A new strategy is introduced to exploit the enhanced spatial resolution offered by dense electrode arrays and to solve the dimensionality problem that plagues the design and evaluation of practical dense array event-related potential (ERP) classifiers. A spatio-temporal model is introduced to observe the dense array ERP amplitude variations across channels and time, simultaneously. Dimensionality reduction is achieved by selecting elements of the spatio-temporal arrays which differ in their probability distributions across the brain activity classes. Each selected spatio-temporal element is classified using an univariate Gaussian classifier and the resulting decisions are fused into a decision fusion vector which is classified using a discrete Bayes vector classifier. Using ERPs from a Stroop color test, it is shown that the performance improves significantly when the strategy is applied to normalized spatio-temporal ERP arrays. The main advantage of the new strategy is that it is not constrained by the dimensionality of the ERP vector. Consequently, it can be used to design ERP classifiers specialized for individual test subjects without having to collect a large number of ERPs from groups of subjects in order to solve the dimensionality problem.