This PDF file contains the front matter associated with SPIE Proceedings Volume 11459, including the Title Page, Copyright information, Table of Contents, Author and Conference Committee lists.

Recognition of specific oscillatory patterns in human electroencephalograms (EEGs) is an important problem that has attracted significant attention for creating brain-computer interfaces (BCIs). Some of these patterns are easily identified by various numerical methods. However, it is much more difficult to recognize mental intentions that can be further transformed into control commands for hardware, and the choice of the appropriate numerical tool becomes very important. In this study, we compare several numerical methods applied to multichannel EEGs recorded in untrained volunteers who imagined arm and leg movements. We show that the quality of recognition varies between different methods and depends on the subject. We discuss the possibilities of reliable separation between imaginary movements of various types.

We have recorded multichannel EEG signals from subjects maintaining the body balance on the balance board. Having synchronized the board oscillations and the recordings we have revealed and described specific features of the cortical activity that relate to balance maintaining and reaching an equilibrium state. We have found that the increase of the equilibrium state duration is accompanied by the change of the EEG spectral amplitude in the β frequency band.

We propose an approach for motor-related brain activity analysis based on the combination of continuous wavelet transform and recurrence quantification analysis (RQA). Detecting such patterns on EEG is a complex task due to the nonstationarity and complexity of EEG signal, which leads to high inter- and intra-subject variability of traditionally applied methods. We show that RQA measures of complexity, such as recurrence rate an laminarity, are very useful in detection of transitions from background to motor-related EEG. Moreover, RQA measures time dependence for upper limbs is contralateral, which allows us to distinguish two types of movements.

The analysis of neurophysiological mechanisms responsible for motor imagery is essential for the development of brain-computer interfaces. The carried out magnetoencephalographic (MEG) experiments with voluntary participants confirm the existence of two types of motor imagery: kinesthetic imagery (KI) and visual imagery (VI), distinguished by activation and inhibition of different brain areas. For classification of the brain states associated with motor imagery, we used the hierarchical cluster analysis and a popular type of artificial neural networks called multilayer perceptron. The application of machine learning techniques allows us to classify motor imagery in raising right and left arms with an average accuracy of 70% for both KI and VI using appropriate filtration of input signals. The same average accuracy is achieved by optimizing MEG channels and reducing their number to only 13.

We develop a noninvasive brain-to-brain interface, which enables a dynamical redistribution of a cognitive workload between subjects based on their current cognitive performances. As a result, a participant who exhibits a higher performance is subjected to a higher workload, while his/her partner receives a lower workload. We demonstrate that the workload distribution allows increasing cognitive performance in the pair of interacting subjects.

It is known that brain performs cognitive functions through the activation of a distributed cortical network, which includes remote cortical regions. With this in mind we have analyzed the spatio-temporal cortical activity based on multichannel EEG recordings during accomplishing cognitive task. As the result, we have revealed typical spatio-temporal structures related to the different levels of cognitive task complexity.

We conducted the functional connectivity analysis of EEG recordings corresponding to motor execution and motor imagery. This study aims at finding the relationship between motor actions and neuronal interactions in different low-frequency bands: μ/α (8-13 Hz) and β (15-30 Hz). To reveal functional networks in mentioned frequency bands we develop and apply the novel model-free approach based on wavelet and recurrence analysis of multivariate time-series.

We have analyzed the neuronal interactions in the children's brain cortex associated with the cognitive activity during simple cognitive task (Schulte table) evaluation in two distinct frequency bands - alpha (8-13 Hz) and beta (15-30 Hz) ranges using linear Pearsons correlation-based connectivity analysis. We observed the task- related suppression of the alpha-band connectivity in the frontal, temporal and central brain areas, while in the parietal and occipital brain regions connectivity exhibits increase. We also demonstrated significant task-related increase of functional connectivity in the beta frequency band all over the distributed cortical network.

We analyzed EEG signals of children recorded during specific cognitive task - Schulte test. We analyzed behavioural characteristics - time intervals required for subject to find each consecutive number in table as well as frequency characteristics of EEG signal calculated with help of continuous wavelet transform considering the wavelet energies averaged over alpha and beta frequency ranges. We also performed statistical analysis of these characteristics with help of ANOVA to find features that can be used to evaluate level of attention and its dynamics during elementary task completion.

