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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Haggag, S. Mohamed, S. Bhatti, A. Nong Gu Hailing Zhou Nahavandi, S. |
| Copyright Year | 2013 |
| Description | Author affiliation: Centre for Intell. Syst. Res., Deakin Univ., Geelong, VIC, Australia (Haggag, S.; Mohamed, S.; Bhatti, A.; Nong Gu; Hailing Zhou; Nahavandi, S.) |
| 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. |
| Sponsorship | IEEE Syst., Man, Cybern. Soc. |
| Starting Page | 3716 |
| Ending Page | 3720 |
| File Size | 324696 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781479906529 |
| DOI | 10.1109/SMC.2013.633 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-10-13 |
| Publisher Place | UK |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Cepstrum Discrete Fourier transforms Feature extraction Sorting Neurons Noise level Clustering algorithms Kolmogorov-Smirnov test Spike detection Superparamagnetic clustering Wavelets |
| Content Type | Text |
| Resource Type | Article |
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