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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 Dataset C 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, yellow and blue represent cluster 1, cluster 2, cluster 3 and cluster 4 respectively. The last column shows the average spike waveforms of four clusters obtained by LDA-DP, and the shaded part represents the standard deviation. (b) Comparison of cluster quality using spikes from one particular channel 54 in Dataset C. We conducted a fivefold cross-validation and draw boxplots of the DBI index for 6 algorithms. *p < 0.05, **p < 0.01, ***p < 0.001, Kruskal–Wallis test. (c) Comparison of cluster quality using spikes from all 30 channels in Dataset C. We conducted a fivefold cross-validation for each channel and then drew the violin plot. The dashed lines represent the median of the DBI. *p < 0.05, ***p < 0.001, Kruskal–Wallis test.