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Descripción
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| The development of brain computer interfaces, especially the ones related to controlling exoskeletons and neurorehabilitation of stroke patients, strongly relies on our understanding of motor system and its neuronal mechanism. Motor imagery (MI) has turned out to be one of the most popular experimental regimes to study this system. Kinesthetic imagery (KI) is a kind of MI which shares a large portion of its neuronal pathway with real movements, except for having an additional inhibitory mechanism to prevent movement execution. Our magnetoencephalographic (MEG) experiments with ten untrained subjects revealed that this inhibitory control implied local neuronal desynchronization. We found that the motor-related communication between the inferior parietal cortex and the prefrontal cortex was carried out using the mu-frequency range. Additionally, three gamma frequencies were also pinpointed that encode the motor command specifics. Using artificial neural networks (ANNs) we classified left- and right-hand MI which reached maximal accuracy when we included these three gamma frequencies in the input signal for ANN. We suggest that mu-activity acts as a carrier of gamma-activity between inferior and parietal areas utilising phase-amplitude coupling | |
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Internacional
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Si |
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Nombre congreso
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3rd School on Dynamics of Complex Networks and their Application in Intellectual Robotics, DCNAIR 2019 |
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Tipo de participación
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960 |
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Lugar del congreso
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Innopolis, Russia |
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Revisores
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Si |
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ISBN o ISSN
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978-172812470-4 |
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DOI
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10.1109/DCNAIR.2019.8875579 |
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Fecha inicio congreso
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09/09/2019 |
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Fecha fin congreso
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11/09/2019 |
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Desde la página
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39 |
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Hasta la página
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45 |
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Título de las actas
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3rd School on Dynamics of Complex Networks and their Application in Intellectual Robotics, DCNAIR 2019 |