In this study, we analyzed the behavioral characteristics of the subjects (response time to visual stimuli and the correctness of interpretation), as well as brain activity at the sensory level when classifying repeatedly presented ambiguous images. We showed that the reaction time decreased for both LA and HA stimuli with the task completion time. In addition, the distribution of perception errors decreased for HA stimuli, but not for LA stimuli. At the sensory level, we found an increase in EEG power in the frequency range of 9-11 Hz with an increase in the task execution time.
In this study, we analyzed the effect of the ambiguity of the previous visual stimulus on the response time taken to process the current visual stimulus. Our experimental paradigm included the repeated presentation of ambiguous Necker cubes images with varying degrees of ambiguity. We studied the response time and the time-frequency features of EEG signals, reflecting the influence of the “sensory prehistory” on the current stimulus processing.
The design of visual decision-making task with uncertainty was proposed. Set of experiments was conducted in accordance with this design and obtained EEG dataset was analyzed. Analysis of EEG characteristics in time, frequency and space domains allowed to introduce certain features that can be used to separate right and wrong outcomes in the task prior actual subject's response.
We consider a network of networks consisting of small input neural network and four small-world subnetworks Hodgkin-Huxley neurons. Input network receives an external signal and transfers it to subnetworks via excitatory couplings while the subnets interact with each other via inhibitory couplings. We show that the subnets are divided into 2 clusters under the influence of an inhibitory couplings between them. The synchronization indexes of subnetworks periodically change in time. We found that SIs can oscillate either in-phase or anti-phase depending on the couplings between subnetworks.
In the present study we aimed to find specific characteristic based on brain activity, that can be used to evaluate attention and, thus, can be used in brain-computer interface. We introduced a characteristic based on prestimulus beta-rhythm activity and proposed an approach to collaborative BCI aimed to enhance human-to-human interaction while performing shared visual task. We also described general setup for such BCI and its possible application in long task of classifying ambiguous visual stimuli with varying degrees of ambiguity by a group of people.
The perception of visual information includes such stages as the initial processing of sensory input and the interpretation of the received information (decision-making). The uncertainty of visual stimuli affects the neural activity during both sensory-processing and the decision-making stages. Here we analyzed spatial and temporal properties of the neural activity in the β-frequency band during the processing of ambiguous bistable stimuli. We tested how the stimulus ambiguity influenced the perceptual decision-making process.
We consider two small-world networks of Hodgkin-Huxley neurons interacting via inhibitory coupling. We found that synchronization indices (SI) in both networks oscillate periodically in time, so that time intervals of high SI alternate with time intervals of low SI. Depending on the coupling strength, the two coupled networks can be in the regime of either in-phase or anti-phase synchronization. We suppose that the inherent mechanism behind such a behavior lies in the cognitive resource redistribution between neuronal ensembles of the brain.
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.
KEYWORDS: Electroencephalography, Wavelets, Control systems, Electrodes, Information visualization, Visualization, Astatine, Signal analyzers, Bandpass filters, Linear filtering
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 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.
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.
In this report we propose an approach based on artificial neural networks for the classification and recognition of various states of the human brain associated with the spatial perception of ambiguous images. Based on the developed numerical methodology and analysis of the experimental multi-channel EEG data, we create and optimize an artificial neural network to ensure the accuracy of the classification of EEG states of the brain in visual perception close to 100%. Different interpretations of ambiguous images produce different oscillatory patterns in the EEG of a person with similar characteristics for each interpretation.
We have proposed brain-computer interface (BCI) for the estimation of the brain response on the presented visual tasks. Proposed BCI is based on the EEG recorder Encephalan-EEGR-19/26 (Medicom MTD, Russia) supplemented by a special home-made developed acquisition software. BCI is tested during experimental session while subject is perceiving the bistable visual stimuli and classifying them according to the interpretation. We have subjected the participant to the different external conditions and observed the significant decrease in the response, associated with the perceiving the bistable visual stimuli, during the presence of distraction. Based on the obtained results we have proposed possibility to use of BCI for estimation of the human alertness during solving the tasks required substantial visual attention.
We have considered time-frequency and spatio-temporal structure of electrical brain activity, associated with real and imaginary movements based on the multichannel EEG recordings. We have found that along with wellknown effects of event-related desynchronization (ERD) in α/μ – rhythms and β – rhythm, these types of activity are accompanied by the either ERS (for real movement) or ERD (for imaginary movement) in low-frequency δ – band, located mostly in frontal lobe. This may be caused by the associated processes of decision making, which take place when subject is deciding either perform the movement or imagine it. Obtained features have been found in untrained subject which it its turn gives the possibility to use our results in the development of brain-computer interfaces for controlling anthropomorphic robotic arm.
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.
Authentic recognition of specific patterns of electroencephalograms (EEGs) associated with real and imagi- nary movements is an important stage for the development of brain-computer interfaces. In experiments with untrained participants, the ability to detect the motor-related brain activity based on the multichannel EEG processing is demonstrated. Using the detrended fluctuation analysis, changes in the EEG patterns during the imagination of hand movements are reported. It is discussed how the ability to recognize brain activity related to motor executions depends on the electrode position.
The main issue of epileptology is the elimination of epileptic events. This can be achieved by a system that predicts the emergence of seizures in conjunction with a system that interferes with the process that leads to the onset of seizure. The prediction of seizures remains, for the present, unresolved in the absence epilepsy, due to the sudden onset of seizures. We developed an algorithm for predicting seizures in real time, evaluated it and implemented it into an online closed-loop brain stimulation system designed to prevent typical for the absence of epilepsy of spike waves (SWD) in the genetic rat model. The algorithm correctly predicts more than 85% of the seizures and the rest were successfully detected. Unlike the old beliefs that SWDs are unpredictable, current results show that they can be predicted and that the development of systems for predicting and preventing closed-loop capture is a feasible step on the way to intervention to achieve control and freedom from epileptic seizures.
