title: Quality metrics of spike sorting using neighborhood components analysis.
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 L ratio. 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.
authors.affiliation: School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, PR China.
authors.forename: Xinyu
authors.initials: X
authors.lastname: Liu
authors.affiliation: School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, PR China.
authors.forename: Hong
authors.initials: H
authors.lastname: Wan
authors.affiliation: School of Electrical Engineering, Zhengzhou University, Zhengzhou 450001, PR China.
authors.forename: Li
authors.initials: L
authors.lastname: Shi
keywords: Kolmogorov-Smirnov test; neighborhood component analysis; quantitative measure; spike sorting; wavelet transform.