Giles, J., Ang, K.K., Mihaylova, L.S. orcid.org/0000-0001-5856-2223 et al. (1 more author) (2018) Data space adaptation for multiclass motor imagery-based BCI. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 40th International Conference of the IEEE Engineering in Medicine and Biology Society, 18-21 Jul 2018, Honolulu, Hawaii. IEEE , pp. 2004-2007. ISBN 978-1-5386-3646-6
Abstract
Various adaptation techniques have been proposed to address the non-stationarity issue faced by electroencephalogram (EEG)-based brain-computer interfaces (BCIs). However, most of these adaptation techniques are only suitable for binary-class BCIs. This paper proposes a supervised multiclass data space adaptation technique (MDSA) to transform the test data using a linear transformation such that the distribution difference between the multiclass train and test data is minimized. The results of using the proposed MDSA on BCI Competition IV dataset 2a improved the classification accuracy by an average of 4.3\% when 20 trials per class were used from the test session to estimate adaptation transformation. The results also showed that the proposed MDSA algorithm outperformed the multi pooled mean linear discrimination (MPMLDA) technique with as few as 10 trials per class used for calculating the transformation matrix. Hence the results showed the effectiveness of the proposed MDSA algorithm in addressing non-stationarity issue for multiclass EEG-based BCI.
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. |
Keywords: | Electroencephalography; Feature extraction; Transforms; Covariance matrices; Training data; Calibration; Training |
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: | 30 Apr 2018 14:25 |
Last Modified: | 28 Nov 2018 14:24 |
Published Version: | https://doi.org/10.1109/EMBC.2018.8512643 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/EMBC.2018.8512643 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:129934 |