Part of Advances in Neural Information Processing Systems 4 (NIPS 1991)
Rita Venturini, William Lytton, Terrence J. Sejnowski
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. We were able to average the ERP over as little as 2 minutes and still get 90% correct prediction of a vigilance measure. Additionally, we achieved similarly good performance using segments of EEG power spectrum as short as 56 sec.