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. 2021 Jul 6;11:13925. doi: 10.1038/s41598-021-93088-w

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© The Author(s) 2021

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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Comparison of the MSTD performance with four state-of-the-art spike sorting algorithms. Results are obtained using the synthesized dataset. (a) Illustration of the SSI metric. It considers both detection and clustering outputs integrated into a single value. (b) Comparison of the proposed fully automatic spike sorting algorithm with four state-of-the-art algorithms employing SSI. The shaded area is the standard error. The proposed MSTD algorithm outperforms all other compared algorithms considering both detection and clustering performances, simultaneously. Only for SNRs lower than 5 dB, GMM has a better performance than the proposed algorithm. (c) The effect of miss detection, false alarm, and clustering performance on SSI. The MSTD algorithm has the lowest performance in terms of miss detection. (d) CA-CA plot between MSTD and the Bayes optimal classifier to investigate the robustness of the proposed method. The R-squared of the fitted line with the least square method is 0.98.