Scientific Papers

# Corrigendum: Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training

In the published article, there was an error. An additional detail regarding the computation of the weighted average classDistinct and classStability metrics was missing.

A correction has been made to Section 2. Materials and methods, subsection “2.1 Performance metric design”, paragraph 7. This sentence previously stated:

“The intra-class dispersion is computed using:

where α2 ∈ [0, 1] is a constant, ϕk−1, c is the intra-class dispersion for the class c trials of the (k-1)th block, and ϕk, c is the intra-class dispersion of class c trials computed only during the current (kth) block.”

The corrected sentences appear below:

“For the weighted average classDistinct and classStability metrics, we made the following modification to the calculation of the intra-class dispersion. We split the set of trials, T, into Ns subsets of Nt trials, Tj, such that

Subsets were formed by splitting trials according to the chronological order in which they were performed; for example, the first Nt trials performed during a block would be grouped into subset T1. Using these subsets, we computed a modified intra-class dispersion as:

where Ns is the number of trial subsets, Nt is the number of trials in each subset, ${\overline{\Gamma }}_{{T}_{j}}$ is the mean covariance matrix of trials within the jth subset of trials, ΓTj, i is the covariance matrix of the ith trial within subset Tj, and δR denotes the Riemannian distance. The motivation behind this modification was to reduce the impact of signal non-stationarities that may artificially increase the intra-class dispersion when considering a large number of trials. For our analysis, we set Nt = 5. Trial subsets were disjoint save for when computing within-block post-trial intra-class dispersion values. If the number of trials completed within the block was not divisible by Nt, subset TNs was formed using the most recently completed Nt trials; consequently, this subset could share up to Nt−1 trials with subset TNs − 1.

The post-trial intra-class dispersion was computed using this modified intra-class dispersion:

where α2 ∈ [0, 1] is a constant, ${\Phi }_{k–1,c}^{*}$ is the modified intra-class dispersion for the class c trials of the (k−1)th block, and ${\varphi }_{k,c}^{*}$ is the modified intra-class dispersion of class c trials completed only during the current (kth) block.”

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

## Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Keywords: brain-computer interface (BCI), electroencephalography (EEG), user training, Riemannian geometry, user evaluation, simulation

Citation: Ivanov N and Chau T (2023) Corrigendum: Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training. Front. Comput. Neurosci. 17:1286681. doi: 10.3389/fncom.2023.1286681

Received: 31 August 2023; Accepted: 06 November 2023;
Published: 17 November 2023.

Edited and reviewed by: Luz Maria Alonso-Valerdi, Monterrey Institute of Technology and Higher Education (ITESM), Mexico

Copyright © 2023 Ivanov and Chau. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Tom Chau, tom.chau@utoronto.ca