We couple the standardized low-resolution electromagnetic tomography (sLORETA), an inverse solution for electroencephalography (EEG) and the common spatial pattern, which is here conceived as a data-driven beamformer, to classify the benchmark BCI (Brain Computer Interface) competition 2003, data set IV. The data-set is from an experiment where a subject performed a selfpaced left and right finger tapping task. Available for analysis are 314 training trials whereas 100 unlabeled test trials have to be classified. The EEG data from 28 electrodes comprise the recording of the 500 ms before the actual finger movements, hence represents uniquely the left and right finger movement intention. Despite our use of an untrained classifier, and we extract only one attribute per class, our method yields accuracy similar to the winners of the competition for this data-set. The distinct advantages of the approach presented here are the use of an untrained classifier and the processing speed, which make the method suitable for actual BCI applications. The proposed method is favourable over existing classification methods based on EEG inverse solution, which either rely on iterative algorithms for single-trial independent component analysis or on trained classifiers.