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Nonlinearity, Information and robotics, (NIR) International Conference

作者： Vladislav Ostankovich Rauf Yagfarov Maksim Rassabin Salimzhan Gafurov Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russian Federation

ISBN: (数字)9781728187631

ISBN: (纸本)9781728187648

Self-driving vehicles contain a number of modules allowing them to autonomously navigate in uncertain environment. The robust, efficient, safe and accurate autonomous navigation are heavily depend on parameters of a perception module. In this paper, we consider perception module as a combination of object detection and road segmentation submodules. As a matter of fact, all of them are based on Deep learning technique. It leads to liability of a big training datasets to provide the accuracy, efficiency and robustness of a perception module for a self-driving car operating in a wide range of scenarios. This paper presents the GAN-based augmentation as a key factor allowing to improve the performances of perception. The provided research shows the comparison between classical augmentation method and CycleGAN-based method. The main focus is made on detection and segmentation problems at nights. The initial training data includes BDD100K dataset and our own one collected in winter time by means of front-view camera of a self-driving car developed in Innopolis University. The obtained results show the improvement of segmentation task in case of application of CycleGAN augmentation. However, the chosen method of GAN-based augmentation has not shown the positive influence on object detection due to appeared visual artifacts.

关键词： Training Visualization Roads Training data Object detection Autonomous automobiles Task analysis

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Spatio-temporal EEG activity in cortical network during visual percetion task

Spatio-temporal EEG activity in cortical network during visu...

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Nonlinearity, Information and robotics, (NIR) International Conference

作者： Alexander Kuc Natalija Malova Neuroscience and Cognivite Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Saratov Russia Neuroscience and Cognivite Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Russia

ISBN: (数字)9781728187631

ISBN: (纸本)9781728187648

A promising task is the analysis of the different rhythms of the brain cortical activity when performing cognitive tasks. Studying this issue allows to advance in the development of brain-computer interfaces. Some brain rhythms show certain dynamics during various cognitive activities. An important rhythm associated with cognitive activity is the alpha rhythm. In this paper, we study features of the dynamics of the alpha rhythm during bistable visual processing of information of varying complexity.

关键词： Visualization Information processing Brain-computer interfaces Complexity theory Task analysis Robots Monitoring

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Functional TMS Mapping During Sensorimotor Integration Task

Functional TMS Mapping During Sensorimotor Integration Task

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Neurotechnologies and Neurointerfaces (CNN), International Conference

作者： Anna Udoratina Nikita Grigorev Andrey Savosenkov Denis Ermolaev Vladimir Maksimenko Susanna Gordleeva Neurotechnology Department Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Mathematics and Mechanics Department Lobachevsky State University of Nizhny Novgorod Nizhny Novgorod Russia Neuroscience Research Institute Samara State Medical University Samara Russia

In the present research, we studied spatiotemporal influence of a single-pulse TMS on the process of sensorimotor integration. We considered how real and sham stimulation of the left or right premotor, motor or sensorimotor cortex, delivered 200 or 800 ms after the demonstration of a visual stimuli, affected response time of subjects during sensorimotor integration task. At this stage of the study, we found that a single TMS of the sensorimotor cortex, delivered 800 ms after stimulus presentation, increases response time.

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Bistable Dynamics of the Brain Extracellular Matrix in the Presence of Periodically Varying Neuronal Activity 5

Bistable Dynamics of the Brain Extracellular Matrix in the P...

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

作者： Rozhnova, Maiya A. Kazantsev, Victor B. Pankratova, Evgeniya V. Department of Applied Mathematics Institute of Information Technologies Mathematics and Mechanics Lobachevsky State University of Nizhni Novgorod Nizhny Novgorod Russia Neurotechnology Department Lobachevsky State University Nizhny Novgorod Russia Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Neuroscience Research Institute Samara State Medical University Samara Russia Center for Neurotechnology and Machine Learning Immanuel Kant Baltic Federal University Kaliningrad Russia

ISBN: (纸本)9781665442824

In this work, the role of the amplitude and frequency of the periodically varying neuronal firing rate in emergence of complicated dynamical modes in recently proposed mathematical model describing the change of extracellular matrix molecules concentration, was examined. Impact of the revealed co-existence of regular and chaotic modes in the extracellular matrix activity on the whole neuronal network functioning is discussed. © 2021 IEEE

关键词： Brain

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Position-based motion control for parallel manipulators under parametric uncertainties and with finite-time external disturbance rejection ⁎

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IFAC-PapersOnLine 2020年第2期53卷 8456-8461页

作者： Dmitrii Dobriborsci Sergey Kolyubin Natalia Gorokhova Marina Korotina Alexey Bobtsov Faculty of Control Systems and Robotics ITMO University St. Petersburg Russia Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia.

