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. 2020 Dec 10;10:21609. doi: 10.1038/s41598-020-77589-8

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© The Author(s) 2020

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 effects of aliasing. In (A) the percentage of aliased variance (from the lead field covariance matrix) is plotted as function of channel count. The Y axis is logarithmically scaled. This is repeated for different levels of source space smoothness (no smoothing, 10 mm, 20 mm and 50 mm). In (B) the histogram of variance explained across the entire cortex is calculated for an array of 100 sensors. The median variance explained (red dot) is highlighted as well as the 2.5% and 97.5% quantiles (red bar). In (C) this same information is visualised across the cortical surface to characterise the spatial profile of aliasing.