Classification of gait motor imagery while standing based on electroencephalographic bandpower

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages61-67
Number of pages7
ISBN (Electronic)9783319597720
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2017 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10338 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/1/17 → …

Fingerprint

Brain computer interface
Gait
Brain
Patient rehabilitation
Rehabilitation
Therapy
Imagery

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Angulo-Sherman, I. N., Rodríguez-Ugarte, M., Iáñez, E., & Azorín, J. M. (2017). Classification of gait motor imagery while standing based on electroencephalographic bandpower. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 61-67). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10338 LNCS). https://doi.org/10.1007/978-3-319-59773-7_7
Angulo-Sherman, I. N. ; Rodríguez-Ugarte, M. ; Iáñez, E. ; Azorín, J. M. / Classification of gait motor imagery while standing based on electroencephalographic bandpower. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2017. pp. 61-67 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Angulo-Sherman, IN, Rodríguez-Ugarte, M, Iáñez, E & Azorín, JM 2017, Classification of gait motor imagery while standing based on electroencephalographic bandpower. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10338 LNCS, pp. 61-67, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1/1/17. https://doi.org/10.1007/978-3-319-59773-7_7

Classification of gait motor imagery while standing based on electroencephalographic bandpower. / Angulo-Sherman, I. N.; Rodríguez-Ugarte, M.; Iáñez, E.; Azorín, J. M.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2017. p. 61-67 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10338 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Angulo-Sherman IN, Rodríguez-Ugarte M, Iáñez E, Azorín JM. Classification of gait motor imagery while standing based on electroencephalographic bandpower. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2017. p. 61-67. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-59773-7_7