Classifying brain activities is a challenging task since Electroencephalography (EEG) recordings exhibit distinct and individualized spatial and temporal characteristics correlated with noise and various physical and mental activities. To increase classification accuracy, it is thus crucial to identify discriminant spatio-temporal features. This paper presents a method for analyzing the spatio-temporal characteristics associated with Event related Potentials (ERPs). First, a resampling procedure based on Global Field Power (GFP) extracts temporal features. Second, a spatially weighted SVM (sw-SVM) is used to learn a spatial filter optimizing the classification performance for each temporal feature. Third, the so-obtained ensemble of sw-SVM classifiers are combined using a weighted combination of all sw-SVM outputs. Results indicate that the inclusion of temporal features provides useful insight regarding classification performance and physiological understanding.