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检索条件"机构=Center for technologies in Robotics and Mechatronics components"

共 242 条记录，以下是61-70 订阅

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Artificial neural network predicts inter-areal functional connectivity

Artificial neural network predicts inter-areal functional co...

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Saratov Fall Meeting 2020 - Computations and Data Analysis: From Molecular Processes to Brain Functions

作者： Pitsik, Elena Frolov, Nikita Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Saratov State Medical University Saratov Russia

ISBN: (数字)9781510645332

ISBN: (纸本)9781510645325

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. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Multilayer neural networks

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Walking Robot Control with a Machine Learning-based Ground Reaction Force Predictor and Generated Linear Contact Model∗

Walking Robot Control with a Machine Learning-based Ground R...

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2021 International Conference "Nonlinearity, Information and robotics", NIR 2021

作者： Savin, Sergei Golousov, Svyatoslav Zalyaev, Eduard Salikhzyanov, Alek Klimchik, Alexandr Innopolis University Center for Technologies in Robotics and Mechatronics Components Innopolis420500 Russia Technology Arrival Ltd Banbury United Kingdom Edge Vision Ltd. Innopolis420500 Russia

ISBN: (纸本)9781665424066

This paper aims to present a comprehensive view on a new control method for legged robot: data-driven ground reaction predictor-based control. The idea of the method is to use machine learning tools to build a reliable predictor for ground reaction forces and then exclude them from the control formulation by building local contact interaction models. The advantage of this approach is twofold: first, it allows to avoid making models for every contact scenario, which become less accurate as the robot changes during its life cycle;instead it relies on the data gathered by the robot's sensors. Second, it allows the use of the wealth of the control methods designed for the serial robots, as is demonstrated in the paper. The paper shows three experiments: with a planar walking robot, with a simplified three dimensional model tailored for optimization-based control, and with AR-601 humanoid robot. © 2021 IEEE.

关键词： Anthropomorphic robots

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Analysis of information perception and processing during long-term and intense cognitive load using combined EEG and NIRS

Analysis of information perception and processing during lon...

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2020 International Conference Nonlinearity, Information and robotics, NIR 2020

作者： Grubov, Vadim Badarin, Artem Maksimenko, Vladimir Center for Technologies in Robotics and Mechatronics Components Innopolis University Neuroscience and Cognitive Technology Laboratory Russia

ISBN: (纸本)9781728187631

This work is devoted to the analysis of information perception and processing during long-term and intense cognitive load using combined EEG + NIRS. We consider changes in the reaction time during long-term and intense cognitive load and a comparison of brain activity with behavioral characteristics. We found the establishment of a characteristic mode in the brain corresponding to a routine cognitive task. © 2020 IEEE.

关键词： Brain

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Features of real and imaginary human motor activity with EEG and fNIRS

Features of real and imaginary human motor activity with EEG...

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2020 International Conference Nonlinearity, Information and robotics, NIR 2020

作者： Grubov, Vadim Badarin, Artem Frolov, Nikita Pitsik, Elena Center for Technologies in Robotics and Mechatronics Components Innopolis University Neuroscience and Cognitive Technology Lab Russia

ISBN: (纸本)9781728187631

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. © 2020 IEEE.

关键词： Electroencephalography

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On Choosing Structure for a Machine Learning-based Reaction Force Predictor for Walking Robots

On Choosing Structure for a Machine Learning-based Reaction ...

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2020 International Conference on robotics and Artificial Intelligence, RoAI 2020

作者： Zalyaev, E. Savin, S. Salikhzyanov, A. Golousov, S. Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis420500 Russia Edge Vision Ltd. Innopolis420500 Russia Technology Arrival Ltd Banbury London United Kingdom

This paper focuses on the topic of contact reaction prediction for walking robots, namely on the analysis of performances on different structures of the machine-learning-based predictors. Predicting reaction forces is important due to the fact that it can allow us to retrieve a simplified model of the contact scenario, as was proposed in the literature preciously. This allows to turn the problem of contact model identification into a data collection and processing problem. In order to do it effectively, both a data compression (feature extraction) and regression strategies might be used. This research provides an analysis of both with respect to the discussed problem. © Published under licence by IOP Publishing Ltd.

关键词： Machine learning

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Factor of border crossing limitation with an infection hub in the SIR covid model 5

Factor of border crossing limitation with an infection hub i...

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5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Khorev, Vladimir Kazantsev, Victor B. Center for technologies in robotics and mechatronics components Innopolis University Innopolis Russia Neurotechnology deparment Lobachevsky University Nizhniy Novgorod Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665442824

In this work we develop a new nonlinear net-based SIR epidemic problem modeling the spreading of coronavirus under the effect of a border crossing limits by the government measures to stop coronavirus spreading. We show that the emergence and disappearance of subsequent waves of infection on the degree of relationship with a hub is non-linear, and with a large degree of relationships, the number of waves can decrease. © 2021 IEEE

关键词： Coronavirus

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Topological classification of population activity in spiking neural network

Topological classification of population activity in spiking...

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Saratov Fall Meeting 2020 - Computations and Data Analysis: From Molecular Processes to Brain Functions

作者： Lobov, Sergey A. Zharinov, Alexey I. Semenova, Olga Kazantsev, Victor B. Lobachevsky State University Nizhny Novgorod Russia Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Laboratory of Neuromodeling Samara Russia

ISBN: (数字)9781510645332

ISBN: (纸本)9781510645325

The role of propagating waves of neuronal activity in different brain areas, particularly in cerebral cortex, is still debated in modern neuroscience. We consider spiking neural network (SNN) model with spike timing dependent plasticity (STDP). It is shown that in the SNN the topological type of neural activity depends on the average length of connections between neurons - the connectivity radius. At the small radius (mostly local connections), the activity spreads in the form of traveling waves with well shaped leading and trailing edge. Such simple wave activity can be treated as a network spike. With increase in the connectivity radius, blurring of the leading and trailing edges is observed up to the "instant"activation of the network. In this case, the sparse neurons first generate spikes, and then all neurons in the network generate multiple spikes. This type of activity can be considered as a network burst. We also analyze the velocity of neural activity propagation depending on the connectivity radius. In the case of the network burst mode, the velocity is predominantly limited by axonal delays. In turn, in the case of the network spike mode it is defined by synaptic delays. In addition, we found that the STDP facilitates network synchronization in SNNs with a predominance of local connections. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Neurons

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Efficiency and fatigue in marksmanship training 5

Efficiency and fatigue in marksmanship training

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5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Grubov, Vadim Badarin, Artem Savosenkov, Andrey Grigoriev, Nikita Lukojanov, Maxim Ydoratina, Anna Maximenko, Vladimir Kazantsev, Viktor B. Hramov, Alexander E. Neuroscience and Cognitive Technology Lab Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Brain-Machine Interface Development Lab Institute for Neurosciences Lobachevskiy University Nijnij Novgorod Russia

In the present study we developed experimental paradigm and setup for marksmanship training in untrained subjects. We aimed to demonstrate correlation between subject’s fatigue and efficiency during training. © ... 详细信息

ISBN: (纸本)9781665442824

关键词： Efficiency

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Classification of hand-related real and imaginary motor activity with fNIRS 4

Classification of hand-related real and imaginary motor acti...

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4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2020

作者： Hramov, Alexander Grubov, Vadim Badarin, Artem Center for Technologies in Robotics and Mechatronics Components Innopolis University Neuroscience and Cognitive Technology Lab Innopolis Russia

ISBN: (纸本)9781728172866

In the present work we studied blood oxygenation/deoxygenation spatial dynamics related to real and imaginary motor activity using functional near-infrared spectroscopy (fNIRS). We revealed biomarkers based on pronounced hemispheric lateralization of hemodynamical response in the motor cortex during motor activity. We used these markers to design a sensing method for classification of movement's type. The accuracy of real movements classification was close to 100%, while for imaginary movements it was lower but still quite high (about 90%). The proposed system can find application, for example, in neurorehabilitation after severe brain injuries, including traumas and strokes. © 2020 IEEE.

关键词： Infrared devices

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Detecting inter-areal functional connectivity using artificial neural network 4

Detecting inter-areal functional connectivity using artifici...

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4th Scientific School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2020

作者： Pitsik, Elena Frolov, Nikita Innopolis University Neuroscience and Cognitive Technology Lab Center for Technologies in Robotics and Mechatronics Components Innopolis Russia

ISBN: (数字)9781728172866

ISBN: (纸本)9781728172866

In present paper we introduce an extended machine-learning-based approach to detect inter-areal functional connectivity based on artificial neural network (ANN). We prove the efficiency of the proposed method by applying multi-layer perceptron to find functional relations between the cortex and thalamus of the pathological WAG/Rij rats during the epileptic seizure based on the multivariate electrocorticography (ECoG) recordings. We show that the proposed algorithm is able to reconstruct the increased coupling within a thalamo-cortical network during the seizure versus a baseline activity. © 2020 IEEE.

关键词： Multilayer neural networks

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