Using wavelet analysis of the signals of electrical brain activity (EEG), we study the processes of neural activity, associated with perception of visual stimuli. We demonstrate that the brain can process visual stimuli in two scenarios: (i) perception is characterized by destruction of the alpha-waves and increase in the high-frequency (beta) activity, (ii) the beta-rhythm is not well pronounced, while the alpha-wave energy remains unchanged. The special experiments show that the motivation factor initiates the first scenario, explained by the increasing alertness. Based on the obtained results we build the brain-computer interface and demonstrate how the degree of the alertness can be estimated and controlled in real experiment.
In the present research we studied the cognitive processes, associated with the perception of ambiguous images using the multichannel MEG recordings. Using the wavelet transformation, we considered the dynamics of the neural network of brain in different frequency bands, including high (up to 100 Hz) frequency gamma-waves. Along with the time-frequency analysis of single MEG traces, the interactions between remote brain regions, associated with the perception, were also taken into consideration. As the result, the new features of bistable visual perception were observed and the effect of image ambiguity was analyzed.
Problem of interaction between human and machine systems through the neuro-interfaces (or brain-computer interfaces) is an urgent task which requires analysis of large amount of neurophysiological EEG data. In present paper we consider the methods of parallel computing as one of the most powerful tools for processing experimental data in real-time with respect to multichannel structure of EEG. In this context we demonstrate the application of parallel computing for the estimation of the spectral properties of multichannel EEG signals, associated with the visual perception. Using CUDA C library we run wavelet-based algorithm on GPUs and show possibility for detection of specific patterns in multichannel set of EEG data in real-time.
The focal riddle for physicists and neuroscientists consists in disclosing the way microscopic scale neural interactions pilot the formation of the different activities revealed (at a macroscopic scale) by EEG and MEG equipments. In the current paper we estimate the degree of the interactions between the remote regions of the brain, based on the wavelet analysis of EEG signals, recorded from these brain areas. With the help of the proposed approach we analyze the neural interactions, associated with cognitive processes, taken place in human’s brain during the perception of visual stimuli. We show that neurons in the remote regions of brain interact with the different degree of intensity in the generation of different rhythms. In particular during the perception of visual stimuli strong interaction has been observed in β - frequency band while strong interaction in α - frequency band has been observed in resting state.
KEYWORDS: Oscillators, Numerical analysis, Multilayers, Analytical research, Neurons, Systems modeling, Chemical elements, Mathematical modeling, Chemical analysis, Data modeling
We numerically study the interaction between the ensembles of the Hindmarsh-Rose (HR) neuron systems, arranged in the multilayer network model. We have shown that the fully identical layers, demonstrated individually different chimera due to the initial mismatch, come to the identical chimera state with the increase of inter-layer coupling. Within the multilayer model we also consider the case, when the one layer demonstrates chimera state, while another layer exhibits coherent or incoherent dynamics. It has been shown that the interactions chimera-coherent state and chimera-incoherent state leads to the both excitation of chimera as from the ensemble of fully coherent or incoherent oscillators, and suppression of initially stable chimera state
In this paper we study the conditions of chimera states excitation in ensemble of non-locally coupled Kuramoto-Sakaguchi (KS) oscillators. In the framework of current research we analyze the dynamics of the homogeneous network containing identical oscillators. We show the chimera state formation process is sensitive to the parameters of coupling kernel and to the KS network initial state. To perform the analysis we have used the Ott-Antonsen (OA) ansatz to consider the behavior of infinitely large KS network.
In the paper we study the possibility to control the frequency of the sub-THz source, based on the semiconductor superlattice by means of optimal spatial distribution of the doping density. We propose the appropriate mathematical model, which allows to describe the collective transport of charge in miniband semiconductor, where the spatial profile of the equilibrium charge density is defined by function. As the example we consider the uniform spatial distribution of doping density, contained local inhomogeneity, caused by local increase of density and described approximately by Gaussian function. We show that such inhomogeneity being placed in different areas of the transport region can affect the dynamics of charge domain, which, in turn, leads to increase (or decrease) of the frequency of current oscillations.
The data transmission method using the highest harmonics of semiconductor superlattice-based microwave generator has been proposed for biomedical applications. Semiconductor superlattice operated in charge domain formation regime is characterized by the rich high-harmonics power spectrum. The numerical modeling of modulation and detection of the THz range signals using the highest harmonics of the fundamental frequency of the superlattice-based generator was carried out. We have shown effectiveness of the proposed method and discussed the possible applications.
We investigate effects of a linear resonator on spatial electron dynamics in semiconductor superlattice. We have shown that coupling the external resonant system to superlattice leads to occurrence of the additional area of negative differential conductance on the current-voltage characteristic, which does not occur in autonomous system. Furthermore, this region shows great increase of generation frequency, that contains practical interest.
The competition of homophily and homeostasis mechanisms taking place in the multilayer network where several layers of connection topologies are simultaneously present as well as the interaction between layers is considered. We have shown that the competition of homophily and homeostasis leads in such networks to the formation of synchronous patterns within the different layers of the network, which may be both the distinct and identical.
This paper is devoted to the analysis of topological changes in complex networks that are reflected in the macroscopic characteristics. We consider a model of the complex network with the adaptive links, in which the synchronous dynamics leads to the appearance of clusters of strongly coupled elements and show that structural changes significantly affect the macroscopic dynamics. As the result, we demonstrate a high possibility of cluster formation in the network that can be analyzed via the consideration of macroscopic characteristics. We also discuss a prospective application for the detection of structural features of neural networks.
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.
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.
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