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Descripción
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| The analysis of neurophysiological mechanisms responsible for motor imagery is essential for the development of brain-computer interfaces. The carried out magnetoencephalographic (MEG) experiments with voluntary participants confirm the existence of two types of motor imagery: kinesthetic imagery (KI) and visual imagery (VI), distinguished by activation and inhibition of different brain areas. For classification of the brain states associated with motor imagery, we used the hierarchical cluster analysis and a popular type of artificial neural networks called multilayer perceptron. The application of machine learning techniques allows us to classify motor imagery in raising right and left arms with an average accuracy of 70% for both KI and VI using appropriate filtration of input signals. The same average accuracy is achieved by optimizing MEG channels and reducing their | |
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Internacional
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Si |
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Nombre congreso
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Saratov Fall Meeting 2019: Computations and Data Analysis: from Nanoscale Tools to Brain Functions |
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Tipo de participación
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OTHERS |
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Lugar del congreso
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Saratov, Russian Federation |
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Revisores
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Si |
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ISBN o ISSN
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9781510637221 |
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DOI
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10.1117/12.2563813 |
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Fecha inicio congreso
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23/09/2019 |
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Fecha fin congreso
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27/09/2019 |
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Desde la página
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1 |
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Hasta la página
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6 |
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Título de las actas
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Using artificial neural networks for classification of kinesthetic and visual imaginary movements by meg data |