When data is sampled from an unknown subspace, principal component analysis (PCA)
provides an effective way to estimate the subspace and hence reduce the dimension of the …

We propose novel transforms of stochastic vectors, called the generalized Brillinger
transforms (GBT1 and GBT2), which are generalizations of the Brillinger transform (BT). The …

We propose two techniques for the fast numerical implementation of a random vector
transform within Wiener filtering paradigm. In signal processing terminology, the transform is …

In this paper, we propose spatial filters for a linear regression model, which are based on the
minimum-variance pseudo-unbiased reduced-rank estimation (MV-PURE) framework. As a …

The stochastic minimum-variance pseudo-unbiased reduced-rank estimator (stochastic MV-
PURE estimator) has been developed to provide linear estimation with robustness against …

Brain activity pattern recognition from EEG or MEG signal analysis is one of the most
important method in cognitive neuroscience. The supFunSim library is a new Matlab toolbox …

The breakthrough ideas in the modern proximal splitting methodologies allow us to express
the set of all minimizers of a superposition of multiple nonsmooth convex functions as the …

Localization of sources of brain electrical activity from electroencephalographic and
magnetoencephalographic recordings is an ill-posed inverse problem. Therefore, the best …

We consider the problem of localization of sources of brain electrical activity from
electroencephalographic (EEG) and magnetoencephalographic (MEG) measurements …

Our work addresses a development and justification of the new approach to the modeling of
nonlinear systems. Let $\f $ be an unknown input-output map of the system with a random …