Estimation of Human Signal Detection Performance from Event-related Potentials Using Feed-forward Neural Network Model

We compared linear and neural network models for estimating human signal detection performance from event-related potentials (ERP) elicited by task-relevant stimuli. Data consisted of ERPs and performance measures from ve trained operators who monitored a radar display and detected and classiied visual symbols at three contrast levels. The performance measure (PF1) was a composite of accuracy, speed, and conndence of classiication responses. The ERPs, which were elicited by the symbols, were represented in the interval 0-1500 ms post-stimulus at three midline electrodes (Fz, Cz, Pz) using either principal components (PCA) or coeecients of autoregressive (AR) models. We constructed individual models of PF1 from both PCA and AR representations using either linear regression or radial basis function (RBF) networks. Applying the normalized mean square error of approximation as a criterion, we found that the PCA representation was superior to AR and that RBF networks estimated PF1 much more accurately than linear regression. This suggests that nonlinear methods combined with suitable ERP feature extraction can provide more accurate and reliable estimates of display-monitoring performance than linear models.