A brain-machine interface to navigate mobile robots along human-like paths amidst obstacles

Abstract

This paper presents an interface that allows a human user to specify a desired path for a mobile robot in a planar workspace with noisy binary inputs that are obtained at low bit-rates through an electroencephalograph (EEG). We represent desired paths as geodesics with respect to a cost function that is defined so that each path-homotopy class contains exactly one (local) geodesic. We apply max-margin structured learning to recover a cost function that is consistent with observations of human walking paths. We derive an optimal feedback communication protocol to select a local geodesicequivalently, a path-homotopy class-using a sequence of noisy bits. We validate our approach with experiments that quantify both how well our learned cost function characterizes human walking data and how well human subjects perform with the resulting interface in navigating a simulated robot with EEG.

The performance of 9–11-year-old children using an SSVEP-based BCI for target selection

“…For both the calibration phase and the experimental phase the targets were three white circles (5.1 cm in diameter) flashing between white and black at 6.2 Hz, 7.7 Hz, and 10 Hz. We chose these frequencies based on previous experience [34] and because they are less likely to elicit photoinduced seizures. The second of these two reasons is especially important when working with younger individuals, who are often unaware that they are photosensitive [31].…”