Azab, A.M.M.E., Mihaylova, L. and Arvaneh, M. (2019) Weighted multi-task learning in classification domain for improving brain-computer interface. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 07-10 Oct 2018, Miyazaki, Japan. IEEE , pp. 1093-1098. ISBN 978-1-5386-6650-0
Abstract
One of the major limitations of brain computer interface (BCI) is its long calibration time. Due to between sessions/subjects nonstationarity, typically a big amount of training data needs to be collected at the beginning of each session in order to tune the parameters of the system for the target user. In this paper, a number of novel weighted multi-task transfer learning algorithms are proposed in the classification domain to reduce the calibration time without sacrificing the classification accuracy of the BCI system. The proposed algorithms use data from other subjects and combine them to estimate the classifier parameters for the target subject. This combination is done based on how similar the data from each subject is to the few trials available from the target subject. The proposed algorithms are evaluated using dataset 2a from BCI competition IV. According to the results, the proposed algorithms lead to reduce the calibration time by 75% and enhance the average classification accuracy at the same time.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 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: |
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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 Jul 2018 15:15 |
Last Modified: | 17 Jan 2020 01:39 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/SMC.2018.00193 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:132995 |