Memorias de investigación
Ponencias en congresos:
Machine learning approaches for classification of imaginary movement type by MEG data for neurorehabilitation
Año:2019

Áreas de investigación
  • Ingenierías

Datos
Descripción
The conducted 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. Similar to real movement, KI implies muscular sensation when performing an imaginary moving action that leads to event-related desynchronization (ERD) of motor-associated brain rhythms. By contrast, VI refers to visualization of the corresponding action that results in event-related synchronization (ERS) of ?- and ?-wave activity. A notable difference between KI and VI groups occurs in the frontal brain area. The application of artificial neural networks allows us to classify MI in raising right and left arms with average accuracy of 70% for both KI and VI using appropriate filtration of input signals.
Internacional
Si
Nombre congreso
3rd School on Dynamics of Complex Networks and their Application in Intellectual Robotics, DCNAIR 2019
Tipo de participación
960
Lugar del congreso
Innopolis, Russia
Revisores
Si
ISBN o ISSN
978-172812470-4
DOI
10.1109/DCNAIR.2019.8875579
Fecha inicio congreso
09/09/2019
Fecha fin congreso
11/09/2019
Desde la página
106
Hasta la página
108
Título de las actas
3rd School on Dynamics of Complex Networks and their Application in Intellectual Robotics, DCNAIR 2019

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Participantes

Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Tecnologías para Ciencias de la Salud
  • Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB