title: Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller.
abstract: 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.
  
  
  METHODS
  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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering, École Polytechnique Fédérale de Lausanne, Station 11, 1015 Lausanne, Switzerland.
authors.forename: S
authors.initials: S
authors.lastname: Perdikis
authors.forename: R
authors.initials: R
authors.lastname: Leeb
authors.forename: J
authors.initials: J
authors.lastname: Williamson
authors.forename: A
authors.initials: A
authors.lastname: Ramsay
authors.forename: M
authors.initials: M
authors.lastname: Tavella
authors.forename: L
authors.initials: L
authors.lastname: Desideri
authors.forename: E-J
authors.initials: EJ
authors.lastname: Hoogerwerf
authors.forename: A
authors.initials: A
authors.lastname: Al-Khodairy
authors.forename: R
authors.initials: R
authors.lastname: Murray-Smith
authors.forename: J D R
authors.initials: JD
authors.lastname: Millán
mesh_terms: D000328:Adult; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods; D062207:Brain-Computer Interfaces; D003143:Communication Devices for People with Disabilities; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology; D005260:Female; D006801:Humans; D007092:Imagination / Q000502:physiology*; D007802:Language; D008297:Male; D009044:Motor Cortex / Q000502:physiology; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D014584:User-Computer Interface