Macroscopic and microscopic spectral properties of brain networks during local and global synchronization

Maksimenko, Vladimir A.; Lüttjohann, Annika; Макаров, В. В.; Goremyko, Mikhail V.; Короновский, А. А.; Nedaivozov, Vladimir; Runnova, Anastasiya E.; Luijtelaar, Gilles van; Hramov, Alexander E.; Boccaletti, Stefano

doi:10.1103/physreve.96.012316

Cited by 67 publications

(19 citation statements)

References 53 publications

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“…In the previous papers [48,49], we have introduced the measure of connectivity based on the reconstruction of functional links between neuronal ensembles in different frequency bands by comparing spectral components of the EEG signals belonging to these bands. Here, we extend this approach to the time domain for analyzing the restoration of connectivity by similarity of hemodynamic responses in different areas of the motor cortex.…”

Section: Resultsmentioning

confidence: 99%

Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

Hramov

Grubov

Badarin

et al. 2020

Sensors

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Sensor-level human brain activity is studied during real and imaginary motor execution using functional near-infrared spectroscopy (fNIRS). Blood oxygenation and deoxygenation spatial dynamics exhibit pronounced hemispheric lateralization when performing motor tasks with the left and right hands. This fact allowed us to reveal biomarkers of hemodynamical response of the motor cortex on the motor execution, and use them for designing a sensing method for classification of the type of movement. The recognition accuracy of real movements is close to 100%, while the classification accuracy of imaginary movements is lower but quite high (at the level of 90%). The advantage of the proposed method is its ability to classify real and imaginary movements with sufficiently high efficiency without the need for recalculating parameters. The proposed system can serve as a sensor of motor activity to be used for neurorehabilitation after severe brain injuries, including traumas and strokes. different brain areas (e.g., event-related synchronization/desynchronization) on a sensor level provide important information about the current state of the nervous system and cognitive brain ability [7][8][9][10]. Particular brain states are associated with motor brain activity during either real or imaginary movement.Revealing specific features of spatial brain cortex activity related to real motions and motor imagery of different limbs can be essential not only for basic research in neuroscience, but also for applications in medicine to improve the quality of life of post-traumatic and post-stroke patients using brain-computer interfaces (BCI) for rehabilitation [11][12][13] or to control prostheses and exoskeletons [14]. One of the important BCI functions is online detection of specific features of electromagnetic brain activity using electroencephalography (EEG) [15] or magnetoencephalography (MEG) [16], and transformation of certain patterns into control commands to perform specific actions in the environment without the need of "classical" methods of human-machine interaction [17].Apart from EEG and MEG, other methods are also used to acquire information about brain states. In particular, functional near-infrared spectroscopy (fNIRS) [18,19] is a powerful tool of noninvasive optical imaging successfully used in BCI for registration of brain activity and control command formation [20][21][22]. Control commands for this kind of BCI should not be affected by any muscular activity [23]. Therefore, a study of brain states related to motor imagery is very important for designing such BCI [16,24]. Motor imagery is a mental process by which a person rehearses or simulates a given action with no real motor activity. Some researchers treat motor imagery as a conscious application of unconscious preparation for real motor activity [25]. A number of studies have highlighted common features for real and imaginary motor activity [26][27][28]. One of the common features, important for the BCI development, is that the cortical layout in the primary ...

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Section: Resultsmentioning

confidence: 99%

Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

Hramov

Grubov

Badarin

et al. 2020

Sensors

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“…DOI: 10.21883/PJTF.2018. 10.46105.17219 Изучение нейронных сетей головного мозга представляет большой интерес для исследователей, что подтверждается ростом числа научных публикаций в данной области науки в течение последних лет [1][2][3][4][5]. Значительную часть занимают междисциплинарные публикации, описы-вающие результаты, полученные на стыке нейронауки и других (физико-математических, психологических, инженерных) наук и т. д.…”

Section: поступило в редакцию 18 января 2018 гunclassified

Потоковая Обработка Многоканальных Сигналов Электроэнцефалограммы С Использованием Технологии Параллельных Вычислений На Графических Процессорах NVIDIA CUDA

Grubov¹,

Nedaivozov²

2018

Письма В Журнал Технической Физики

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Prospects of using parallel computing technology (PaCT) methods for the stream processing and online analysis of multichannel EEG data are considered. It is shown that the application of PaCT to calculation and evaluation of spectral characteristics of EEG signals makes online determination of changes in the energy of the main rhythms of neural activity in various parts of the cerebral cortex possible. The possibility of implementing the PaCT algorithm with CUDA C library and its use in a modern brain–computer interface (BCI) for cognitive-activity monitoring in the course of visual perception.

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“…Furthermore, competition between layers is ubiquitous in real world networks, e.g. in urban mobile networks, and in neuroscience when describing the processes occurring both in local neuron networks and during the interaction between different parts of the brain [19][20][21], in particular interactions between dynamical timescales in the neural network [22]. In this paper we study how the competition between network layers can influence the structural and dynamical features of multiplex networks.…”

Section: Introductionmentioning

confidence: 99%

Inter-layer competition in adaptive multiplex network

et al. 2018

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We consider competition between layers in adaptive multiplex networks of phase oscillators, where adaptation principles (which cause intra-layer topology evolution) are inspired by real world homophily and homeostasis phenomena. Our model yields the emergence of both scale-free topologies and meso-scale structures in the layers, for an appropriate choice of the control parameters. We further report that the growth of the number of interacting layers leads to a decrease of the global order, due to inter-layer structural competition. However, the increase of the system's scale can effect local synchronization between neighboring (or strongly coupled) nodes. Such unforeseen phenomena is connected with the nature of the competitive mechanism, which implies the rivalry for optimal structure within the whole system, a situation occurring in a variety of natural systems.

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Macroscopic and microscopic spectral properties of brain networks during local and global synchronization

Cited by 67 publications

References 53 publications

Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

Потоковая Обработка Многоканальных Сигналов Электроэнцефалограммы С Использованием Технологии Параллельных Вычислений На Графических Процессорах NVIDIA CUDA

Inter-layer competition in adaptive multiplex network

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