Preliminary study of pedaling motor imagery classification based on EEG signals

M. Rodriguez-Ugarte, I. N. Angulo-Sherman, E. Ianez, M. Ortiz, J. M. Azorin

Resultado de la investigación

1 Cita (Scopus)

Resumen

This is a preliminary study about which of two classifiers: Support vector machine (SVM) or linear discriminant analysis (LDA), and which frequency band: δ (0.1-4Hz), μ (8-12Hz) and β (6-31Hz), provide higher accuracy using brain-computer interface (BCI) for detecting two different cognitive states: Pedaling (a motor complex imagery task) and relaxation. Results show that after using independent components analysis, in δ band for 3 out of 5 subjects achieved over 90% of accuracy and the other two over 60% of accuracy.

Idioma originalEnglish
Páginas1-2
Número de páginas2
DOI
EstadoPublished - 12 jun 2018
Publicado de forma externa
Evento2017 International Symposium on Wearable Robotics and Rehabilitation, WeRob 2017 -
Duración: 12 jun 2018 → …

Conference

Conference2017 International Symposium on Wearable Robotics and Rehabilitation, WeRob 2017
Período12/6/18 → …

All Science Journal Classification (ASJC) codes

  • Rehabilitación
  • Inteligencia artificial
  • Ingeniería biomédica
  • Control y optimización
  • Neurología clínica

Huella

Profundice en los temas de investigación de 'Preliminary study of pedaling motor imagery classification based on EEG signals'. En conjunto forman una huella única.

Citar esto