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检索条件"机构=Center for Technologies in Robotics and Mechatronics Components Innopolis"

共 264 条记录，以下是41-50 订阅

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Recurrence plot analysis of functional brain connectivity during bistable visual perception 3

Recurrence plot analysis of functional brain connectivity du...

引用

3rd School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2019

作者： Miftahova, Amina R. Hramov, Alexander E. Innopolis University Innopolis420500 Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis420500 Russia

ISBN: (纸本)9781728124704

To develop fully-featured brain-computer interfaces it is crucial to study the behaviour of the brain in ambiguous situations. In this study, we analyzed the EEG data recorded during the perception of the bistable image - Necker's cube with the varying intensity of the lines. The image was demonstrated to the test person for one second and the person was asked to decide whether the cube is right-oriented or left-oriented and to push the corresponding data. During these actions the brain activity was recorded using 31-channel EEG, electrodes were placed according to the 10-20 system. The analysis, which was performed with the application of joint recurrence plots, suggests the existence of the synchronization between the channels of the frontal and occipital lobes. We believe that more rigorous research could reveal statistically significant brain connections and their dependence on physiological factors. Besides, this study may be significant for the development of the self-learning adaptive control systems. © 2019 IEEE.

关键词： Brain

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Dynamic Object Grasping in Human-Robot Cooperation Based on Mixed-Reality

Dynamic Object Grasping in Human-Robot Cooperation Based on ...

引用

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

作者： Demian, Albert Ostanin, Mikhail Klimchik, Alexandr Innopolis University Center for Technologies in Robotics and Mechatronics Components Innopolis Russia Innopolis University Robotics and Computer Vision Institute Innopolis Russia

ISBN: (纸本)9781665424066

Static Object grasping is a challenging task that has been studied for decades. The difficulty of the task comes back to the reason that a grasping attempt can have many solutions or due to the uncertainty about the targeted object's features and characteristics. This makes the fact about dynamic object grasping with un-modeled dynamics even more challenging. In this paper, an approach for dynamic object grasping is presented. The approach considers human-robot handover operation where the robot should be able to track human's holding-object hand and plan a successful grasp of the object in hand. The system was implemented with the help of Mixed-Reality using HoloLens glasses for human's hand tracking. A serial manipulator was used to execute the operation mounted with end-effector-mounted camera to perform computer vision operations for grasp planning and correction. The main task is robot at random configuration can be able to find hand-holding object and plan grasp on object in hand. The implemented system shows success and was able to perform most of the grasping tasks successfully. © 2021 IEEE.

关键词： Mixed reality

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Development of a digital software platform for the study of nonlinear dynamics of electronic systems 3

Development of a digital software platform for the study of ...

引用

3rd School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2019

作者： Badarin, Artem Neuroscience and Cognivite Technology Lab Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

The paper presents the results of applied research on the design and application of an integrated platform for solving applied problems of developing, studying and optimizing high-power electron-plasma devices from th... 详细信息

ISBN: (纸本)9781728124704

关键词： Simulation platform

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Approaches for the Improvement of Motor-Related Patterns Classification in EEG Signals 3

Approaches for the Improvement of Motor-Related Patterns Cla...

引用

3rd School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2019

作者： Kurkin, Semen Maksimenko, Vladimir Pitsik, Elena Neuroscience and Cognitive Techology Lab Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

ISBN: (纸本)9781728124704

We apply artificial neural network (ANN) for recognition and classification of electroencephalographic (EEG) patterns associated with motor imagery for untrained subjects. Classification accuracy is optimized by reducing complexity of input experimental data. For multichannel EEG recorded by the set of 31 electrodes, we select an appropriate type of ANN which reaches 80±10% accuracy for single trial classification. We analyze the time-frequency structure of EEG signals and find that motor-related features associated with left- and right-leg motor imagery, are more pronounced in the mu (8-13 Hz) and delta (1-5 Hz) brainwaves. Based on the obtained results, we propose the optimization approach by pre-processing the EEG signals with a low-pass filter with different cutoffs. We demonstrate that the filtration of high-frequency spectral components significantly enhances the classification performance: up to 90±5% using 8 electrodes only. © 2019 IEEE.

