P300 as a Measure of Workload in Multi-Task Environments

A biocybernetic system was validated for determining the quality of psychophysiological measures for use in the design of adaptive automation technology. Eighteen participants were asked to perform a compensatory tracking task while their EEG was continuously sampled from four cortical sites (Pz, Cz, P3, P4). Three engagement indices ( I / a , 20 Pla+B, 10 Pla) were tested. The system switched between manual and automatic task modes according to the level of participant engagement. The degree of engagement was based upon the absolute value of the EEG engagement index compared to baseline EEG data. The results of the study were that the index 20 P/a+€l was found to best determine optimal automatiodmanual task allocation mixes in terms of system, behavioral, and physiological correlates. These data provide preliminary indication for the potential of psychophysiological measures for use in adaptive automation.