In this work we present the development process of a wireless portable module. It is developed to record various characteristics during sport shooting, such as automatic detection of the moment of shot and barrel movement during aiming, taking into account the peculiarities of its use in neurophysiological research. We propose an approach allowing to synchronize devices in a wireless local network with high accuracy (synchronization accuracy was 2 ms), as well as a method of logging the recorded data at a sampling rate of up to 2 kHz on an onboard flash drive.
In this paper, we present an analysis of the dynamics of functional connectivity of the cerebral cortical network using near-infrared spectroscopy during human solutions to simple cognitive tasks. A task-based on the Sternberg paradigm was chosen to provide a cognitive load. We identified statistically significant changes in the communication forces obtained on the basis of the analysis of oxyhemoglobin signals during the experiment. We found that in the course of the experiment, there is a restructuring of the functional network, which is accompanied by a decrease in the average connectivity strength between the cortical areas under study. We showed that there is a correlation between the subjective evaluation of fatigue degree and the characteristics of the identified functional neural network.
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.
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 main goal of this project was to identify the patterns of muscular activity of a person in the process of his interaction with the environment, as well as to identify mechanisms that make it possible to adapt behavior in response to changing external conditions. For this, we conducted a series of experiments with subjects placed in an unstable state. We carried out statistical analysis for the received signals of muscle activity. Based on the results of the analysis of behavioral characteristics, we revealed positive dynamics when subjects were reaching a state of balance and a pattern associated with training.
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.
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 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.
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 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.
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.
In this paper we applied analysis of multivariate time series for detection of changes in functional relations in brain during observation of educational material. Applied method is based on definition of mutual interdependence of processes and is known as Recurrent Measure of Dependence. In the paper we analyzed multichannel EEG signals obtained during experiments with observation of educational material by human subjects. We applied the method to EEG signals and showed qualitative changes in brain dynamics during educational process in comparison to dynamics of background activity.
In this paper we study speciifc oscillatory patterns of proepileptic activity on EEG signals of WAG/Rij rats. These patterns occur during the development of absence-epilepsy before fully-formed epileptic seizures. In the paper we analyze EEG signals of WAG/Rij rats with continuous wavelet transform to find particular features of proepileptic patterns in time-frequency domain. Then we develop new method for automated detection of proepileptic activity on EEG signals. We analyze results of method's performance and its efficiency.
In this paper we analyzed possibility for detection of EEG oscillatory patterns related to states of low and high levels of human concentration during perception of visual stimuli with help of artificial neural network. We analyzed different variation of EEG signals combination in order to find optimal one. We performed classification of brain states with perceptron-type artificial neural network and analyzed quality of classification.
In this paper we study specific oscillatory patterns on EEG signals of WAG/Rij rats. These patterns are known as proepileptic because they occur in time period during the development of absence-epilepsy before fully-formed epileptic seizures. In the paper we analyze EEG signals of WAG/Rij rats with continuous wavelet transform and empirical mode decomposition in order to find particular features of epileptic spike-wave discharges and nonepileptic sleep spindles. Then we introduce proepileptic activity as patterns that combine features of epileptic and non-epileptic activity. We analyze proepileptic activity in order to specify its features and time-frequency structure.
In this paper, based on the apparatus of artificial neural networks, a technique for recognizing and classifying patterns corresponding to imaginary movements on electroencephalograms (EEGs) obtained from a group of untrained subjects was developed. The works on the selection of the optimal type, topology, training algorithms and neural network parameters were carried out from the point of view of the most accurate and fast recognition and classification of patterns on multi-channel EEGs associated with the imagination of movements. The influence of the number and choice of the analyzed channels of a multichannel EEG on the quality of recognition of imaginary movements was also studied, and optimal configurations of electrode arrangements were obtained. The effect of pre-processing of EEG signals is analyzed from the point of view of improving the accuracy of recognition of imaginary movements.
In this paper, we investigate the problem of identification of patterns on magnetoencephalography signals of a brain associated with human movements. The design of registration of experimental data during magnetoencephalography (MEG) is developed and described. Consecutive imaginary movements of the hands and legs of the person are chosen as the basic movements. We solve the problem of recognition and classification of patterns using artificial neural networks. For a multilayer perceptron, good results of recognition of patterns of brain activity associated with different types of motion have been obtained.
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.
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.
In the paper we propose the new method for removing noise and physiological artifacts in human EEG recordings based on empirical mode decomposition (Hilbert-Huang transform). As physiological artifacts we consider specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the proposed method with steps including empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing these empirical modes and reconstructing of initial EEG signal. We show the efficiency of the method on the example of filtration of human EEG signal from eye-moving artifacts.
