Navigating a smart wheelchair with a brain-computer interface interpreting steady-state visual evoked potentials

Abstract

In order to allow severely disabled people who cannot move their arms and legs to steer an automated wheelchair, this work proposes the combination of a non-invasive EEG-based human-robot interface and an autonomous navigation system that safely executes the issued commands. The robust classification of steady-state visual evoked potentials in brain activity allows for the seamless projection of qualitative directional navigation commands onto a frequently updated route graph representation of the environment. The deduced metrical target locations are navigated to by the application of an extended version of the well-established nearness diagram navigation method. The applicability of the system proposed is demonstrated by a real-world pilot study in which eight out of nine untrained subjects successfully navigated an automated wheelchair, requiring only some ten minutes of preparation.