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. 2022 Jun 27;12:10862. doi: 10.1038/s41598-022-14458-6

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© The Author(s) 2022, corrected publication 2023

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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Euclidean distance between the benchmarks of the sensor positions and the registered positions. The probability density function of the localization errors obtained with CPD affine (top left panel), CPD non linear (top right panel), ANTs affine (bottom left panel) and ANTs non linear (bottom right panel) are superimposed to the probability density function of the localization errors obtained with the affine transformation based on landmarks. In the lower part of each panel, the same data are presented as a box plot. Left and right edges of the box indicate the 25th and 75th percentile, whereas the central mark indicates the median. The whiskers extend to the most extreme data value not considered an outlier. Each dot represents a sensor of a participant. Probability density functions were estimated with the kernel density estimation method and graphs were generated as described in Allen et al.⁶⁵.