Classification of gait motor imagery while standing based on electroencephalographic bandpower
- ,
- M. Rodríguez-Ugarte,
- E. Iáñez,
- J. M. Azorín
- Centro de Investigacion y de Estudios Avanzados,
- Miguel Hernández University
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
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SciVal
Author count
4
SciVal
Paper percentile
28
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.
Publication Information
Output type
Research Output:
Chapter in Book/Report/Conference proceeding
Conference contribution
Original language
EnglishPages from-to (Number of pages)
Pages 61-67 (7 pages)Publication milestones
- Published - 01/01/2017
Publication status
Published - 01/01/2017
Publication series
- Publication series name: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print): 0302-9743
ISSN (Electronic): 1611-3349
Volume: 10338 LNCS
ISBN (Print)
9783319597720ISBN (Electronic)
9783319597720Publication IDs
- Scopus: 85027184967
Host publication title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)Access to documents
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)