Marina V. Khramova

Time-frequency analysis of epileptic EEG patterns by means of empirical modes and wavelets

In this paper we perform a time-frequency analysis of epileptic EEG patterns based on two approaches for characterizing nonstationary multi-frequency signals, namely, the continuous wavelet transform (CWT) and the empirical mode decomposition (EMD). Possibilities and limitations of both these techniques are considered, and a combined approach for automatic pattern detection is proposed.

Perspective sub-THz powerful microwave generator nanovircator for T-rays biomedical diagnostics

In this paper we suggest the new approach of powerful sub-THz signal generation based on intense electron beams containing oscillating virtual cathode. Suggested compact microwave source complies with a number of biomedical applications such as imaging, preventive healthcare, etc. In this work we discuss the results of numerical simulation and optimization of the novel device called “nanovircator” that have been carried out. The results of the numerical study show the possibility of “nanovircator” operation at 0.1-0.4 THz frequency range.

Analysis of the characteristics of the synchronous clusters in the adaptive Kuramoto network and neural network of the epileptic brain

In the paper we study the mechanisms of phase synchronization in the adaptive model network of Kuramoto oscillators and the neural network of brain by consideration of the integral characteristics of the observed networks signals. As the integral characteristics of the model network we consider the summary signal produced by the oscillators. Similar to the model situation we study the ECoG signal as the integral characteristic of neural network of the brain. We show that the establishment of the phase synchronization results in the increase of the peak, corresponding to synchronized oscillators, on the wavelet energy spectrum of the integral signals. The observed correlation between the phase relations of the elements and the integral characteristics of the whole network open the way to detect the size of synchronous clusters in the neural networks of the epileptic brain before and during seizure.

Generalized synchronization in the complex network: theory and applications to epileptic brain

Generalized synchronization in complex networks with chaotic dynamical systems being in their nodes has been studied. The synchronous regime is shown to be detected by the sign-change of the second positive Lyapunov exponent of the network or by the nearest neighbor method. The same method is shown to be applied for the detection of the synchronous regime between the different fields of epileptic brain.

Organization of virtual environment for interaction of university and high-tech sector of the economy

The aim of this article is to examine the problems of the organization of virtual interaction between the university and the high-tech sector of the economy. One of such possible tools is information system realized with WEB-technologies services. It lets meet the innovative infrastructure, potential of the university, specific innovation and human resources in the innovation field, conduct preliminary analysis of mutually beneficial issues and identify the right contacts to interact. However, it is important to prepare the youth to activities in innovative projects. For these purposes, the use of expert systems to support innovative design activity of students is considered. It lets to students choose the best project leader of the student group of classmates and provides consulting support of innovative project activities. Another component of the expert system implemented using fuzzy logic device, will help students to evaluate the project. Thus, a virtual environment allows to remove some routine burden of the organizers from the beginning to the completion of the project.

THz-generation in semiconductor superlattice in the external tilted magnetic field

We study effects of the external tilted magnetic field on the generation of sub-THz/THz oscillations in the semiconductor superlattice. We show that this field provides the increased power of harmonics in the THz range. Changing the tilt angle essentially influences the distribution of spectral power of current oscillations in the semiconductor superlattice.

Analysis of structural patterns in the brain with the complex network approach

In this paper we study mechanisms of the phase synchronization in a model network of Van der Pol oscillators and in the neural network of the brain by consideration of macroscopic parameters of these networks. As the macroscopic characteristics of the model network we consider a summary signal produced by oscillators. Similar to the model simulations, we study EEG signals reflecting the macroscopic dynamics of neural network. We show that the appearance of the phase synchronization leads to an increased peak in the wavelet spectrum related to the dynamics of synchronized oscillators. The observed correlation between the phase relations of individual elements and the macroscopic characteristics of the whole network provides a way to detect phase synchronization in the neural networks in the cases of normal and pathological activity.

Age-dependent seizures of absence epilepsy and sleep spindles dynamics in WAG/Rij rats

In the given paper, a relation between time-frequency characteristics of sleep spindles and the age-dependent epileptic activity in WAG/Rij rats is discussed. Analysis of sleep spindles based on the continuous wavelet transform is performed for rats of different ages. It is shown that the epileptic activity affects the time-frequency intrinsic dynamics of sleep spindles.

Multifractal analysis of real and imaginary movements: EEG study

We study abilities of the wavelet-based multifractal analysis in recognition specific dynamics of electrical brain activity associated with real and imaginary movements. Based on the singularity spectra we analyze electroencephalograms (EEGs) acquired in untrained humans (operators) during imagination of hands movements, and show a possibility to distinguish between the related EEG patterns and the recordings performed during real movements or the background electrical brain activity. We discuss how such recognition depends on the selected brain region.

Human personality reflects spatio-temporal and time-frequency EEG structure

The reliable and objective assessment of intelligence and personality has been a topic of increasing interest of contemporary neuroscience and psychology. It is known that intelligence can be measured by estimating the mental speed or velocity of information processing. This is usually measured as a reaction time during elementary cognitive task processing, while personality is often assessed by means of questionnaires. On the other hand, human personality affects the way a subject accomplishes elementary cognitive tasks and, therefore, some personality features can define intelligence. It is expected that these features, as well as mental abilities in performing cognitive tasks are associated with the brain’s electrical neural activity. Although several studies reported correlation between event-related potentials, mental ability and intelligence, there is a lack of information about time-frequency and spatio-temporal structures of neural activity which characterize this relation. In the present work, we analyzed human electroencephalograms (EEG) recorded during the performance of elementary cognitive tasks using the Schulte test, which is a paper-pencil based instrument for assessing elementary cognitive ability or mental speed. According to particular features found of the EEG structure, we divided the subjects into three groups. For subjects in each group, we applied the Sixteen Personality Factor Questionnaire (16PF) to assess the their personality traits. We demonstrated that each group exhibited a different score on the personality scale, such as warmth, reasoning, emotional stability and dominance. Summing up, we found a link between EEG features, mental abilities and personality traits. The obtained results can be of great interest for testing human personality to create automatized intelligent programs which combine simple tests and EEG measurements for real estimation of human personality traits and mental abilities.