Recurrence quantification analysis was applied to detect the P300 potential on single-trial EEG. We demonstrated that the emergence of P300 as a result of a response to a stimulus is associated with the brain activity complexity increase. The measures of recurrence quantification analysis have sufficient sensitivity to detect these changes even on the single time series.
Healthy aging affects structural and neurochemical properties of the human brain neural network. It also changes the brain functioning via the transformation of neural interactions both within and between functionally distinct brain areas. The age-related degradation of the brain functioning is evident on the behavioral level in terms of the decline in reaction time, low ability to execute and control complex motor actions, weak flexibility in learning new skills. In this paper we apply functional connectivity analysis to reveal the age-related changes in the integrative brain dynamic during the motor initiation before the dominant hand movements accompanied. Analyzing the whole-scalp electroencephalography (EEG) signals on the sensor level, we find higher theta-band coupling in the ipsilateral hemisphere.
In the present paper, we introduce an extended machine-learning-based approach to detect inter-areal functional connectivity based on an artificial neural network (ANN). Using the concept of generalized synchronization, we show that the proposed approach is relevant to infer functional dependencies between remote brain areas of interest from multivariate EEG recordings. We verify the ANN-based method to capture the reconfiguration of functional connectivity during motor execution. The proposed model showed good ability to approximate functional relations between the electrical activity of parietal and frontal areas and motor cortex at different stages of motor execution, providing an adequate pattern of functional connectivity network.
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 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 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 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.
KEYWORDS: Electromyography, Signal detection, Motion analysis, Signal analyzers, Electronic filtering, Signal processing, Electroencephalography, Brain, Linear filtering, Neuroscience
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.
Here, we introduce the method based on artificial neural networks (ANNs) for recognition and classification of patterns in electroencephalograms (EEGs) associated with imaginary and real movements of untrained volunteers. In order to get the fastest and the most accurate classification performance of multichannel motor imagery EEG-patterns, we propose our approach to selection of appropriate type, topology, learning algorithm and other parameters of neural network. We considered linear neural network, multilayer perceptron, radial basis function network (RBFN) and support vector machine. We revealed that appropriate quality of recognition can be obtained by using particular groups of electrodes according to extended international 10−10 system. Besides, pre-processing of EEGs by low-pass filter can significantly increase the classification performance. We developed mathematical model based on ANN for classification of EEG patterns corresponding to imaginary or real movements, which demonstrated high efficiency for untrained subjects. Achieved recognition accuracy of movements was up to 90−95% for group of subjects. RBFN demonstrated more accurate classification performance in both cases. Pre-filtering of input data using low-pass filter significantly increases recognition accuracy on 10−20% in average, and the low-pass filter with cutoff frequency 4 Hz shows the best results. It was revealed that using different sets of electrodes placed on different brain areas and consisted of 6-12 channels, one can achieve close to maximal classification accuracy. It is convenient to use electrodes on frontal and temporal lobes for real movements, and several sets containing 6-9 electrodes — in case with imagery movements.
In this paper we propose a model of the spatially distributed network based on the spatially correlated preferential attachments. Nodes in the spatially distributed networks of the real word, such as various urban or biological networks, aren't establishing randomly: the probability of emergence of new nodes is higher in the area of already existing ones. In this work we unite two principles of the real network modeling: the correlated percolation model and preferential attachment. To regulate spatial limitations of the network, we use density gradient, which determines the decrease of the probability of the connection emergence between two nodes with increase of the distance between them. We also consider the consistency of our results in the context of the real-world system modeling.
The paper considers the phenomena of competition in multiplex network whose structure evolves corresponding to dynamics of it’s elements, forming closed loop of self-learning with the aim to reach the optimal topology. Numerical analysis of proposed model shows that it is possible to obtain scale-invariant structures for corresponding parameters as well as the structures with homogeneous distribution of connections in the layers. Revealed phenomena emerges as the consequence of the self-organization processes related to structure-dynamical selflearning based on homeostasis and homophily, as well as the result of the competition between the network’s layers for optimal topology. It was shown that in the mode of partial and cluster synchronization the network reaches scale-free topology of complex nature that is different from layer to layer. However, in the mode of global synchronization the homogeneous topologies on all layer of the network are observed. This phenomenon is tightly connected with the competitive processes that represent themselves as the natural mechanism of reaching the optimal topology of the links in variety of real-world systems.
This paper considers the possibility of classification of electroencephalogram (EEG) and electromyogram (EMG) signals corresponding to different phases of sleep and wakefulness of mice by the means of artificial neural networks. A feed-forward artificial neural network based on multilayer perceptron was created and trained on the data of one of the rodents. The trained network was used to read and classify the EEG and EMG data corresponding to different phases of sleep and wakefulness of the same mouse and other mouse. The results show a good recognition quality of all phases for the rodent on which the training was conducted (80–99%) and acceptable recognition quality for the data collected from the same mouse after a stroke.
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