Detecting System Errors in Virtual Reality Using EEG Through Error-Related Potentials

When persons interact with the environment and experience or wit-ness an error (e.g. an unexpected event), a specific brain pattern,known as error-related potential (ErrP) can be observed in the elec-troencephalographic signals (EEG). Virtual Reality (VR) technologyenables users to interact with computer-generated simulated envi-ronments and to provide multi-modal sensory feedback. Using VRsystems can, however, be error-prone. In this paper, we investigatethe presence of ErrPs when Virtual Reality users face 3 types ofvisualization errors: (Te) tracking errors when manipulating virtualobjects, (Fe) feedback errors, and (Be) background anomalies. Weconducted an experiment in which 15 participants were exposed tothe 3 types of errors while performing a center-out pick and placetask in virtual reality. The results showed that tracking errors gener-ate error-related potentials, the other types of errors did not generatesuch discernible patterns. In addition, we show that it is possible todetect the ErrPs generated by tracking losses in single trial, with anaccuracy of 85%. This constitutes a first step towards the automaticdetection of error-related potentials in VR applications, paving theway to the design of adaptive and self-corrective VR/AR applicationsby exploiting information directly from the user’s brain.