关键词： Neural networks

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Multilayer perceptron reveals functional connectivity structrure in thalamo-cortical brain network 3

Multilayer perceptron reveals functional connectivity struct...

引用

3rd School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2019

作者： Frolov, Nikita S. Hramov, Alexander E. Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

ISBN: (纸本)9781728124704

Artificial neural networks (ANNs) are known to be a powerful tool for big data analysis. They are widely used in computer science, nonlinear dynamics, robotics, and neuroscience for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience ANNs allow recognizing specific forms of brain activity from multichannel electro- (EEG) or magnetoencephalographic (MEG) data and, therefore, widely used as a computational core in various brain-computer interfaces. Another challenging problem is the analysis of connectivity structures in big multivariate data. In neuroscience restoring the functional brain network using multichannel EEG/MEG signals uncovers mechanisms of neuronal interaction during various physiological or cognitive processes. In this report we use recent advances in the area of machine learning known as feed-forward artificial neuronal network to formulate a method for detecting functional dependence in unidirectionally and bidirectionally coupled systems without additional information about them. We apply our method for the first time to reveal functional connectivity structure in the thalamo-cortical network of epileptic brain based on a rodent electrocorticography (ECoG) data set. © 2019 IEEE.

关键词： Brain

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Upper limb exoskeleton for neurorehabilitation with control via brain-computer interface 6

Upper limb exoskeleton for neurorehabilitation with control ...

引用

6th Scientific School "Dynamics of Complex Networks and their Applications", DCNA 2022

作者： Antipov, Vladimir Innopolis University Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis Russia

ISBN: (数字)9781665461740

ISBN: (纸本)9781665461740

This paper describes the results of developing control methods for an active exoskeleton for rehabilitation of human upper limb motor activity. The experimental setup, real-time software implementation of the control system, and also the developed software environment for stimulation of the subject during the neurophysiological experiment are described. The research object of the above system is the processes of activation of brain areas in terms of analysis of bioelectrical electroencephalographic signals registered during a motor activity, suitable for use in the tasks of motor rehabilitation. The purpose of this work is to develop a hardware-software part of the experiment to study real-time algorithms for control of the upper limb exoskeleton for rehabilitation and education tasks using 'brain-computer' interfaces. © 2022 IEEE.

关键词： Brain computer interface

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The control of the dynamics of intense electron beams coupled through a common field 3

The control of the dynamics of intense electron beams couple...

引用

3rd School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2019

作者： Badarin, Artem Neuroscience and Cognivite Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

ISBN: (纸本)9781728124704

This paper presents the results of a study of the nonlinear dynamics of a system with several electron beams. A review and analysis of the latest results obtained in this area. In this case, the main emphasis in this work is made on controlling the generation characteristics in such a highly nonlinear system. The main dynamic modes typical for this kind of systems are shown. © 2019 IEEE.

关键词： Electron beams

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Features of brain activity in children during cognitive tasks of different types 3

Features of brain activity in children during cognitive task...

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3rd School on Dynamics of Complex Networks and their Application in Intellectual robotics, DCNAIR 2019

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

ISBN: (纸本)9781728124704

EEG signals of children were recorded during cognitive load of various types in accordance with proposed design of experiment. Obtained EEG data were analyzed with methods of time-frequency analysis and statistics. Differences in time-frequency structure of EEG signals and ratio between frequency ranges was shown for background activity and two types of cognitive activity. © 2019 IEEE.

关键词： Electroencephalography

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Analysis of multilayer perceptron based approach in studying brain neuronal connectivity networks 4

Analysis of multilayer perceptron based approach in studying...

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

作者： Smirnov, Nikita M. Tishkin, Pavel S. Lutanin, Evgeny A. Neuroscience and Cognitive Technology Lab Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

ISBN: (纸本)9781728172866

In this paper, we present an analysis of modern tools for processing brain neuronal connectivity graphs-feed-forward artificial neural network. Different configurations of multilayer perceptron were considered and tested against an inference of functional dependence in model systems, specifically on Rossler oscillators generated data. Graphs of training, test and dynamics were considered. Furthermore, analysis and comparison of the r-squared scores and executed time were collected to measure the efficiency of the configurations. © 2020 IEEE.

关键词： Multilayer neural networks

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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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