Innovative Application of VR Technology for Brain Activity Measurement
A novel development has emerged from The University of Texas at Austin, where a research team has engineered a noninvasive electroencephalogram (EEG) sensor seamlessly integrated into a Meta VR headset. This pioneering technology enables the measurement of brain electrical activity during immersive virtual reality (VR) interactions.
The potential applications of this device are far-reaching, encompassing domains such as anxiety management, cognitive assessment of aviators utilizing flight simulators, and even offering individuals the unique opportunity to perceive the world from a robot’s perspective.
According to Nanshu Lu, a distinguished professor in the Cockrell School of Engineering’s Department of Aerospace Engineering and Engineering Mechanics, VR significantly enhances user immersion compared to traditional displays. The EEG sensor augments this experience by providing more precise measurements of the brain’s responses to the virtual environment.
This landmark research has been published in the esteemed journal “Soft Science.”
Although the fusion of VR and EEG sensors has been previously explored in commercial contexts, the current market offerings tend to be cost-prohibitive. In contrast, the researchers assert that their EEG electrodes prioritize user comfort, thereby extending the duration of wear and expanding the array of potential applications.
Typically, leading EEG devices employ caps adorned with electrodes. However, this design does not harmonize well with VR headsets, and individual electrodes often struggle to establish robust connections due to interference from hair. Conventional electrodes, often rigid and comb-shaped, necessitate insertion through hair to reach the skin, resulting in discomfort for users.
As Hongbian Li, a research associate in Lu’s lab, points out, prevailing options all possess significant limitations that this novel system seeks to overcome.
The research team engineered a solution in the form of a soft, conductive spongy electrode, meticulously developed by Li. These modified VR headsets integrate electrodes along the top strap and forehead pad, featuring a flexible circuit with conductive traces similar to electronic tattoos developed by Lu. An EEG recording device is affixed to the rear of the headset.
Remarkably, this innovation aligns seamlessly with another major research endeavor at UT Austin: an expansive robot delivery network, doubling as a comprehensive study on human-robot interactions. Lu’s involvement in this initiative is pivotal, as the VR headsets serve participants traveling alongside robots or stationed in remote observation posts. This technology offers participants the ability to perceive through the visual perspective of the robot while concurrently gauging the cognitive strain imposed by extended observation.
Luis Sentis, a professor in the Department of Aerospace Engineering and Engineering Mechanics and a co-leader of the robot delivery project, elucidates the significance: the capacity to view the world from a robot’s vantage point provides invaluable insights into human reactions, subsequently enhancing safety protocols.
In a practical demonstration of the VR EEG headset’s viability, researchers collaborated with José del R. Millán, a distinguished faculty member skilled in brain-machine interfaces. Together, they devised a driving simulation wherein participants responded to turn commands through button presses. The EEG data acquired during this exercise correlated with attention levels, exemplifying the headset’s efficacy in measuring cognitive engagement.
The research team has commenced preliminary patent procedures for their EEG technology and expresses openness to collaboration with VR enterprises to integrate their innovation directly into VR headsets.
Contributing members of this groundbreaking research include academics from diverse disciplines, such as Hyonyoung Shin, Minsu Zhang, Nicholas Riveira, and Susmita Gangopadahyay from the Chandra Family Department of Electrical and Computer Engineering, and Andrew Yu, Heeyong Huh, Zhengjie Li, and Yifan Rao from the Department of Aerospace Engineering and Engineering Mechanics, among others.
