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In the present investigation a new methodology for macrostructural EEG characterization based on automatic segmentation has been applied to sleep analysis. A nonparametric statistical approach for EEG segmentation was chosen, because it minimizes the need for a priori information about a signal. The method provides the detection of change-points i.e.(More)
Interior proximal methods for variational inequalities are, in fact, designed to handle problems on polyhedral convex sets or balls, only. Using a slightly modified concept of Bregman functions, we suggest an interior proximal method for solving variational inequalities (with maximal monotone operators) on convex, in general non-polyhedral sets, including(More)
For variational inequalities characterizing saddle points of Lagragians associated with convex programming problems in Hilbert spaces, the convergence of an interior proximal method based on Bregman distance func-tionals is studied. The convergence results admit a successive approximation of the varia-tional inequality and an inexact treatment of the(More)
In this paper we clarify that the interior proximal method developed in [6] (vol. 27 of this journal) for solving variational inequalities with monotone operators converges under essentially weaker conditions concerning the functions describing the " feasible " set as well as the operator of the variational inequality. References [1] A. Auslender and M.(More)
We describe nonlinear deterministic versus stochastic methodology, their applications to EEG research and the neurophysiological background underlying both approaches. Nonlinear methods are based on the concept of attractors in phase space. This concept on the one hand incorporates the idea of an autonomous (stationary) system, on the other hand implicates(More)
On the basis of three different experiments: oddball task (visual, auditory, and audio-visual stimuli), modified Sternberg's, and multistage memory tasks, it was shown that: a) there was not a single typical spectral pattern type that would characterize the majority of the trials; b) the total number of the different spectral pattern types was limited; c)(More)
The short-term structure of electroencephalogram (EEG) spectral transformations during different brain functional states (closed/opened eyes and memory task) was studied. It was shown that approximately 50% of spectral pattern (SP) types occur not more than 2-3 times per 149 analysis epochs in a 1-min EEG. The remaining 50% of SP types were the same for the(More)