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

A system was evaluated for use in adaptive automation using two experiments with electroencephalogram (EEG) indices based on the beta, alpha, and thet a bandwidths. Subjects performed a compensatory tracking task while their E EG was recorded and converted to one of three engagement indices: beta/(alp ha + Theta), beta/alpha, or 1/alpha. In experiment one, the tracking task w as switched between manual and automatic modes depending on whether the sub ject's engagement index was increasing or decreasing under a positive or ne gative feedback condition. Subjects were run for three consecutive 16-min t rials. In experiment two, the task was switched depending on whether the ab solute level of the engagement index for the subject was above or below bas eline levels. It was hypothesized that negative feedback would produce more switches between manual and automatic modes, and that the beta/(alpha + Th eta) index would be most effective. The results confirmed these hypotheses. Tracking performance was better under negative feedback in both experiment s; also, the use of absolute levels of engagement in experiment two resulte d 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. (C) 1999 Elsevier Science B.V. All right s reserved.