- Analysis of Event Related Potentials (ERPs) produced by brain activities can provide insight into the timing of underlying brain function. ERPs can be classified by their time/frequency characteristics and spatial location on the scalp. Traditionally, ERPs are manually located by temporally and spatially averaged EEG signals. This process is error prone and sensitive to a priori assumptions. Our proposed algorithm is a general neuroscience-focused data mining algorithm that performs time and frequency analysis on ERPs and automatically extracts templates corresponding to Spectral Spatio-Temporal (SST) regions exhibiting significant differences between experimental outcomes. The method uses time-aligned templates, which preserve the characteristics of the signal important to cognitive researchers. The ability of the selected signal templates to differentiate between stimulus responses has been verified using a pattern recognition procedure. SST template extraction is tested on data taken from a Go/NoGo task and shown to both find relationships consistent with published neuroscience literature as well as novel relationships.