Experimental design for recording of EEG and fNIRS during performance of real and imaginary movement was proposed. Set of experiments was conducted in accordance with this design and obtained EEG and fNIRS dataset was analyzed. Analysis allowed to introduce certain features in time-frequency domain that can be used to separate real motor activity from motor imagery.

The work carried out an analysis of EEG data for various groups of patients - without problems with the cardiovascular system, with high blood pressure and after a stroke. It is shown that elevated pressure changes the usual picture of the electrical activity of the brain. Changes in the dynamics are similar to those observed in patients after a stroke.

We provided a combined analysis of electroencephalogram and functional near-infrared spectroscopy signals in order to investigate the process of prolonged visual perception. We investigated perception and decision-making processing during long-term and intense cognitive load. We found characteristic changes in electrical and hemodynamic activities during the neurophysiological experiment. The relationship was found between the EEG characteristics and the ΔO2Hb oscillation registered with the help of functional near-infrared spectroscopy.

A new method of spatial-temporal analysis based on using of the concept of continuous wavelet transform is proposed. This technique allows time to detect the dominant frequency in each of the EEG recording channels and visualize the results. The results of applying the developed method to EEG signals of rat brain activity are demonstrated. Various structural features of the vibrational activity of the brain during behavioral sleep in rats with abscess epilepsy and conditionally healthy ones have been shown.

In this article, we proposed a method for identifying the individual characteristics of motor activity based on recurrence analysis as applied to encepholography of the human brain. The analysis was carried out according to the real and imaginary (for the MEG only by imaginary) movements of the subjects and was compared with the background recording when the subject was at rest. This approach allows us to identify channels for each subject in which the frequency increases at the moment of motor activity and / or a stable pattern is formed that corresponds to this movement. The proposed method has a large number of applied applications in medicine and neurophysiology.

In the present paper we describe a modification of the method of time-frequency analysis of EEG data based on continuous wavelet transform. A distinctive feature of the proposed technique is that it allows simultaneous spatial-temporal analysis of all EEG recording channels in different frequency ranges.

Purpose. Optimal value of the embedding lag calculation is made. Lag is one of empirical parameters of mathematical models, used in autoregressive models for prediction, coupling analysis, signal classification etc. Methods. The first minimum in the dependence of the mutual information function on the time lag was detected. Results. The calculation showed that the optimal lag is about 8 sampling intervals (1/64 s or 1/8 of the characteristic oscillation period for the absence seizures). Discussion. The optimal lag is about 1/8 of the characteristic oscillation period was obtained for both epileptiform and background activity, including preictal and different stages of ictal activity, i. e. this time scale is present in the signal throughout the observation time.

In this paper we have developed a technique allowing automatic detection of the precursor of movement beginning based on the analysis of electromyographic signals. Methods for determining the beginning of movement and the moments of movement planning are of urgent need in neuroscience, and a separate problem is the use of muscle electrical activity signals (electromyograms) to accurately determine the beginning of hand movement due to the complexity, short duration and noise of the original signals. We have found out that in the case when the movement starts on a certain sound signal, the moment of the movement beginning is detected with a some time delay.

The early stages of gastric cancer are not recognized due to its asymptomatic development. Detection of patho- logical changes can be achieved using the photodynamic diagnostics, but this approach provides rather low tumor selectivity. Additional opportunities to improve the detection of precancerous stages may be provided by ana- lyzing blood circulation in the vessels of the stomach. We consider the possibilities of multiresolution analysis in recognizing changes in blood circulation in rats with different levels of the fluorescent signal in the stomach associated with precancerous stages.

