Spatial filtering is a widely used dimension reduction method in electroencephalogram based brain-computer interface systems. In this paper a new algorithm is proposed, which learns spatial filters from a training dataset. In contrast to existing approaches the proposed method yields spatial filters that are explicitly designed for the classification of event-related potentials, such as the P300 or movement-related potentials. The algorithm is tested, in combination with support vector machines, on several benchmark datasets from past BCI competitions and achieves state of the art results.