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

doi:10.1109/WEROB.2017.8383851

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 conference › Paper

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 language	English
Pages	1-2
Number of pages	2
DOIs	https://doi.org/10.1109/WEROB.2017.8383851
Publication status	Published - 12 Jun 2018
Externally published	Yes
Event	2017 International Symposium on Wearable Robotics and Rehabilitation, WeRob 2017 - Duration: 12 Jun 2018 → …

Conference

Conference	2017 International Symposium on Wearable Robotics and Rehabilitation, WeRob 2017
Period	12/6/18 → …

Access to Document

10.1109/WEROB.2017.8383851

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85049961524&origin=inward

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Cite this

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title = "Preliminary study of pedaling motor imagery classification based on EEG signals",

abstract = "{\textcopyright} 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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AU - Angulo-Sherman, I. N.

AU - Ianez, E.

AU - Ortiz, M.

AU - Azorin, J. M.

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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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