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. 2020 Jan 29;10:1429. doi: 10.1038/s41598-020-58097-1

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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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Feature ensemble volitional state go-phase confusion matrices. Offline decoding accuracies were computed using an LDA classifier implemented within a 10-dimensional CSIM representation of the neural feature data, using 10-fold cross-validation, over a 400 ms sliding window stepped down in 100 ms increments. Classification accuracies for individual volitional states were averaged over all time points within the go phase of the trial, resulting in a confusion matrices of true vs. predicted (P) observed (O), imagined (I), and attempted (A) volitional states. Note that the attempted volitional state is classified with a high accuracy rate across all sessions, while observed trials are classified with high accuracy during sessions 1, 7, and 11.