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.
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.
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.
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 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 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.
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 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.
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 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 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 present work we studied features of the human brain states classification, corresponding to the real movements of hands and legs. For this purpose we used supervised learning algorithm based on feed-forward artificial neural networks (ANNs) with error back-propagation along with the support vector machine (SVM) method. We compared the quality of operator movements classification by means of EEG signals obtained experimentally in the absence of preliminary processing and after filtration in different ranges up to 25 Hz. It was shown that low-frequency filtering of multichannel EEG data significantly improved accuracy of operator movements classification.
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.
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 this paper we suggest the new approach of powerful sub-THz signal generation based on intense electron beams containing oscillating virtual cathode. Suggested compact microwave source complies with a number of biomedical applications such as imaging, preventive healthcare, etc. In this work we discuss the results of numerical simulation and optimization of the novel device called “nanovircator” that have been carried out. The results of the numerical study show the possibility of “nanovircator” operation at 0.1-0.4 THz frequency range.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.