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Memorias de investigación
Ponencias en congresos:
Using artificial neural networks for classification of kinesthetic and visual imaginary movements by meg data
Año:2019
Áreas de investigación
  • Procesos cognitivos
Datos
Descripción
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
Internacional
Si
Nombre congreso
Saratov Fall Meeting 2019: Computations and Data Analysis: from Nanoscale Tools to Brain Functions
Tipo de participación
OTHERS
Lugar del congreso
Saratov, Russian Federation
Revisores
Si
ISBN o ISSN
9781510637221
DOI
10.1117/12.2563813
Fecha inicio congreso
23/09/2019
Fecha fin congreso
27/09/2019
Desde la página
1
Hasta la página
6
Título de las actas
Using artificial neural networks for classification of kinesthetic and visual imaginary movements by meg data
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Semen Kurkin (Innopolis University)
  • Autor: Parth Chholak (UPM)
  • Autor: Julia Guiomar Niso Galan (UPM)
  • Autor: Nikita Frolov (Innopolis University)
  • Autor: Alexander Pisarchik (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB
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