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

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 → …

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

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