In this study 300 skin lesion (including 32 skin melanomas) multispectral data cubes were analyzed. The multi-step and single step machine learning approaches were analyzed to find the wavebands that provide the most information that helps discriminate skin melanoma from other benign pigmented lesions. The multi-step machine learning approach assumed training several models but proved itself to be ineffective. The reason for that is a necessity to train a segmentation model on a very small dataset and utilization of standard machine learning classifier which have shown poor classification performance. The single-step approach is based on a deep learning neural network. We have conducted 2600 experiments on two neural network architectures: popular pre-trained image analysis “InceptionV3” and simple custom convolutional neural network (ConvNet) classifiers. Observing performance metrics of these two deep-learning (DL) based architectures allowed to determine combinations of three spectral wavebands allowing to train a classifier with the best classification results. It was found that a simple ConvNet classifier allowed us to get better classification results. ConvNet training results have shown that most informative wavebands are 450nm which is the most informative for melanin concentration on the skin surface, 590nm that represents integral information about melanin and hemoglobin distribution from epidermis-dermis layer, and 950 nm that provide information from deeper skin layers. As introduced the convolutional neural network (CNN) model was simple but has not shown great performance. Also, we have to explore alternative CNN architectures. AutoKeras framework was used to find an architecture of the image classifier using the found waveband triplets.

Phase interactions between cardiovascular and respiratory systems were analyzed at rest in volunteers. 22 healthy normotensive non-smoking subjects aged from 21 to 45 years participated in the study. The following physiological signals were recorded simultaneously: respiratory rate, heart rate variability (HRV), forearm and foot skin blood flow, tissue blood volume from hand and foot finger pads. The degree of synchronization between phases of analyzed signals was estimated with the value of wavelet phase coherence function. It was found high phase synchronization between respiration and tissue blood volume oscillations of both fingers and low synchronization between respiration and skin blood flow oscillations of both skin sites under study. It was also obtained high phase synchronization between HRV and tissue blood volume oscillations of both fingers as well as low synchronization between HRV and skin blood flow oscillations of both skin sites at the respiration frequency (~ 0.3 Hz). There are similarities of phase interactions of both analyzed signals (blood flow and blood volume) with HRV in the low frequency range from 0.0095 to 0.1 Hz. These results were independent of extremities under study. We assume the results obtained can be used for development of new diagnostic approaches for assessment of state of peripheral vessels in pathologies.

In this work, we analyzed the signals of heart rate variability and leg’s photoplethysmograms of newborns. A series o f experiments included 10 conditionally healthy subjects; each recording was carried out for 15 minutes during feeding. We used methods of spectral analysis and methods of nonlinear dynamics. This work shows some features of the autonomic nervous regulation of the cardiovascular system in newborns, an assessment of the degree of synchronization and the coupling of the loops for regulating heart rate variability and vascular tone using methods for calculating crossspectrum, the coherence coefficient and the total percentage of phase synchronization.

We present a two-dimensional model of astrocytic Ca²⁺ dynamics. We take into account two main factors of the considered process: the IP_3-mediated release from intracellular Ca²⁺ stores and the plasma membrane bidirectional exchange, i.e. the Na⁺/Ca²⁺-exchanger (NCX). We based on the Hodgkin - Huxley formalism to describe NCX effects and takes into account the glutamate transporter-mediated increase in Na⁺ during synaptic activity and Na⁺- and Ca²⁺-dependent regulation. The results of the unified model numerical solution confirm the emergence of calcium waves, which occur due to the synaptic activity and spread over the astrocyte network. The presence of NCX leads to a decrease in the average areas that are affected by a global calcium wave during excitation. However, the Na/Ca exchanger stimulates calcium waves, making possible the formation of more long-lived waves.

We simulate the formation and dynamics of calcium waves in a distributed astrocyte network. We take into account volume effects induced by the real morphology of an astrocyte cell. Astrocytes are sponge-like structures which do not occupy the entire volume of its compartment: astrocyte leaflets are surrounded by the neuropil. We introduce the astrocyte volume fraction (AVF) parameter which shows the part of the 2D template occupied by an astrocyte versus neuropil and correlates with this ratio in a real 3D structure. To describe the difference in the processes in branchlets and leaflets of an astrocyte and the continuous transition between them we use the surface-to-volume ratio (SVR) parameter.

The neurovascular unit of the brain parenchyma is called the cellular circuit, which provides control of local blood flow depending on the needs of neurons. When the activity of a neuron changes, a chain of signaling mechanisms is activated, which leads to an increase or decrease in the radius of a nearby blood vessel. A number of mathematical models of neurovascular coupling have been developed that reproduce the main signaling pathways and allow estimating the quantitative side of the processes. However, these models are too computationally heavy to be the basis for 2D and even more so 3D modeling of parenchyma nervous tissue. In this paper we propose a functional model of neurovascular unit aimed at correct reproduction of dynamic patterns of neurovascular response with minimum number of equations and control parameters. Such model includes the most significant elements of the cellular scheme: volume transmission coupling in the intercellular space of the parenchyma, intracellular dynamics of calcium concentration in astrocyte, as well as biphasic nature of the reaction of a cell of smooth muscle of a vessel to the growth of potassium concentration in perivascular space. The results of the computational experiment show good agreement between the model dynamics and the known types of neurovascular response.

