Abstract
| Original language | English |
|---|---|
| Pages | 1-2 |
| Number of pages | 2 |
| DOIs | |
| 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 → … |
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Preliminary study of pedaling motor imagery classification based on EEG signals. / Rodriguez-Ugarte, M.; Angulo-Sherman, I. N.; Ianez, E.; Ortiz, M.; Azorin, J. M.
2018. 1-2 Paper presented at 2017 International Symposium on Wearable Robotics and Rehabilitation, WeRob 2017, .Research output: Contribution to conference › Paper
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.
PY - 2018/6/12
Y1 - 2018/6/12
N2 - © 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.
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.
U2 - 10.1109/WEROB.2017.8383851
DO - 10.1109/WEROB.2017.8383851
M3 - Paper
SP - 1
EP - 2
ER -