Effective and usable closed-loop attention management systems are on the cusp of reality. The purpose of a closedloop attention management system is to monitor an operator’s attention via psychophysiological indicators, predict episodes of low vigilance and lapses of attention, and then modify the system interface to maintain optimal levels of performance. There are many requirements for bringing this concept to fruition, including minimal or no contact psychophysiological measures that are minimally invasive or constraining, accurate and precise prediction of attention level and task performance, and effective interface modifications. In the present study, we report a novel combination of no contact head and eye measures combined with a wireless EEG measure to predict performance on a sustained vigilance task. Each measure was computed for 10 five minute blocks over the course of the experiment. The four most predictive variables were eye opening (vertical distance between eyelids), head pitch variability (amount of nodding), high vigilance (a measure derived from a spectral analysis of the EEG signal), and the sum of the high and low vigilance indices (Berka et al., 2004). Together, the measures predicted 42 % of the variance in the miss rate (39 % of the variance in A’). No individual variable accounted for more than 13 % of the variance. Separate multiple regressions using the same four variables were computed for each participant. The percentage of variance in the miss rate accounted for varied from 32 % to 91%. These findings suggest that these minimally invasive measures, used in combination, may be sensitive enough and acceptable enough for use i
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