We consider the task of motion control for non-prehensile manipulation using parallel kinematics mechatronic setup, in particular, stabilization of a ball on a plate under unmeasured external harmonic disturbances. System parameters are assumed to be unknown, and only a ball position is measurable with a resistive touch sensor. To solve the task we propose a novel passivity-based output control algorithm that can be implemented for unstable linearized systems of an arbitrary relative degree. In contrast to previous works, we describe a new way to parametrize harmonic signal generators and an estimation algorithm with finite-time convergence. This scheme enables fast disturbance cancellation under control signal magnitude constraints.

关键词： Adaptive control output control motion control disturbance rejection finite-time estimation ball-and-plate

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Cognitive interaction during a collaborative attentional task 3

Cognitive interaction during a collaborative attentional tas...

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

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

ISBN: (纸本)9781728124704

The modern trend in neuroscience and engineering is the developing systems allowing increase human physical and cognitive abilities through human-machine interaction. In these systems the human's condition is controlled by a brain-computer interface and assistance is activated when cognitive (or physical) performance decreases. In the current paper we consider the system, where the human's cognitive performance is enhanced due to cognitive interaction with another human. The proposed interface was tested at experimental sessions, in which the subjects were asked for a long time to solve the problem of classification of ambiguous visual stimuli of varying complexity. The proposed interface allows to increase the average productivity of a group of people due to the distribution of cognitive load between all participants. © 2019 IEEE.

关键词： Brain computer interface

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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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Nonlinearity, Information and robotics, (NIR) International Conference

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

ISBN: (数字)9781728187631

ISBN: (纸本)9781728187648

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.

关键词： Time-frequency analysis Continuous wavelet transforms Wavelet analysis Electroencephalography Hemodynamics 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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Nonlinearity, Information and robotics, (NIR) International Conference

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

ISBN: (数字)9781728187631

ISBN: (纸本)9781728187648

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.

关键词： Brain Electroencephalography Task analysis Robots

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Learning Stabilizing Control Policies for a Tensegrity Hopper with Augmented Random Search

arXiv

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arXiv 2020年

作者： Kurenkov, Vladislav Hamed, Hany Savin, Sergei Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia Robotics Institute Innopolis University Innopolis Russia

In this paper, we consider tensegrity hopper - a novel tensegrity-based robot, capable of moving by hopping. The paper focuses on the design of the stabilizing control policies, which are obtained with Augmented Random Search method. In particular, we search for control policies which allow the hopper to maintain vertical stability after performing a single jump. It is demonstrated, that the hopper can maintain a vertical configuration, subject to the different initial conditions and with changing control frequency rates. In particular, lowering control frequency from 1000Hz in training to 500Hz in execution did not affect the success rate of the balancing task. Copyright © 2020, The Authors. All rights reserved.

关键词： Hoppers

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Detecting epileptic seizures using machine learning and interpretable features of human EEG

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The European Physical Journal Special Topics 2022年第5期232卷 673-682页

作者： Karpov, Oleg E. Afinogenov, Sergey Grubov, Vadim V. Maksimenko, Vladimir Korchagin, Sergey Utyashev, Nikita Hramov, Alexander E. National Medical and Surgical Center named after N. I. Pirogov Ministry of Healthcare of the Russian Federation Moscow Russia Faculty of Information Technology and Big Data Analysis Financial University under the Government of the Russian Federation Moscow Russia Baltic Center for Artificial Intelligence and Neurotechnology Immanuel Kant Baltic Federal University Kaliningrad Russia Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

Epilepsy is a neurological disorder distinguished by sudden and unexpected seizures. To diagnose epilepsy, clinicians register the signals of brain electric activity (electroencephalograms, EEG) and extract segments with seizures. It enables characterizing their type and finding an onset zone, a brain area where they originate. This procedure requires manual EEG deciphering, which is slow and necessitates the assistance of machine learning (ML) algorithms. Traditionally, ML handles this issue in a supervised fashion, i.e., after the training on the representative data, it constructs a boundary in the feature space that separates classes. As the number of features grows, this boundary becomes complex and less generalized. The feature space of brain data is high dimensional. The standard recording includes 30 signals and 50 frequencies resulting in 1500 features. Using additional time-domain features may further enlarge the feature space. Thus, selecting appropriate features is a big part of the successful classification. The selection procedure relies on either a data-based mathematical approach (e.g., principal components, PCs) or the expert domain knowledge of data (explainable features, EFs). Here, we demonstrate the benefits of using EFs. For the EEG data of 30 epileptic patients, we trained a RandomForest algorithm using PCs and EFs. The feature importance analysis revealed that explainable features outperform principal components.

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