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. 2022 Sep 15;12:15504. doi: 10.1038/s41598-022-19771-8

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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The sorting results on the in-vivo Dataset B when comparing 6 spike sorting algorithms. (a) Two-dimensional feature subspace extracted by each method. The data points are colored according to the sorting results, in which red, green and blue represent cluster 1, cluster 2 and cluster 3 respectively. The waveform panel shows the average spike waveforms of three clusters obtained by LDA-DP, and the shaded part represents the standard deviation. (b) The precision rate and the recall rate obtained according to partial ground truth on dataset d533101 in HC1. (c,d) The precision rate and recall rate comparison with regard to SNR on all the 43 datasets in Dataset B.