Abstract
Electrocorticography has been widely explored as a long-term signal acquisition platform for brain-computer interface (BCI) control of upper extremity prostheses. However, a comprehensive study of elementary upper extremity movements and their relationship to electrocorticogram (ECoG) signals has yet to be performed. This study examines whether kinematic parameters of 6 elementary upper extremity movements can be decoded from ECoG signals in 3 subjects undergoing subdural electrode placement for epilepsy surgery evaluation. To this end, we propose a 2-stage decoding approach that consists of a state decoder to determine idle/move states, followed by a Kalman filter-based trajectory decoder. This proposed decoder successfully classified idle/move states with an average accuracy of 91%, and the correlation between decoded and measured trajectory averaged 0.70 for position and 0.68 for velocity. These performances represent an improvement over a simple regression-based approach.
Original language | English (US) |
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Title of host publication | International IEEE/EMBS Conference on Neural Engineering, NER |
Pages | 969-972 |
Number of pages | 4 |
DOIs | |
State | Published - 2013 |
Externally published | Yes |
Event | 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 - San Diego, CA, United States Duration: Nov 6 2013 → Nov 8 2013 |
Other
Other | 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013 |
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Country/Territory | United States |
City | San Diego, CA |
Period | 11/6/13 → 11/8/13 |
ASJC Scopus subject areas
- Artificial Intelligence
- Mechanical Engineering