Strand 4: NeuroVR – Modeling Brain response to Virtual Reality and its application for neurorehabilitation

Researchers: Ian Mills, Dr. Michael Barros, Dr. Sasitharan Balasubramaniam

Virtual reality (VR) is the immersion of a person into a simulated virtual environment using either a head mounted display or a projection based system. It allows for the person to experience and interact, using either peripherals or tracked body parts, with the simulated virtual environment.

TBI will investigate the impact of VR on the brain, and how it interprets virtual environments. The research is aiming to understand how the brain functional network, which is modeled using graph theory, can be used to understand the emotions of VR users. The brain shares a number of key topological properties with modern networks. As such we can study the activity of the brain and carry out analysis using graph theory. By linking both VR and the brain, we can study the network patterns of a functional brain network and identify common topological properties associated with correct brain function (Small worldness, Modularity, Fat Tailed degree distribution). This in turn could lead to new forms of virtual-neurorehabilitation, which could be applied to patients suffereing from Alzheimers, schizophrenia, depression and Parkinsons. Several of these neurological disorders and condition affect the brain network properties and exhibit different patterns and properties. Therefore, through virtual-neurorehabilitation, we may be able to correct the network properties as patients are immersed into virtual environments.

VR Headset

Research Objectives:

  • Composing the brain functional networks based on virtual reality stimuli and determining the core network topological structures and properties, and linking this to different emotional states based on the fluctuations in brain signal activity.


  • Defining the predictive brain functional network state using learning algorithms. The aim is to predict the next possible neural state for a defined segment of VR content.
  • VR adaptive content derived from emotional/predictive network state models.

This research will allow for VR adaptive content to be applied not only to entertainment industry for Brain Machine Interface inputs and dynamically changing content, but also to medical studies for monitoring and providing neurofeedback for neurological disorders.