Cross-Subject Transfer Learning for Boosting Recognition Performance in SSVEP-based BCIs

Zhang, Y, Xie, SQ orcid.org/0000-0002-8082-9112, Shi, C et al. (2 more authors) (2023) Cross-Subject Transfer Learning for Boosting Recognition Performance in SSVEP-based BCIs. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31. pp. 1574-1583. ISSN 1558-0210

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: This is an author produced version of an article accepted for publication in IEEE Transactions on Neural Systems and Rehabilitation Engineering, made available under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: Brain–computer interface (BCI) , electroencephalography (EEG) , steady-state visual evoked potential (SSVEP) , transfer learning , cross-subject
Dates:
  • Accepted: 25 February 2023
  • Published (online): 1 March 2023
  • Published: 1 March 2023
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds)
Funding Information:
FunderGrant number
EPSRC (Engineering and Physical Sciences Research Council)EP/S019219/1
UKRI (UK Research and Innovation)Not Known
Depositing User: Symplectic Publications
Date Deposited: 09 Mar 2023 14:18
Last Modified: 12 May 2023 01:19
Status: Published
Publisher: IEEE
Identification Number: https://doi.org/10.1109/TNSRE.2023.3250953

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