be formulated as a linear system of equations. If only the output of the system (the convolved signal) is known, then the problem is blind so that given one equation, two unknowns are sought. Here, the blind deconvolution problem is solved using independent component analysis (ICA). To facilitate this, several time lagged versions of the...

Electrocardiogram (ECG) signal processing aims basically 1) at artifact reduction to make the ECG signals cleaner and better interpretable by human or machine observers, 2) at revealing aspects not immediately observable in plain measured ECG signals even after artifact reduction, or 3) at diagnostics decision support and automated ECG signal interpretation, including classification of ECG sign...