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Spike sorting using hidden markov models
conference contribution
posted on 2013-01-01, 00:00 authored by Hailing Zhou, Shady MohamedShady Mohamed, Asim BhattiAsim Bhatti, Chee Peng LimChee Peng Lim, Nong Gu, Sherif Haggag, Saeid NahavandiIn this paper, hidden Markov models (HMM) is studied for spike sorting. We notice that HMM state sequences have capability to represent spikes precisely and concisely. We build a HMM for spikes, where HMM states respect spike significant shape variations. Four shape variations are introduced: silence, going up, going down and peak. They constitute every spike with an underlying probabilistic dependence that is modelled by HMM. Based on this representation, spikes sorting becomes a classification problem of compact HMM state sequences. In addition, we enhance the method by defining HMM on extracted Cepstrum features, which improves the accuracy of spike sorting. Simulation results demonstrate the effectiveness of the proposed method as well as the efficiency.
History
Event
Neural Information Processing. Conference (20th : 2013 : Daegu, Korea)Pagination
553 - 560Publisher
SpringerLocation
Daegu, KoreaPlace of publication
Berlin, GermanyPublisher DOI
Start date
2013-11-03End date
2013-11-07ISSN
0302-9743eISSN
1611-3349ISBN-13
9783642420542ISBN-10
3642420540Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2013, SpringerEditor/Contributor(s)
M Lee, A Hirose, Z Hou, R KilTitle of proceedings
ICONIP 2013 : Neural Information Processing : 20th International Conference, Daegu, Korea, November 2013 Proceedings, Part 1Usage metrics
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