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

Nguyen,T, Khosravi,A, Bhatti,A, Creighton,D and Nahavandi,S 2014, Neural signal analysis by landmark-based spectral clustering with estimated number of clusters, in IJCNN 2014 : Proceedings of the 2014 International Joint Conference on Neural Networks, IEEE, Pistacaway, N.J., pp. 4042-4049, doi: 10.1109/IJCNN.2014.6889674.

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Title Neural signal analysis by landmark-based spectral clustering with estimated number of clusters
Author(s) Nguyen,TORCID iD for Nguyen,T orcid.org/0000-0001-9709-1663
Khosravi,AORCID iD for Khosravi,A orcid.org/0000-0001-6927-0744
Bhatti,AORCID iD for Bhatti,A orcid.org/0000-0001-6876-1437
Creighton,DORCID iD for Creighton,D orcid.org/0000-0002-9217-1231
Nahavandi,SORCID iD for Nahavandi,S orcid.org/0000-0002-0360-5270
Conference name International Joint Conference on Neural Networks (2014 : Beijing, China)
Conference location Beijing, China
Conference dates 6-11 July 2014
Title of proceedings IJCNN 2014 : Proceedings of the 2014 International Joint Conference on Neural Networks
Editor(s) [Unknown]
Publication date 2014
Conference series International Joint Conference on Neural Networks
Start page 4042
End page 4049
Total pages 8
Publisher IEEE
Place of publication Pistacaway, N.J.
Summary 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.
ISBN 9781479914845
Language eng
DOI 10.1109/IJCNN.2014.6889674
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071096

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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