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Authors: Alexander Hramov 1 ; Elena Pitsik 1 ; Parth Chholak 2 ; Vladimir Maksimenko 1 ; Nikita Frolov 1 ; Semen Kurkin 1 and Alexander Pisarchik 3

Affiliations: 1 Neuroscience and Cognitive Technology Laboratory, Innopolis University, 1 Universitetskaya str., Innopolis, 420500, The Republic of Tatarstan and Russia ; 2 Center for Biomedical Technology, Technical University of Madrid, Campus Montegancedo, 28223 Pozuelo de Alarcón, Madrid and Spain ; 3 Neuroscience and Cognitive Technology Laboratory, Innopolis University, 1 Universitetskaya str., Innopolis, 420500, The Republic of Tatarstan, Russia, Center for Biomedical Technology, Technical University of Madrid, Campus Montegancedo, 28223 Pozuelo de Alarcón, Madrid and Spain

ISBN: 978-989-758-380-3

ISSN: 2184-2809

Keyword(s): Brain-computer Interface, MEG, Motor Imagery, Exoskeleton, HCA, Artificial Neural Network, Wavelet Analysis.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Human-Machine Interfaces ; Image and Video Analysis ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Machine Learning in Control Applications ; Nonlinear Signals and Systems ; Real-Time Systems Control ; Robotics and Automation ; Signal Processing, Sensors, Systems Modeling and Control ; Time-Frequency Analysis ; Vision, Recognition and Reconstruction

Abstract: Motor imagery is a most commonly studied neurophysiological pattern that is used in brain-computer interfaces as a command for exoskeletons, bioprostheses, wheelchair and other robotic devices. The mechanisms of motor imagery manifestation in human brain activity include dynamics of motor-related frequency bands in various brain areas, among which the most common is sensorimotor rhythnm. In present work we consider time-frequency structure of magnitoencephalographical (MEG) motor imagery in untrained subjects. We conduct series of experiments to collect MEG motor imagery dataset in untrained subjects. We confirm the emergence of two types of motor imagery – visual (VI) and kinesthetic (KI), which cause different types of event-related potentials (ERP) dynamics and require different approaches to classification using mashine learning methods. We also reveal the impact of dataset optimization on the artificial neural network performance, which is essential topic in brain-computer interf ace (BCI) development. We show that developing classification stratedy based on time-frequency features of the particular MEG signal can increase classification accuracy of the VI mode to the level of the KI. (More)

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Paper citation in several formats:
Hramov, A.; Pitsik, E.; Chholak, P.; Maksimenko, V.; Frolov, N.; Kurkin, S. and Pisarchik, A. (2019). A MEG Study of Different Motor Imagery Modes in Untrained Subjects for BCI Applications.In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-380-3, ISSN 2184-2809, pages 188-195. DOI: 10.5220/0007810001880195

@conference{icinco19,
author={Hramov, A. and Elena N. Pitsik. and Parth Chholak. and Vladimir A. Maksimenko. and Nikita S. Frolov. and Semen A. Kurkin. and Alexander N. Pisarchik.},
title={A MEG Study of Different Motor Imagery Modes in Untrained Subjects for BCI Applications},
booktitle={Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2019},
pages={188-195},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007810001880195},
isbn={978-989-758-380-3},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - A MEG Study of Different Motor Imagery Modes in Untrained Subjects for BCI Applications
SN - 978-989-758-380-3
AU - Hramov, A.
AU - Pitsik, E.
AU - Chholak, P.
AU - Maksimenko, V.
AU - Frolov, N.
AU - Kurkin, S.
AU - Pisarchik, A.
PY - 2019
SP - 188
EP - 195
DO - 10.5220/0007810001880195

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