To Err is Human: Learning from Error Potentials in Brain-Computer Interfaces

Abstract

Several studies describe evoked EEG potentials elicited when a subject is aware of an erroneous decision either taken by him or by an external interface. This paper studies Error-related potentials (ErrP) elicited when a human user monitors an external system upon which he has no control whatsoever. In addition, the possibility of using the ErrPs as a learning signals to infer the user’s intended strategy is also addressed. Experimental results show that single-trial recognition of correct and error trials can be achieved, allowing the fast learning of the user’s strategy. These results may constitute the basis of a new kind of human-computer interaction where the former provides monitoring signals that can be used to modify the performance of the latter.