Abstract
Day to day variability and non-stationarity caused by changes in subject motivation, learning and behavior pose a challenge in using local field potentials (LFP) for practical Brain Computer Interfaces. Pattern recognition algorithms require that the features possess little to no variation from the training to test data. As such models developed on one day fail to represent the characteristics on the other day. This paper provides a solution in the form of adaptive spatial features. We propose an algorithm to capture the local spatial variability of LFP patterns and provide accurate long-term decoding. This algorithm achieved more than 95% decoding of eight movement directions two weeks after its initial training.
Original language | English (US) |
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Title of host publication | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1642-1645 |
Number of pages | 4 |
Volume | 2014 |
ISBN (Electronic) | 9781424479290 |
DOIs | |
State | Published - Jan 1 2014 |
Event | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States Duration: Aug 26 2014 → Aug 30 2014 |
Publication series
Name | Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference |
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Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISSN (Print) | 1557-170X |
Other
Other | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 |
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Country/Territory | United States |
City | Chicago |
Period | 8/26/14 → 8/30/14 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
Keywords
- Brain Computer Interface
- Local Field Potentials
- Long-term decoding