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CORRECTION article

Front. Neurosci., 26 October 2022
Sec. Neural Technology
Volume 16 - 2022 | https://doi.org/10.3389/fnins.2022.1024150
This article is part of the Research Topic Multimodal Fusion Technologies and Applications in the Context of Neuroscience View all 17 articles

Corrigendum: Multi-person feature fusion transfer learning-based convolutional neural network for SSVEP-based collaborative BCI

This article is a correction to:

  1. Multi-person feature fusion transfer learning-based convolutional neural network for SSVEP-based collaborative BCI

    1. Read original article
\nPenghai LiPenghai Li1Jianxian SuJianxian Su1Abdelkader Nasreddine BelkacemAbdelkader Nasreddine Belkacem2Longlong Cheng Longlong Cheng3*Chao Chen Chao Chen4*
  • 1School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin, China
  • 2Department of Computer and Network Engineering, College of Information Technology, UAE University, Al Ain, United Arab Emirates
  • 3China Electronics Cloud Brain Technology Co., Ltd., Tianjin, China
  • 4Key Laboratory of Complex System Control Theory and Application, Tianjin University of Technology, Tianjin, China

A corrigendum on
Multi-person feature fusion transfer learning-based convolutional neural network for SSVEP-based collaborative BCI

by Li, P., Su, J., Belkacem, A. N., Cheng, L., and Chen, C. (2022). Front. Neurosci. 16:971039. doi: 10.3389/fnins.2022.971039

In the published article, there was an error in the article title as published. Instead of “Steady-state visually evoked potential collaborative BCI system deep learning classification algorithm based on multi-person feature fusion transfer learning-based convolutional neural network,” it should be “Multi-person feature fusion transfer learning-based convolutional neural network for SSVEP-based collaborative BCI.”

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: steady-state visually evoked potential, collaborative BCI, feature fusion, convolutional neural network, transfer learning

Citation: Li P, Su J, Belkacem AN, Cheng L and Chen C (2022) Corrigendum: Multi-person feature fusion transfer learning-based convolutional neural network for SSVEP-based collaborative BCI. Front. Neurosci. 16:1024150. doi: 10.3389/fnins.2022.1024150

Received: 21 August 2022; Accepted: 12 October 2022;
Published: 26 October 2022.

Edited and reviewed by: Michele Giugliano, International School for Advanced Studies (SISSA), Italy

Copyright © 2022 Li, Su, Belkacem, Cheng and Chen. 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: Longlong Cheng, chenglonglong@cecdat.com; Chao Chen, cccovb@hotmail.com

Disclaimer: 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.