A method has been developed for localizing spatiotemporal patterns observed in sequentially recorded biomedical images of laser scanning microscopy and reflecting the dynamics of the biological structures under study. By means of interpolation by radial basis functions of each individual image, a compact mathematical model of the space-time dynamics of the brightness function on a sequence of images is obtained. The subsequent localization of the structures of the sought-for dynamic patterns is carried out by means of the mathematical apparatus of singular spectral analysis. The results of experiments on optical visualization of the activity patterns of the olfactory bulb of a macrosmatic (rat) confirmed the efficiency of the developed method for localizing the reaction to biomarkers of human oncological diseases.

The study was aimed at developing a new automatic search technique for specific invariant patterns of movement-related brain potentials reflected in multidimensional electroencephalogram (EEG) signals. An adaptive band-pass filter with bandwidth closely matching the spectrum of the desired EEG pattern at the observed moment was synthesized based on the Singular Spectrum Analysis methodology. The preliminary filtering of the original EEG signals provides the required sensitivity for subsequent searching of time boundaries in patterns. The correctness of the developed method was confirmed with standard machine learning tools through the validation of the adaptive search method carried out on the general set of initial data. It is shown that the synthesized method has provided a reliable automatic search for induced pre-movement EEG patterns and the correct determination of their time boundaries (accuracy up 29% on average and reached maximum values to 100% for some individuals). The developed method expands the existing tools to improve the functionality and reliability of various Brain-computer interfaces for various purposes, including medical applications for paralyzed patients.

The study was aimed at developing a new automatic search technique for specific invariant patterns associated with the execution of voluntary motor activity (Readiness potentials, RP) reflected in multidimensional electroencephalogram (EEG) signals. The Hausdorff metric was used as a criterion function for searching specific movement-related EEG patterns in brain activity. To increase the metric sensitivity, an adaptive low-pass filter was synthesized with the multivariate singular spectrum analysis (SSA). It has been experimentally demonstrated that significant temporal characteristics of the brain potentials preceding movement execution in the frontal, central and parietal areas of the cerebral cortex, on average, were 240±90 ms. It is shown that the synthesized adaptive filter has provided a reliable automatic search for induced pre-movement EEG patterns and the correct determination of their time boundaries (accuracy up to 96%). The developed method expands the existing tools to improve the functionality and reliability of various Brain-computer interfaces including those based on randomly generated, user-induced patterns of brain activity associated with voluntary motor activity.

The study on the features of electrophysiological indicators associated with brain potentials is a key aspect in improving the accuracy and reliability of the Brain-computer interface. The study was aimed at developing a new automatic search technique for specific invariant patterns associated with the execution of voluntary motor activity reflected in multidimensional electroencephalogram (EEG) signals. The Hausdorff metric was used as a criterion function for searching specific EEG patterns in bioelectrical activity. In order to increase the metric sensitivity an adaptive low-pass filter was synthesized with the multivariate singular spectrum analysis. It is shown that the use of the synthesized adaptive filter has provided a reliable automatic search for induced premotor EEG patterns and the correct determination of their time boundaries (accuracy up to 96%). The developed method expands the existing tools to improve the functionality and reliability of various neural interfaces including those based on randomly generated, user-induced patterns of brain bioelectrical activity associated with motor activity.

An adaptive low-pass filter has been synthesized for automatic patterns detection of arbitrary mental movements in records of multidimensional electroencephalograms (EEG). The filter is based on the multivariate singular spectrum analysis. The effective bandwidth of the filter corresponds to the spectrum of the sought patterns of mental activity on the observed EEG time interval. The use of the synthesized filter provided a reliable automatic search for patterns and the correct determination of their time boundaries. The correctness of the results has been confirmed in experiments with 24 volunteers.

We propose a method for finding a priori undefined structures of unknown temporal fluctuations for frequency oscillators of various intensities as part of the output signals of synchronized dynamical systems. Unlike traditional approaches, the developed method is based on the continuous wavelet transform of the observed signal and is efficient in cases when frequency characteristics of the desired pattern are close to the noise characteristics of the output signal.

Statement of the problem: the article looks into a class of problems which require identification of hidden regularities in adjustment of the bioelectrical activity of living organisms registered against various stimuli, through search and temporal localization of patterns in noised electrograms containing useful information. One of the approaches to solving such problems is based on an analysis of the Shannon entropy calculated based on the components of the power spectrum and called the spectral entropy function. It is found that under the conditions providing that the patterns in question pertain to high-frequency rhythms, and the boundaries of their energy spectra are a priori unknown, the criterial functions of spectral entropy are of low sensitivity. Purpose of the research: to develop cost functions of entropy analysis which are sufficiently sensitive for searching for high-frequency patterns with a priori unknown parameters in noised electrograms. Results: development of a cost function that makes it possible to find the frequency sub-band where the spectral components of the patterns in question maximally contribute to the total spectrum power. The subsequent computation of the spectral entropy in the identified frequency sub-band provides a solution to the problem of search for the response patterns in noised electrograms under the above conditions. Practical significance: the results confirm the effectiveness of the developed functions whose use is limited by the requirement that the electrogram should be recorded on more than one lead.

Introduction: We discuss a wide range of problems about revealing hidden regularities in rearrangement of bio-electric activity of living organisms when it is registered on the background of various impacts, using look-up and temporal localization of event-related patterns in electrograms with noise. One of the approaches to solve such problems is based on Shannon entropy analysis calculated by the components of the power spectrum and called a function of spectrum entropy. When the sought patterns relate to high-frequency rhythms and their energy spectrum limits are a priori unknown, the cost functions of the spectrum entropy have low sensitivity. Purpose: Developing cost functions for entropy analysis which would have a sensitivity high enough to search for high-frequency patterns with a priori unknown parameters in electrograms with noise. Results: A cost function has been found which allows you to detect a frequency band corresponding to the maximum contribution of the spectral components of the sought patterns towards the total power of the spectrum. The subsequent calculation of the spectral entropy in the found frequency band provides a solution for the problem of finding event-related patterns under the conditions mentioned above. Practical relevance: The presented results confirm the effectiveness of using the developed functions. The only restriction is that an electrogram must be recorded on several electrodes.