SSVEP-Based BCI in Virtual Reality - Control of a Vacuum Cleaner Robot

Abstract

Brain-Computer Interfaces (BCIs) allow communication and control of the environment without the use of peripheral muscles. One of the standard BCI paradigms is based on steady state visual evoked potentials (SSVEPs), brain signals induced by gazing at a constantly flickering target. In this article, a VR SSVEP-based steering simulation is presented and evaluated in comparison to a standard desktop version. Three control classes were used to control the application. The experimental task was to steer a virtual vacuum robot and collect 10 dust piles (for this, at least 31 command classifications were required). Participants were instructed to complete the task twice, using a head mounted display (HMD) and the laptop screen for visual stimulation. All participants were able to complete the task in both scenarios. Mean accuracies of 98.91% and 97.48% and mean ITRs of 23.96 and 20.71 bits/min were achieved for the HMD and desktop control, respectively. On average, the number of commands needed to complete the task in the online experiment was 32.00 and 32.75, for the HMD and desktop scenario, respectively.