DocumentCode :
3047476
Title :
Cepstrum Based Unsupervised Spike Classification
Author :
Haggag, S. ; Mohamed, Salina ; Bhatti, A. ; Nong Gu ; Hailing Zhou ; Nahavandi, S.
Author_Institution :
Centre for Intell. Syst. Res., Deakin Univ., Geelong, VIC, Australia
fYear :
2013
fDate :
13-16 Oct. 2013
Firstpage :
3716
Lastpage :
3720
Abstract :
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 super paramagnetic 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.
Keywords :
brain; feature selection; medical signal detection; pattern clustering; signal classification; signal representation; statistical testing; KS test; Kolmogorov-Smirnov test; brain neurons; cepstrum based unsupervised spike classification; feature representation; feature selection; multichannel recordings; neural spikes; neural spikes clustering; real-time spike analysis; spike detection; spike signal cepstrum; spike sorting accuracy; super paramagnetic method; threshold-based method; Cepstrum; Clustering algorithms; Discrete Fourier transforms; Feature extraction; Neurons; Noise level; Sorting; Cepstrum; Kolmogorov-Smirnov test; Spike detection; Superparamagnetic clustering; Wavelets;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2013 IEEE International Conference on
Conference_Location :
Manchester
Type :
conf
DOI :
10.1109/SMC.2013.633
Filename :
6722386
Link To Document :
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