A subject-to-subject transfer learning framework based on Jensen-Shannon divergence for improving brain-computer interface

Giles, J., Ang, K.K., Mihaylova, L. et al. (1 more author) (2019) A subject-to-subject transfer learning framework based on Jensen-Shannon divergence for improving brain-computer interface. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). ICASSP 2019, 12-17 May 2019, Brighton, UK. IEEE , pp. 3087-3091. ISBN 978-1-4799-8131-1

Abstract

Metadata

Authors/Creators:
  • Giles, J.
  • Ang, K.K.
  • Mihaylova, L.
  • Arvaneh, M.
Copyright, Publisher and Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Dates:
  • Accepted: 2 February 2019
  • Published (online): 17 April 2019
  • Published: 17 April 2019
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 08 Mar 2019 12:34
Last Modified: 17 Apr 2020 00:38
Status: Published
Publisher: IEEE
Refereed: Yes
Identification Number: https://doi.org/10.1109/ICASSP.2019.8683331

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