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

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 → …

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 I.N. Angulo-Sherman thanks the Mexican Council of Science and Technology (CONACyT) for her scholarship (369756). M. R, E. I.,J. M. A. are with the Brain-Machine Interface Systems Lab, in the Miguel Hernández University, Elche, Spain email:maria.rodriguezu@umh.es).

Publisher Copyright:
© 2017 IEEE.

Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.

All Science Journal Classification (ASJC) codes

Rehabilitation
Artificial Intelligence
Biomedical Engineering
Control and Optimization
Clinical Neurology

Access to Document

10.1109/WEROB.2017.8383851

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

Fingerprint Dive into the research topics of 'Preliminary study of pedaling motor imagery classification based on EEG signals'. Together they form a unique fingerprint.

Cite this

@conference{5203ee82d26947528ae4a5c05c72a3ad,

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

abstract = "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.",

author = "M. Rodriguez-Ugarte and Angulo-Sherman, {I. N.} and E. Ianez and M. Ortiz and Azorin, {J. M.}",

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 I.N. Angulo-Sherman thanks the Mexican Council of Science and Technology (CONACyT) for her scholarship (369756). M. R, E. I.,J. M. A. are with the Brain-Machine Interface Systems Lab, in the Miguel Hern{\'a}ndez University, Elche, Spain email:maria.rodriguezu@umh.es). Publisher Copyright: {\textcopyright} 2017 IEEE. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.; 2017 International Symposium on Wearable Robotics and Rehabilitation, WeRob 2017 ; Conference date: 12-06-2018",

year = "2018",

month = jun,

day = "12",

doi = "10.1109/WEROB.2017.8383851",

language = "English",

pages = "1--2",

}

TY - CONF

T1 - Preliminary study of pedaling motor imagery classification based on EEG signals

AU - Rodriguez-Ugarte, M.

AU - Angulo-Sherman, I. N.

AU - Ianez, E.

AU - Ortiz, M.

AU - Azorin, J. M.

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 I.N. Angulo-Sherman thanks the Mexican Council of Science and Technology (CONACyT) for her scholarship (369756). M. R, E. I.,J. M. A. are with the Brain-Machine Interface Systems Lab, in the Miguel Hernández University, Elche, Spain email:maria.rodriguezu@umh.es). Publisher Copyright: © 2017 IEEE. Copyright: Copyright 2018 Elsevier B.V., All rights reserved.

PY - 2018/6/12

Y1 - 2018/6/12

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85049961524&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85049961524&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/0d8299d9-0e3f-336b-88fa-6b19eb89005b/

U2 - 10.1109/WEROB.2017.8383851

DO - 10.1109/WEROB.2017.8383851

M3 - Paper

SP - 1

EP - 2

T2 - 2017 International Symposium on Wearable Robotics and Rehabilitation, WeRob 2017

Y2 - 12 June 2018

ER -