Cepstrum based unsupervised spike classification Haggag, Sherif, Mohamed, Shady, Bhatti, Asim, Gu, Nong, Zhou, Hailing and Nahavandi, Saeid 2013, Cepstrum based unsupervised spike classification, in SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics, IEEE, Piscataway, N.J., pp. 3716-3720. Attached Files Name Description MIMEType Size Downloads Title Cepstrum based unsupervised spike classification Author(s) Haggag, Sherif Mohamed, Shady orcid.org/0000-0002-8851-1635 Bhatti, Asim orcid.org/0000-0001-6876-1437 Gu, Nong Zhou, Hailing orcid.org/0000-0001-5009-4330 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Conference name IEEE Systems, Man and Cybernetics. Conference (2013 : Manchester, England) Conference location Manchester, England Conference dates 13-16 Oct. 2013 Title of proceedings SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics Editor(s) [Unknown] Publication date 2013 Conference series IEEE Systems, Man and Cybernetics Conference Start page 3716 End page 3720 Total pages 5 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) Cepstrum spike detection superparamagnetic clustering wavelets Kolmogorov-Smirnov test Summary 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. Language eng Field of Research 110999 Neurosciences not elsewhere classified Socio Economic Objective 970101 Expanding Knowledge in the Mathematical Sciences HERDC Research category E1 Full written paper - refereed Copyright notice ©2013, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30058803 Document type: Conference Paper Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Mon, 09 Dec 2013, 10:43:57 EST Mon, 09 Dec 2013, 11:02:10 EST Tue, 10 Dec 2013, 13:54:42 EST Mon, 17 Feb 2014, 15:29:44 EST Tue, 18 Feb 2014, 08:31:42 EST Mon, 11 Aug 2014, 10:04:13 EST Wed, 27 Aug 2014, 23:54:55 EST Thu, 09 Oct 2014, 10:19:08 EST Wed, 21 Oct 2015, 14:56:16 EST Wed, 21 Oct 2015, 18:39:31 EST Filtered Full Citation counts:   Cited 8 times in TR Web of Science Cited 9 times in Scopus Search Google Scholar Access Statistics: 656 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Mon, 09 Dec 2013, 10:43:57 EST

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