Development and assessment of a self-paced BCI-VR paradigm using multimodal stimulation and adaptive performance

Abstract

Motor-Imagery based Brain-Computer Interfaces (BCIs) can provide alternative communication pathways to neurologically impaired patients. The combination of BCIs and Virtual Reality (VR) can provide induced illusions of movement to patients with low-level of motor control during motor rehabilitation tasks. Unfortunately, current BCI systems lack reliability and good performance levels in comparison with other types of computer interfaces. To date, there is little evidence on how BCI-based motor training needs to be designed for transferring rehabilitation improvements to real life. Based on our previous work, we showcase the development and assessment of NeuRow, a novel multiplatform immersive VR environment that makes use of multimodal stimulation through vision, sound and vibrotactile feedback and delivered through a VR Head Mounted Display. In addition, we integrated the Adaptive Performance Engine (APE), a statistical approach to optimize user control in a self-paced BCI-VR paradigm. In this paper, we describe the development and pilot assessment of NeuRow as well as its integration and assessment with APE.