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.
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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 -