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 language | English |
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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 |
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Period | 12/6/18 → … |