The concept of neurovascular unit (NVU) is used to denote cells and their communication mechanisms involved in the formation of autoregulation of blood supply. It has been shown that the "opening" of blood-brain barrier (BBB) can be caused by different types of effects, such as a strong and prolonged sound, or by laser irradiation. It has been noted that the BBB opening is accompanied by perivascular edema, as capillary permeability to water also increases many times. Thus, one can expect that such changes can significantly affect the operational mode of neurovascular unit. We present the model study aimed to estimate the impact of perivascular edema on the functioning of neurovascular coupling using a multidimensional quantitative mathematical model. Our results predict the blocking of neurovascular communication operation when BBB is opened. Namely, we show that a relatively small (2-3 times) change in perivascular volume has a subtle effect, a 10-fold increase in PVS changes markedly, but does not violate the functionality of neurovascular coupling, and a significant degree of edema (increase in PVS more than 100 times) almost completely turns off the neurovascular coupling.

We consider the synchronization between the dynamics of blood flow in cerebral and peripheral vessels in rats from the point of view of locking the instantaneous frequencies associated with slow oscillations caused by the neurogenic regulatory mechanism or, in part, metabolic activity. We show that the degree of synchronization varies with the development of a stroke caused by severe stress, which is reflected in the reduced average duration of segments with frequency locking. Such changes differ between macro- and microcerebral dynamics and are more pronounced for a network of small vessels surrounding the sagittal sinus.

A mathematical hydrodynamic model of human cardiovascular bed has been developed. The model includes a 4- chamber heart, two circles of blood circulation and a multilevel microvasculature. Using model developed we studied the influence of low-intensity noise on blood flow oscillations in microvascular bed. In the study low-intensity noise effects on heart wall tone of left ventricle. Unmodulated noise and noise modulated by a sine with frequencies of 0.02, 0.0625 and 0.1 Hz were used. Unmodulated noise induced the forming of low-frequency oscillations of microvascular blood flow with a peak at the frequency of 0.1 Hz. Modulated noise induced low-frequency oscillations of blood flow with pronounced peaks at the modulation frequencies. The obtained results indicate a detecting property of simulated vascular bed that allows one to define modulating signal. This behavior is a characteristic of the system consisting of nonlinear and filtering components.

Study aims to adopt the cross-recurrence analysis for detection of coupling between the loops of sympathetic regulation of cardiovascular system. To test the applicability of the method and to set its parameters it was applied to the mathematical model of cardiovascular system that has a structure similar to the structure of the real system. To investigate whether the cross-recurrence analysis reflects the dynamics of autonomic control the authors conducted four numerical experiments with gradually decreasing activity of sympathetic regulation. No correlation was found between the results of cross-recurrence analysis and the coupling strengths.

The mechanisms of contractility and spatiotemporal patterns of activity of the lymphatic vessels are studied much less than for blood vessels. However, an understanding of the characteristics of their dynamics is equally important for biomedical applications. In this work, we propose a simple model of contractile activity of the elementary segment of the lymphatic vessel, which we use to assemble a model of lymphangion, a functional unit of the lymphatic system that provides lymph pumping. Our results show that the approach is promising, especially with further parametrization of the model.

We investigate the dynamics of the networks of 100 identical bistable Hodgkin-Huxley neurons with scale-free, small-world and random topologies. For all of them, we discover a phenomenon when one part of the neurons are in the resting state, while the other one is in the oscillatory regime in a certain area of coupling strength and external current amplitude. We investigate this phenomenon and explain it by neuron interaction similar to the short pulse of external current which is able to switch the neuron regime from resting to oscillatory one and vice versa. We find the differences on this phenomenon for different topologies and investigate the evolution of it with increasing of external current.

