Abstract
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 five trained operators who
monitored a radar display and detected and classified visual symbols
at three contrast levels. The performance measure (PF1) was a
composite of accuracy, speed, and confidence of classification
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 component analysis
(PCA) factors or coefficients 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.
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