Present paper is devoted to the study of intermittency during the perception of bistable Necker cube image being a good example of an ambiguous object, with simultaneous measurement of EEG. Distributions of time interval lengths corresponding to the left-oriented and right-oriented cube perception have been obtain. EEG data have been analyzed using continuous wavelet transform and it was shown that the destruction of alpha rhythm with accompanying generation of high frequency oscillations can serve as a marker of Necker cube recognition process.
Characteristics of intermittency during the perception of ambiguous images have been studied in the case the Necker cube image has been used as a bistable object for demonstration in the experiments, with EEG being simultaneously measured. Distributions of time interval lengths corresponding to the left-oriented and right-oriented Necker cube perception have been obtain. EEG data have been analyzed using continuous wavelet transform which was shown that the destruction of alpha rhythm with accompanying generation of high frequency oscillations can serve as a electroencephalographical marker of Necker cube recognition process in human brain.
KEYWORDS: Neural networks, Oscillators, Hemodynamics, Chemical elements, Social networks, Solids, Basic research, Biological research, Chemical analysis, Biology, Internet
In the present paper the mechanism of the global synchronization onset through the formation of the synchronous clusters in complex networks with different topologies of links (scale-free networks, small-world networks, random networks) is studied. We consider the dependencies of integral characteristics of synchronous dynamics (synchronization measure, number of synchronous clusters, etc) on coupling strength between nodes. As a basic element of the node oscillator we consider Kuramoto phase oscillator.
In this report we studied human brain activity in the case of bistable visual perception. We proposed a new approach for quantitative characterization of this activity based on analysis of EEG oscillatory patterns and evoked potentials. Accordingly to theoretical background, obtained experimental EEG data and results of its analysis we studied a characteristics of brain activity during decision-making. Also we have shown that decisionmaking process has the special patterns on the EEG data.
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