Evaluation of an adaptive automation system using three EEG indices with a visual tracking task

Abstract

A system was evaluated for use in adaptive automation using two experiments with electroencephalogram (EEG) indices based on the beta, alpha, and theta bandwidths. Subjects performed a compensatory tracking task while their EEG was recorded and converted to one of three engagement indices: β/(α+Θ), β/α, or 1/α. In experiment one, the tracking task was switched between manual and automatic modes depending on whether the subject’s engagement index was increasing or decreasing under a positive or negative feedback condition. Subjects were run for three consecutive 16-min trials. In experiment two, the task was switched depending on whether the absolute level of the engagement index for the subject was above or below baseline levels. It was hypothesized that negative feedback would produce more switches between manual and automatic modes, and that the β/(α+Θ) index would be most effective. The results confirmed these hypotheses. Tracking performance was better under negative feedback in both experiments; also, the use of absolute levels of engagement in experiment two resulted in better performance. There were no systematic changes in these effects over three 16-min trials. The implications for the use of such systems for adaptive automation are discussed.