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

Research output: Contribution to conferencePaper

1 Citation (Scopus)

Abstract

© 2017 IEEE. 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.
Original languageEnglish
Pages1-2
Number of pages2
DOIs
Publication statusPublished - 12 Jun 2018
Externally publishedYes
Event2017 International Symposium on Wearable Robotics and Rehabilitation, WeRob 2017 -
Duration: 12 Jun 2018 → …

Conference

Conference2017 International Symposium on Wearable Robotics and Rehabilitation, WeRob 2017
Period12/6/18 → …

Fingerprint

Brain computer interface
Independent component analysis
Discriminant analysis
Electroencephalography
Frequency bands
Support vector machines
Classifiers

Cite this

Rodriguez-Ugarte, M., Angulo-Sherman, I. N., Ianez, E., Ortiz, M., & Azorin, J. M. (2018). Preliminary study of pedaling motor imagery classification based on EEG signals. 1-2. Paper presented at 2017 International Symposium on Wearable Robotics and Rehabilitation, WeRob 2017, . https://doi.org/10.1109/WEROB.2017.8383851
Rodriguez-Ugarte, M. ; Angulo-Sherman, I. N. ; Ianez, E. ; Ortiz, M. ; Azorin, J. M. / Preliminary study of pedaling motor imagery classification based on EEG signals. Paper presented at 2017 International Symposium on Wearable Robotics and Rehabilitation, WeRob 2017, .2 p.
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Rodriguez-Ugarte, M, Angulo-Sherman, IN, Ianez, E, Ortiz, M & Azorin, JM 2018, 'Preliminary study of pedaling motor imagery classification based on EEG signals' Paper presented at 2017 International Symposium on Wearable Robotics and Rehabilitation, WeRob 2017, 12/6/18, pp. 1-2. https://doi.org/10.1109/WEROB.2017.8383851

Preliminary study of pedaling motor imagery classification based on EEG signals. / Rodriguez-Ugarte, M.; Angulo-Sherman, I. N.; Ianez, E.; Ortiz, M.; Azorin, J. M.

2018. 1-2 Paper presented at 2017 International Symposium on Wearable Robotics and Rehabilitation, WeRob 2017, .

Research output: Contribution to conferencePaper

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AB - © 2017 IEEE. 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.

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Rodriguez-Ugarte M, Angulo-Sherman IN, Ianez E, Ortiz M, Azorin JM. Preliminary study of pedaling motor imagery classification based on EEG signals. 2018. Paper presented at 2017 International Symposium on Wearable Robotics and Rehabilitation, WeRob 2017, . https://doi.org/10.1109/WEROB.2017.8383851