Osuagwu, B. A., Zych, M. and Vuckovic, A. (2017) Is implicit motor imagery a reliable strategy for a brain computer interface? IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(12), pp. 2239-2248. (doi: 10.1109/TNSRE.2017.2712707)
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Abstract
Explicit motor imagery (eMI) is a widely used brain computer interface (BCI) paradigm, but not everybody can accomplish this task. Here we propose a BCI based on implicit motor imagery (iMI). We compared classification accuracy between eMI and iMI of hands. Fifteen able bodied people were asked to judge the laterality of hand images presented on a computer screen in a lateral or medial orientation. This judgement task is known to require mental rotation of a person’s own hands which in turn is thought to involve iMI. The subjects were also asked to perform eMI of the hands. Their electroencephalography (EEG) was recorded. Linear classifiers were designed based on common spatial patterns. For discrimination between left and right hand the classifier achieved maximum of 81 ± 8% accuracy for eMI and 83 ± 3% for iMI. These results show that iMI can be used to achieve similar classification accuracy as eMI. Additional classification was performed between iMI in medial and lateral orientations of a single hand; the classifier achieved 81 ± 7% for the left and 78 ± 7% for the right hand which indicate distinctive spatial patterns of cortical activity for iMI of a single hand in different directions. These results suggest that a special brain computer interface based on iMI may be constructed, for people who cannot perform explicit imagination, for rehabilitation of movement or for treatment of bodily spatial neglect.
Item Type: | Articles |
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Status: | Published |
Refereed: | Yes |
Glasgow Author(s) Enlighten ID: | Zych, Miss Magdalena and Vuckovic, Dr Aleksandra |
Authors: | Osuagwu, B. A., Zych, M., and Vuckovic, A. |
College/School: | College of Science and Engineering > School of Engineering College of Science and Engineering > School of Engineering > Biomedical Engineering |
Journal Name: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
Publisher: | IEEE |
ISSN: | 1534-4320 |
ISSN (Online): | 1558-0210 |
Published Online: | 29 June 2017 |
Copyright Holders: | Copyright © 2017 IEEE |
First Published: | First published in IEEE Transactions on Neural Systems and Rehabilitation Engineering 25(12): 2239-2248 |
Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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