Covert attention as a paradigm for subject-independent brain-computer interfacing

Abstract

Covert attention has been introduced as a paradigm for gaze-independent brain computer interfacing (BCI). As we know that the applicability of a BCI system depends on the consistency of its paradigm over a group of subjects, one important feature of a BCI is its subject-independence. To generalize a BCI to be applicable for new users, one can think of either to use a machine learning algorithm above a standard BCI to improve the generalization or to choose a more appropriate paradigm. Various machine learning algorithms have been applied on imaginary movement based paradigms towards having a subject-independent BCI, yet there are a few efforts on testing other paradigms in this context. In this paper we show that covert attention can be used as a robust paradigm for subject-independent brain computer interfacing.