EEG-Based Error Detection Can Challenge Human Reaction Time in a VR Navigation Task

Abstract

Error perception is known to elicit distinct brain patterns, which can be used to improve the usability of systems facilitating human-computer interactions, such as brain-computer interfaces. This requires a high-accuracy detection of erroneous events, e.g., misinterpretations of the user's intention by the interface, to allow for suitable reactions of the system. In this work, we concentrate on steering-based navigation tasks. We present a combined electroencephalography-virtual reality (VR) study investigating different approaches for error detection and simultaneously exploring the corrective human behavior to erroneous events in a VR flight simulation. We could classify different errors allowing us to analyze neural signatures of unexpected changes in the VR. Moreover, the presented models could detect errors faster than participants naturally responded to them. This work could contribute to developing adaptive VR applications that exclusively rely on the user's physiological information.