We investigate the dynamics of individual Hodgkin-Huxley neuron in a multistable area where both stable fixed point and stable limit cycle coexist. We demonstrate a possibility of controlling neuron dynamics by a short pulse of the constant external current. Depending on the pulse time, duration and amplitude it can switch the neuron state from resting to oscillatory one and vice versa. We investigate the possibility of controlling the dynamics of a network of 100 bistable Hodgkin-Huxley neurons by a short external current pulse. We show that for certain values of the pulse parameters, such as amplitude, time length, and applying time, the pulse can force some neurons to change their dynamics.

We propose a new model-free method based on feed-forward artificial neuronal network for detecting functional connectivity in coupled systems. The developed method which does not require large computational costs and which is able to work with short data trials can be used for analysis and restoration of connectivity in experimental multichannel data of different nature. We test this approach on the chaotic Rössler system and demonstrate good agreement with the previous well-know results.

Crowdion is a variation of an interstitial defect located in closely packed atomic rows, can play an important role in relaxation processes occurring in crystals in nonequilibrium conditions effectively transferring mass and energy. Recently dynamics of crowdions has been extensively studied for different types of lattices and dimensions. However, the point of energy exchange between crowdions has not been considered earlier. The paper presents an analysis of energy exchange in a complex of crowdions located in neighboring closely packed atomic row. Obtained results reveal that closely located crowdions can intensively transfer energy from one to another thus affecting the dynamics and scenario of defect structure evolution in the crystal.

Crowdions being interstitial atoms located in close-packed atomic rows, play an important role in relaxation processes that occur in metals and alloys under severe external influences, effectively transferring mass and energy. Recently the concept of a supersonic crowdion was expanded to an N-crowdion consisting in one dimensional motion of N extra atoms along a closely packed atomic row. In this study, the molecular dynamics method was used to study the motion of 1- and 2-crowdions in the fcc Pt lattice. The N-crowdion was excited by applying similar velocity to N neighboring atoms along a closely packed row. It was established that independently of the initial conditions, the crowdion exhibits quasiperiodic breather dynamics, while the average path length is practically independent of the initial velocity and configuration.

Discrete kinks in Klein-Gordon equations typically have two equilibrium configurations, an unstable one with maximum potential energy and a stable one with minimal energy. The difference between the kink energies in these two configurations gives the height of the Peierls-Nabarro potential. The maximal gradient of this potential gives the minimum force needed to set the kink in motion. It has been shown that some exceptional, non-integrable discretizations of the Klein-Gordon equation have zero static Peierls-Nabarro potential. An arbitrarily small external force in such models results in kink acceleration. Here several methods that give discrete Klein-Gordon models with zero static Peierls-Nabarro potential will be reviewed. Conservation laws which are satisfied for these discrete equations will be mentioned.

Carbon nanotubes (CNTs) have very high mechanical properties and that is why they are used for making super strong and light yarns, ropes, fillers for composites, solid lubricants, etc. Mechanical properties of CNT bundles have been addressed in a number of experimental and theoretical studies. Development of efficient computational methods for solving this problem is an important step in design of new CNT based materials. In the present study, an atomistic chain model is used to analyse mechanical response of CNT crystal under plane strain conditions. The model takes into account tension and bending of CNT wall and the van der Waals interactions. Discrete character of the model allows description of large curvature of CNT wall and CNT fracture at very high pressure. Equilibrium structures of CNT crystal under biaxial, strain controlled loading are obtained and the potential energy of the structure is decomposed into the energy of valence bonds, valence angles and van der Waals interactions. It is shown that the main contribution to the potential energy comes from the energy of valence angles related to bending of CNT walls. The reported simulation results are in a good agreement with the existing literature. The chain model offered here can be efficiently applied to the analysis of mechanical properties of single-walled or multi-walled CNT bundles under plane strain conditions or, under straightforward modifications, to similar structures made of other 2D nanomaterials.

We discuss how random switching between two states in the dynamics of complex systems affects characterization of oscillations based on data analysis. The appearance of unwanted short segments with distinct correlation features can significantly change the properties of time series estimated by standard numerical methods and, therefore, complicate the diagnosis of the system dynamics. The effects of random switching are much stronger for systems with anti-correlated dynamics compared with the case of power-law correlations.