Abstract
Brain-machine interfaces (BMIs) are systems that establish a direct connection between the human brain and a machine. These systems are applicable to neuro-rehabilitation. In this study, we propose a method of finding optimal threshold of canonical correlation analysis (CCA) based steady state visual evoked potentials (SSVEPs) classification for detecting resting state and reducing misclassification. As a result, we successfully found optimal threshold for the best performance. This result shows the possibility of SSVEP based exoskeleton online control with a proposed method.
Original language | English |
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DOIs | |
Publication status | Published - 2014 |
Event | 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014 - Gangwon, Korea, Republic of Duration: 2014 Feb 17 → 2014 Feb 19 |
Other
Other | 2014 International Winter Workshop on Brain-Computer Interface, BCI 2014 |
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Country/Territory | Korea, Republic of |
City | Gangwon |
Period | 14/2/17 → 14/2/19 |
Keywords
- Brain Machine Interfaces
- Electroencephalogram
- Exoskeleton
- Steady State Visual Evoked Potential (SSVEP)
ASJC Scopus subject areas
- Human-Computer Interaction
- Human Factors and Ergonomics