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Neural signal analysis by landmark-based spectral clustering with estimated number of clusters

Spike sorting plays an important role in analysing electrophysiological data and understanding neural functions. Developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice. This paper proposes an automatic unsupervised spike sorting method using the landmark-based spectral clustering (LSC) method in connection with features extracted by the locality preserving projection (LPP) technique. Gap statistics is employed to evaluate the number of clusters before the LSC can be performed. Experimental results show that LPP spike features are more discriminative than those of the popular wavelet transformation (WT). Accordingly, the proposed method LPP-LSC demonstrates a significant dominance compared to the existing method that is the combination between WT feature extraction and the superparamagnetic clustering. LPP and LSC are both linear algorithms that help reduce computational burden and thus their combination can be applied into realtime spike analysis.

History

Event

International Joint Conference on Neural Networks (2014 : Beijing, China)

Pagination

4042 - 4049

Publisher

IEEE

Location

Beijing, China

Place of publication

Piscataway, N.J.

Start date

2014-07-06

End date

2014-07-11

ISBN-13

9781479914845

Language

eng

Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2014, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

IJCNN 2014 : Proceedings of the 2014 International Joint Conference on Neural Networks