Anton Chernihovskyi

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SUMMARY Although there are numerous studies exploring basic neuronal mechanisms that are likely to be associated with seizures, to date no definite information is available as to how, when, or why a seizure occurs in humans. The fact that seizures occur without warning in the majority of cases is one of the most disabling aspects of epilepsy. If it were(More)
We propose a method for estimating phase synchronization between time series using the parallel computing architecture of cellular nonlinear networks (CNN's). Applying this method to time series of coupled nonlinear model systems and to electroencephalographic time series from epilepsy patients, we show that an accurate approximation of the mean phase(More)
The detection of patterns embedded within a complex, nonstationary, and noisy background activity is a crucial and important task in EEG analysis. The authors present a biologically inspired, analog approach to EEG analysis that is conceptually different from a variety of statistical approaches currently used. A nonlinear, excitable, spatially extended(More)
We present a method for estimating the degree of generalized synchronization between long-lasting multichannel recordings of brain electrical activity from epilepsy patients. Using the nonlinear interdependency measure N as an estimator for generalized synchronization and the parallel computing power of a cellular nonlinear network (CNN) with(More)
We apply the method of frequency-selective excitation waves in excitable media to characterize synchronization phenomena in interacting complex dynamical systems by measuring coincidence rates of induced excitations. We analyzed time series of coupled nonlinear model systems and multi-channel, multi-day electroencephalographic recordings from epilepsy(More)
We present a biologically inspired approach to time series analysis by means of nonlinear excitable media simulated with cellular neural networks. Following main principles of biophysical and neuronal mechanisms underlying sound processing in mammals we develop a method for the noise-tolerant instantaneous detection of transient spectral patterns. We also(More)
The ability to quantify structural attributes using cellular neural networks (CNN) has been shown for a wide range of objects. We here introduce an application that allows the detection of structural alterations in the human brain. Using a CNN-based classification approach we show that a defined class of abnormalities - the so called hippocampal sclerosis -(More)
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