Classification of gait motor imagery while standing based on electroencephalographic bandpower

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

doi:10.1007/978-3-319-59773-7_7

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 proceeding › Conference 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 language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages	61-67
Number of pages	7
ISBN (Electronic)	9783319597720
DOIs	https://doi.org/10.1007/978-3-319-59773-7_7
Publication status	Published - 1 Jan 2017
Externally published	Yes
Event	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - Duration: 25 Jul 2011 → …

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10338 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period	25/7/11 → …

Bibliographical note

Funding Information:
This research has been carried out in the framework of the project Associate - Decoding and stimulation of motor and sensory brain activity to support long term potentiation through Hebbian and paired associative stimulation during rehabilitation of gait (DPI2014-58431-C4-2-R), funded by the Spanish Ministry of Economy and Competitiveness and by the European Union through the European Regional Development Fund (ERDF) “A way to build Europe”. Also, the Mexican Council of Science and Technology (CONACyT) provided I.N. Angulo-Sherman her scholarship.

Publisher Copyright:
© Springer International Publishing AG 2017.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-59773-7_7

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85027184967&origin=inward

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. et al. / 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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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.",

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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), 25/7/11. 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. et al.
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 proceeding › Conference contribution

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N1 - Funding Information: This research has been carried out in the framework of the project Associate - Decoding and stimulation of motor and sensory brain activity to support long term potentiation through Hebbian and paired associative stimulation during rehabilitation of gait (DPI2014-58431-C4-2-R), funded by the Spanish Ministry of Economy and Competitiveness and by the European Union through the European Regional Development Fund (ERDF) “A way to build Europe”. Also, the Mexican Council of Science and Technology (CONACyT) provided I.N. Angulo-Sherman her scholarship. Publisher Copyright: © Springer International Publishing AG 2017.

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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)). doi: 10.1007/978-3-319-59773-7_7

Classification of gait motor imagery while standing based on electroencephalographic bandpower

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