Clinical evaluation of BrainTree, a motor imagery hybrid BCI speller

Abstract

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 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 6 severely disabled end-users and 10 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 (HCI) 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 modelbased 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.

Towards Independence: A BCI Telepresence Robot for People With Severe Motor Disabilities

“…Nevertheless, we want to point out that the BCI technology has been used by endusers also in other application fields [27], [28], since the same techniques, as applied in this paper, can be used also outside the area of robotic control, e.g. like in spelling applications [29]. Hence it is possible to transfer the technology from one application to another, and from healthy participants to end-users with disabilities [3].…”

The effect of multimodal and enriched feedback on SMR-BCI performance

“…Brain Computer Interfaces (BCIs) based on the modulation of sensorimotor rhythms (SMR) classify differences in the electroencephalogram (EEG) elicited by different motor imagery (MI), actual movement or movement preparation (Pfurtscheller et al, 1997) and translate these into control commands, e.g., for a spelling application (Kübler et al, 2001;Perdikis et al, 2014;Wolpaw et al, 2002) or cursor control on a computer screen (Wolpaw et al, 1991). This provides an alternative communication channel for people diagnosed with neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS), who have only residual control of few muscles, which may be unreliable (Kübler et al, 2005).…”

Brain-Machine Interfaces: The Perception-Action Closed Loop: A Two-Learner System

“…In a BMI system, neural signals recorded from the brain are fed into a decoding algorithm that translates these signals into motor outputs to control a variety of practical devices for motor-disabled people [1]- [5]. Feedback from the prosthetic device, conveyed to the user either via normal sensory pathways or directly through brain stimulation, establishes a closed control loop.…”