It has previously been suggested that the visual cortex performs a data analysis similar to independent component analysis (ICA). Following this idea we show that an incomplete ICA, applied after filtering, can be used to detect objects in natural scenes. Based on this we show that an incomplete ICA can be used to efficiently cluster independent components. We further apply this algorithm to to...

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...

We have developed an iterative approach based on Independent Component Analysis (ICA) to obtain improved estimates of auditory evoked potentials (AEPs), which represent the electrical response of the brain to auditory stimuli. AEPs are often contaminated by artifacts which reduce the localization accuracy of the sources underlying the surface recordings. We use ICA to separate the activity of n...

Blind source separation is an important but highly challenging technology in astronomy, physics, chemistry, life science, medical science, earth science, and applied sciences. Independent Component Analysis (ICA) employed technologies in applied computer science for blind source separation. In the separation of blind sources under multiple sensors, it can estimate approximately the types of sig...

Independent Component Analysis (ICA) is a recently developed technique that in many cases characterizes the data in a natural way. The main application area of the linear ICA model is blind source separation. Here, unknown source signals are estimated from their unknown linear mixtures using the strong assumption that the sources are mutually independent. In practice, separation can be achieved...

Independent component analysis (ICA) the theory of mixed, independent, non-Gaussian sources has a central role in signal processing, computer vision and pattern recognition. One of the most fundamental conjectures of this research eld is that independent subspace analysis (ISA) the extension of the ICA problem, where groups of sources are independent can be solved by traditional ICA followed by...

Spatial independent component analysis (ICA) applied to functional magnetic resonance imaging (fMRI) data identifies functionally connected networks by estimating spatially independent patterns from their linearly mixed fMRI signals. Several multi-subject ICA approaches estimating subject-specific time courses (TCs) and spatial maps (SMs) have been developed, however, there has not yet been a f...

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...

The performance of six neuromorphic adaptive structurally different algorithms was analyzed in blind separation of independent artificially generated signals using the stationary linear independent component analysis (ICA) model. The estimated independent components were ranked and compared among different ICA approaches. All algorithms were run with different contrast functions, which were opt...

In this work, we apply the independent component analysis (ICA) on the extraction of artifacts from the electrocardio-graphic (ECG) signals. ECG analysis is not an easy task when artifacts (electrodes, muscle, breathing, etc) corrupts the ECG, hiding important information. If the mixed signals in the ECG recordings (heart signal and artifacts) are statistically independent, the ICA can blindly ...