Towards Utilization of Error-Related Potentials for Brain-to-Vehicle Communication

Abstract

Brain-computer interfaces (BCIs) rely on accurate classification of a user’s intent in order to perform the 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, error-related potentials (ErrPs) may be evoked and can be captured from the user’s neural signals. Detection of these ErrPs can then be used to recognize and correct erroneous responses. 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.