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

Thanh Nguyen, Abbas Khosravi, Asim Bhatti, Douglas Creighton, Saeid Nahavandi

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages4042-4049
Number of pages8
ISBN (Electronic)9781479914845
DOIs
Publication statusPublished - 2014
Externally publishedYes
EventIEEE International Joint Conference on Neural Networks 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014
https://ieeexplore.ieee.org/xpl/conhome/6880678/proceeding (Proceedings)

Conference

ConferenceIEEE International Joint Conference on Neural Networks 2014
Abbreviated titleIJCNN 2014
Country/TerritoryChina
CityBeijing
Period6/07/1411/07/14
Internet address

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