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