RESEARCH ARTICLE


Quality Metrics of Spike Sorting Using Neighborhood Components Analysis



Xinyu Liu, Hong Wan* , Li Shi*
School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, PR China


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Creative Commons License
© Liu et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to these authors at the School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, PR China; Tel: +86 371 67781421; Fax: +86-0371-67783113; E-mails: wanhong@zzu.edu.cn;shili@zzu.edu.cn


Abstract

While an electrode has allowed for simultaneously recording the activity of many neurons in microelectrode extracellular recording techniques, quantitative metrics of cluster quality after sorting to identify clusters suited for single unit analysis are lacking. In this paper, an objective measure based on the idea of neighborhood component analysis was described for evaluating cluster quality of spikes. The proposed method was tested with experimental and simulated extracellular recordings as well as compared to isolation distance and Lratio. The results of simulation and real data from the rodent primary visual cortex have shown that values of the proposed method were related to the accuracy of spike sorting, which could discriminate well- and poorly-separated clusters. It can apply on any study based on the activity of single neurons.

Keywords: Kolmogorov-Smirnov test, neighborhood component analysis, quantitative measure, spike sorting, wavelet transform..