EC-PC spike detection for high performance brain-computer interface

EC-PC spike detection for high performance brain-computer interface
Title:
EC-PC spike detection for high performance brain-computer interface
Other Titles:
2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Keywords:
Publication Date:
25 August 2015
Citation:
W. k. Tam, R. So, C. Guan and Z. Yang, "EC-PC spike detection for high performance brain-computer interface," 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, 2015, pp. 5142-5145. doi: 10.1109/EMBC.2015.7319549
Abstract:
Spike detection is often the first step in neural signal processing. It has profound effects on subsequent steps down the signal processing pipeline. Most existing spike detection algorithms require manual setting of detection threshold, which is very inconvenient for long-term neural interface. Furthermore, these algorithms are usually only evaluated using simulated dataset. Few studies are devoted to evaluating how different spike detection algorithms affect decoding performance in brain-computer interface. We have proposed a new spike detection algorithm called “exponential component - power component” (EC-PC) that offers fully automatic unsupervised spike detection. In this study, we compared the performance of a motor decoding task when different spike detection algorithms were used. EC-PC is shown to produce a higher decoding accuracy compared with other existing algorithms. Our results suggest that EC-PC can help improve motor decoding performance of brain-computer interface.
License type:
PublisherCopyrights
Funding Info:
Description:
(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
ISSN:
1094-687X
1558-4615
978-1-4244-9271-8
978-1-4244-9270-1
Files uploaded:

File Size Format Action
07319549.pdf 807.02 KB PDF Open