In the paper we propose the novel method for dealing with the physiological artifacts caused by intensive activity of facial and neck muscles and other movements in experimental human EEG recordings. The method is based on analysis of EEG signals with empirical mode decomposition (Hilbert-Huang transform). We introduce the mathematical algorithm of the method with following steps: empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing empirical modes with artifacts, reconstruction of the initial EEG signal. We test the method on filtration of experimental human EEG signals from movement artifacts and show high efficiency of the method.
In the paper we study the appearance of the complex patterns in human EEG data during a psychophysiological experiment by stimulating cognitive activity with the perception of ambiguous object. A new method based on the calculation of the maximum energy component for the continuous wavelet transform (skeletons) is proposed. Skeleton analysis allows us to identify specific patterns in the EEG data set, appearing in the perception of ambiguous objects. Thus, it becomes possible to diagnose some cognitive processes associated with the concentration of attention and recognition of complex visual objects. The article presents the processing results of experimental data for 6 male volunteers.
In the paper we propose the new method for dealing with noise and physiological artifacts in experimental human EEG recordings. The method is based on analysis of EEG signals with empirical mode decomposition (Hilbert-Huang transform). We consider noises and physiological artifacts on EEG as specific oscillatory patterns that cause problems during EEG analysis and can be detected with additional signals recorded simultaneously with EEG (ECG, EMG, EOG, etc.) We introduce the algorithm of the method with following steps: empirical mode decomposition of EEG signal, choosing of empirical modes with artifacts, removing empirical modes with artifacts, reconstruction of the initial EEG signal. We test the method on filtration of experimental human EEG signals from eye-moving artifacts and show high efficiency of the method.
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.
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.
Sleep spindles are known to appear spontaneously in the thalamocortical neuronal network of the brain during slow-wave sleep; pathological processes in the thalamocortical network may be the reason of the absence epilepsy. The aim of the present work is to study developed changes in the time-frequency structure of sleep spindles during the progressive development of the absence epilepsy in WAG/Rij rats. EEG recordings were made at age 7 and 9 months. Automatic recognition and subsequent analysis of sleep spindles on the EEG were performed using the continuous wavelet transform. The duration of epileptic discharges and the total duration of epileptic activity were found to increase with age, while the duration of sleep spindles, conversely, decreased. In terms of the mean frequency, sleep spindles could be divided into three classes: ‘slow’ (mean frequency 9.3Hz), ‘medium’ (11.4Hz), and ‘fast’ (13.5Hz). Slow and medium (transitional) spindles in five-month-old animals showed increased frequency from the beginning to the end of the spindle. The more intense the epilepsy is, the shorter are the durations of spindles of all types. The mean frequencies of ‘medium’ and ‘fast’ spindles were higher in rats with more intense signs of epilepsy. Overall, high epileptic activity in WAG/Rij rats was linked with significant changes in spindles of the transitional type, with less marked changes in the two traditionally identified types of spindle, slow and fast.
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.
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.
The problem of automatic recognition of specific oscillatory patterns on electroencephalograms (EEG) is addressed using the continuous wavelet-transform (CWT). A possibility of improving the quality of recognition by optimizing the choice of CWT parameters is discussed. An adaptive approach is proposed to identify sleep spindles (SS) and spike wave discharges (SWD) that assumes automatic selection of CWT-parameters reflecting the most informative features of the analyzed time-frequency structures. Advantages of the proposed technique over the standard wavelet-based approaches are considered.
Spike-wave discharges are electroencephalographic hallmarks of absence epilepsy. Spike-wave discharges are known to originate from thalamo-cortical neuronal network that normally produces sleep spindle oscillations. Although both sleep spindles and spike-wave discharges are considered as thalamo-cortical oscillations, functional relationship between them is still uncertain. The present study describes temporal dynamics of spike-wave discharges and sleep spindles as determined in long-time electroencephalograms (EEG) recorded in WAG/Rij rat model of absence epilepsy. We have proposed the wavelet-based method for the automatic detection of spike-wave discharges, sleep spindles (10–15Hz) and 5–9Hz oscillations in EEG. It was found that non-linear dynamics of spike-wave discharges and sleep spindles fits well to the law of ’on-off intermittency’. Intermittency in sleep spindles and spike-wave discharges implies that (1) temporal dynamics of these oscillations are deterministic in nature, and (2) it might be controlled by a system-level mechanism responsible for circadian modulation of neuronal network activity.
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