Detection of error-related potentials during human-vehicle interaction

Abstract

Brain-computer interfaces (BCIs) rely on accurate classification of a user’s intent in order to perform correct actions. However, when used in reality, devices controlled by BCIs may often react differently from what the user intended due to noise and other factors resulting in misclassification. In such cases where an erroneous response of the device are observed by the user, error-related potentials (ErrPs) may be evoked and can be captured from the user’s neural signals. Researches on ErrPs have been conducted in recent years to recognize and correct erroneous responses elicited from the devices. In this research, we have created a graphical application in which the user drives a virtual car from a first-person perspective. Results of our experiments show that ErrPs can be captured from the user when the car moves differently from how the user intended to drive.