Time-varying system identification using an ultra-orthogonal forward regression and multiwavelet basis functions with applications to EEG

Li, Y., Cui, W., Guo, Y. et al. (3 more authors) (2017) Time-varying system identification using an ultra-orthogonal forward regression and multiwavelet basis functions with applications to EEG. IEEE Transactions on Neural Networks and Learning Systems. ISSN 2162-237X

Abstract

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Copyright, Publisher and Additional Information: © 2017 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.
Keywords: B-splines; EEG; mutual information (MI); ultra-orthogonal forward regression (UOFR); time-varying system identification; parameter estimation; TV; Brain modeling; Adaptation models; Splines (mathematics); Time-varying systems; Electroencephalography
Dates:
  • Accepted: 24 May 2017
  • Published (online): 22 June 2017
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: 25 May 2017 11:59
Last Modified: 20 Jan 2020 15:19
Published Version: https://doi.org/10.1109/TNNLS.2017.2709910
Status: Published online
Publisher: Institute of Electrical and Electronics Engineers
Refereed: Yes
Identification Number: https://doi.org/10.1109/TNNLS.2017.2709910

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