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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 NahavandiSaeid Nahavandi
In 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 - 3720

Publisher

IEEE

Location

Manchester, England

Place of publication

Piscataway, N.J.

Start date

2013-10-13

End date

2013-10-16

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2013, IEEE

Title of proceedings

SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics