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 → …

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  • 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