Classification of gait motor imagery while standing based on electroencephalographic bandpower

I. N. Angulo-Sherman, M. Rodríguez-Ugarte, E. Iáñez, J. M. Azorín

Resultado de la investigación

Resumen

Brain-computer interfaces (BCIs) translate brain signals into commands for a device. BCIs are a complementary option in therapy during gait rehabilitation. This paper presents a strategy based on electroencephalographic (EEG) bandpower for detecting gait motor imagery (MI) while being standing. In particular, µ (8–13 Hz) and 20–35 Hz bands were used. Preliminary results show that two out of three users could achieve an accuracy above 70% of correct classifications. The proposed strategy could be used in a MI-based BCI to enhance brain activity associated to the gait process.

Idioma originalEnglish
Título de la publicación alojadaLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Páginas61-67
Número de páginas7
ISBN (versión digital)9783319597720
DOI
EstadoPublished - 1 ene 2017
Publicado de forma externa
EventoLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duración: 1 ene 2017 → …

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen10338 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Período1/1/17 → …

Nota bibliográfica

Publisher Copyright:
© Springer International Publishing AG 2017.

All Science Journal Classification (ASJC) codes

  • Ciencia computacional teórica
  • Informática (todo)

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