Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller

Perdikis, S., Leeb, R., Williamson, J. , Ramsay, A. , Tavella, M., Desideri, L., Hoogerwerf, E.-J., Al-Khodairy, A., Murray-Smith, R. and Millán, J.d.R. (2014) Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller. Journal of Neural Engineering, 11(3), 036003. (doi: 10.1088/1741-2560/11/3/036003)

93305.pdf - Accepted Version



Objective. While brain–computer interfaces (BCIs) for communication have reached considerable technical maturity, there is still a great need for state-of-the-art evaluation by the end-users outside laboratory environments. To achieve this primary objective, it is necessary to augment a BCI with a series of components that allow end-users to type text effectively. Approach. This work presents the clinical evaluation of a motor imagery (MI) BCI text-speller, called BrainTree, by six severely disabled end-users and ten able-bodied users. Additionally, we define a generic model of code-based BCI applications, which serves as an analytical tool for evaluation and design. Main results. We show that all users achieved remarkable usability and efficiency outcomes in spelling. Furthermore, our model-based analysis highlights the added value of human–computer interaction techniques and hybrid BCI error-handling mechanisms, and reveals the effects of BCI performances on usability and efficiency in code-based applications. Significance. This study demonstrates the usability potential of code-based MI spellers, with BrainTree being the first to be evaluated by a substantial number of end-users, establishing them as a viable, competitive alternative to other popular BCI spellers. Another major outcome of our model-based analysis is the derivation of a 80% minimum command accuracy requirement for successful code-based application control, revising upwards previous estimates attempted in the literature.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick and Williamson, Dr John and Ramsay, Mr Andrew
Authors: Perdikis, S., Leeb, R., Williamson, J., Ramsay, A., Tavella, M., Desideri, L., Hoogerwerf, E.-J., Al-Khodairy, A., Murray-Smith, R., and Millán, J.d.R.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Journal of Neural Engineering
Publisher:IOP Publishing
ISSN (Online):1741-2552
Copyright Holders:Copyright © 2014 IOP Publishing Ltd.
First Published:First published in Journal of Neural Engineering 11(3):036003
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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