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Neural signal analysis by landmark-based spectral clustering with estimated number of clusters
conference contribution
posted on 2014-09-03, 00:00 authored by Thanh Thi NguyenThanh Thi Nguyen, Abbas KhosraviAbbas Khosravi, Asim BhattiAsim Bhatti, Douglas CreightonDouglas Creighton, Saeid NahavandiSpike 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.
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Event
International Joint Conference on Neural Networks (2014 : Beijing, China)Pagination
4042 - 4049Publisher
IEEELocation
Beijing, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2014-07-06End date
2014-07-11ISBN-13
9781479914845Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2014, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
IJCNN 2014 : Proceedings of the 2014 International Joint Conference on Neural NetworksUsage metrics
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