Authors:
Alexander E. Hramov
1
;
Elena N. Pitsik
1
;
Parth Chholak
2
;
Vladimir A. Maksimenko
1
;
Nikita S. Frolov
1
;
Semen A. Kurkin
1
and
Alexander E. 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
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 inter
face (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.
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