File(s) under permanent embargo
Cepstrum based unsupervised spike classification
conference contribution
posted on 2013-01-01, 00:00 authored by Sherif Haggag, Shady MohamedShady Mohamed, Asim BhattiAsim Bhatti, Nong Gu, Hailing Zhou, Saeid NahavandiIn this research, we study the effect of feature selection in the spike detection and sorting accuracy.We introduce a new feature representation for neural spikes from multichannel recordings. The features selection plays a significant role in analyzing the response of brain neurons. The more precise selection of features leads to a more accurate spike sorting, which can group spikes more precisely into clusters based on the similarity of spikes. Proper spike sorting will enable the association between spikes and neurons. Different with other threshold-based methods, the cepstrum of spike signals is employed in our method to select the candidates of spike features. To choose the best features among different candidates, the Kolmogorov-Smirnov (KS) test is utilized. Then, we rely on the superparamagnetic method to cluster the neural spikes based on KS features. Simulation results demonstrate that the proposed method not only achieve more accurate clustering results but also reduce computational burden, which implies that it can be applied into real-time spike analysis.
History
Event
IEEE Systems, Man and Cybernetics. Conference (2013 : Manchester, England)Pagination
3716 - 3720Publisher
IEEELocation
Manchester, EnglandPlace of publication
Piscataway, N.J.Start date
2013-10-13End date
2013-10-16Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2013, IEEETitle of proceedings
SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and CyberneticsUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC