RESEARCH ARTICLE
Quality Metrics of Spike Sorting Using Neighborhood Components Analysis
Xinyu Liu, Hong Wan* , Li Shi*
Article Information
Identifiers and Pagination:
Year: 2014Volume: 8
First Page: 60
Last Page: 67
Publisher ID: TOBEJ-8-60
DOI: 10.2174/1874120701408010060
Article History:
Received Date: 23/1/2014Revision Received Date: 5/4/2014
Acceptance Date: 7/4/2014
Electronic publication date: 17 /9/2014
Collection year: 2014
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.
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.