Neural Network Analysis of Event Related Potentials and Electroencephalogram Predicts Vigilance

Automated monitoring of vigilance in attention intensive tasks such as air traffic control or sonar operation is highly desirable. As the opera(cid:173) tor monitors the instrument, the instrument would monitor the operator, insuring against lapses. We have taken a first step toward this goal by us(cid:173) ing feedforward neural networks trained with backpropagation to interpret event related potentials (ERPs) and electroencephalogram (EEG) associ(cid:173) ated with periods of high and low vigilance. The accuracy of our system on an ERP data set averaged over 28 minutes was 96%, better than the 83% accuracy obtained using linear discriminant analysis. Practical vigilance monitoring will require prediction over shorter time periods.