Biomedical subjects

Andrzej Cichocki

Publications and source records attributed to Andrzej Cichocki.

5 recordsLinked to original sources

PubMed2003

Wavelet-like receptive fields emerges by non-linear minimization of neuron error.

Redundancy reduction as a form of neural coding has been since the early sixties a topic of large research interest. A number of strategies has been proposed, but the one which is attracting most attention recently assumes that this coding is carried out so that the output signals are mutually independent. In this work we go one step further and suggest an strategy to deal also with non-orthogonal signals (i.e., "dependent" signals). Moreover, instead of working with the usual squared error, we design a neuron where the non-linearity is operating on the error. It is computationally more economic and, importantly, the permutation/scaling problem is avoided. The framework is given with a biological background, as we avocate throughout the manuscript that the algorithm fits well the single neuron and redundancy reduction doctrine. Moreover, we show that wavelet-like receptive fields emerges from natural images processed by this algorithm.

Algorithms↗

PubMed2002

Blind noise reduction for multisensory signals using ICA and subspace filtering, with application to EEG analysis.

In many applications of signal processing, especially in communications and biomedicine, preprocessing is necessary to remove noise from data recorded by multiple sensors. Typically, each sensor or electrode measures the noisy mixture of original source signals. In this paper a noise reduction technique using independent component analysis (ICA) and subspace filtering is presented. In this approach we apply subspace filtering not to the observed raw data but to a demixed version of these data obtained by ICA. Finite impulse response filters are employed whose vectors are parameters estimated based on signal subspace extraction. ICA allows us to filter independent components. After the noise is removed we reconstruct the enhanced independent components to obtain clean original signals; i.e., we project the data to sensor level. Simulations as well as real application results for EEG-signal noise elimination are included to show the validity and effectiveness of the proposed approach.

Acoustic Stimulation↗

PubMed2002

Equivariant nonstationary source separation.

Most of source separation methods focus on stationary sources, so higher-order statistics is necessary for successful separation, unless sources are temporally correlated. For nonstationary sources, however, it was shown [Neural Networks 8 (1995) 411] that source separation could be achieved by second-order decorrelation. In this paper, we consider the cost function proposed by Matsuoka et al. [Neural Networks 8 (1995) 411] and derive natural gradient learning algorithms for both fully connected recurrent network and feedforward network. Since our algorithms employ the natural gradient method, they possess the equivariant property and find a steepest descent direction unlike the algorithm [Neural Networks 8 (1995) 411]. We also show that our algorithms are always locally stable, regardless of probability distributions of nonstationary sources.

Algorithms↗

PubMed1997

A Minor Component Analysis Algorithm.

The eigenvectors corresponding to the smallest eigenvalues of the autocorrelation matrix of the input signals are defined as the minor components, which play a very important role in many fields of adaptive signal processing such as spectral estimation, total least squares processing, eigen-based bearing estimation, digital beamforming, moving target indication, and clutter cancellation. This paper proposes a learning algorithm which extracts adaptively the minor component. We will use the Rayleigh quotient as an energy function and prove both analytically and by simulation results that the weight vector provided by the proposed algorithm is guaranteed to converge to the minor component of the input signals. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.

Journal Article↗

PubMed1997

Stability Analysis of Learning Algorithms for Blind Source Separation.

Recently a number of adaptive learning algorithms have been proposed for blind source separation. Although the underlying principles and approaches are different, most of them have very similar forms. Two important issues remained to be elucidated further: the statistical efficiency and the stability of learning algorithms. The present letter analyzes a general form of statistically efficient algorithms and gives a necessary and sufficient condition for the separating solution to be a stable equilibrium of a general learning algorithm. Moreover, when the separating solution is unstable, a simple method is given for stabilizing the separating solution by modifying the algorithm.

Journal Article↗