ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)


signal processing

Diagnostics and analysis of oscillatory neuronal network activity of brain with continuous wavelet analysis

In the article we present an overview of a number of continuous wavelet transformbased techniques for analysis and diagnostic of oscillatory neuronal network activity of brain in experimentally obtained electroencephalographic data. We describe a technique for automatic detection of characteristic patterns for paroxysmal activity (spike-wave discharges) in epileptic electroencephalogram (EEG) based on wavelet spectrum power analysis, obtained with continuous wavelet transform with complex mother wavelet (Morlet) in specific frequency ranges.

Modeling from time series and applications to processing of complex signals

Signals obtained from most of real-world systems, especially from living organisms, are irregular, often chaotic, non-stationary, and noise-corrupted. Since modern measuring devices usually realize digital processing of information, recordings of the signals take the form of a discrete sequence of samples (a time series). The present paper gives a brief overview of the possibilities of such experimental data processing based on reconstruction and usage of a predictive empirical model of a time realization under study.

A neural network as a predictor of the discrete map

The possibility of predicting the regular and chaotic dynamics of a discrete map by using artificial neural network is studied. The method of error back­propagation is used for calculation the coefficients of the multilayer network. The predicting properties of the neural network are explored in a wide region of the system parameter for both regular and chaotic behaviors. The dependance of the prediction accuracy from the degree of chaos and from the number of layers of the network is studied.