High stimuli virtual reality training for a brain controlled robotic wheelchair

Abstract

Smart robotic wheelchairs, as well as other assistive robotic devices, can provide an effective form of independent mobility for those who suffer with motor disabilities. Although many control interfaces exist to operate these devices, brain computer interfaces (BCI) offer a control modality for those who have little to no motor function, as well as being able to re-associate movement with brain functionality. Although BCIs have been designed for robotic wheelchairs, more research and development is required before they can be adopted for use in the ‘real world’. One key challenge on that journey is the user training required to achieve an acceptable accuracy of the control. In this paper, we aim to identify the best training method by comparing users trained on a simple task, in a simulated environment on a 2D display (VR-2DD) and in a virtual environment using a virtual reality headset (VR-HMD). We trained 15 participants in mix of high and low noise virtual environments or on a simple training task, and found a significant improvement in the classification accuracies of the participants who trained using the VR-2DD task compared with those who were trained with the simple task. We also carried out active (online) tests across all participants in the same virtual training environment, with a varying level of external stimuli, and found a significant improvement in the performance of participants in both VR groups compared to participants in the simple task group.