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Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller.

Perdikis, S and Leeb, R and Williamson, J and Ramsay, A and Tavella, M and Desideri, L and Hoogerwerf, E-J and Al-Khodairy, A and Murray-Smith, R and Millán, JDR (2014) 'Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller.' Journal of Neural Engineering, 11 (3). ISSN 1741-2552

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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: Article
Uncontrolled Keywords: Adult, Algorithms, Brain Mapping, Brain-Computer Interfaces, Communication Aids for Disabled, Electroencephalography, Evoked Potentials, Motor, Female, Humans, Imagination, Language, Male, Motor Cortex, Movement, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Software, User-Computer Interface
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 06 Oct 2020 11:22
Last Modified: 06 Oct 2020 12:15

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