Adaptive Riemannian BCI for Enhanced Motor Imagery Training Protocols

Freer, D., Deligianni, F. and Yang, G.-Z. (2019) Adaptive Riemannian BCI for Enhanced Motor Imagery Training Protocols. In: 2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Chicago, IL, USA, 19-22 May 2019, ISBN 9781538674772 (doi:10.1109/BSN.2019.8771079)

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Abstract

Traditional methods of training a Brain-Computer Interface (BCI) on motor imagery (MI) data generally involve multiple intensive sessions. The initial sessions produce simple prompts to users, while later sessions additionally provide realtime feedback to users, allowing for human adaptation to take place. However, this protocol only permits the BCI to update between sessions, with little real-time evaluation of how the classifier has improved. To solve this problem, we propose an adaptive BCI training framework which will update the classifier in real time to provide more accurate feedback to the user on 4-class motor imagery data. This framework will require only one session to fully train a BCI to a given subject. Three variations of an adaptive Riemannian BCI were implemented and compared on data from both our own recorded datasets and the commonly used BCI Competition IV Dataset 2a. Results indicate that the fastest and least computationally expensive adaptive BCI was able to correctly classify motor imagery data at a rate 5.8% higher than when using a standard protocol with limited data. In addition it was confirmed that the adaptive BCI automatically improved its performance as more data became available.

Item Type:Conference Proceedings
Additional Information:This research is partially supported by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant reference EP/R026092/1.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Deligianni, Dr Fani
Authors: Freer, D., Deligianni, F., and Yang, G.-Z.
College/School:College of Science and Engineering > School of Computing Science
ISSN:2376-8894
ISBN:9781538674772
Published Online:25 July 2019
Copyright Holders:Copyright © 2019 IEEE
First Published:First published in Proceedings of the 2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN)
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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