Traditional analysis methods for single-trial classification of electro-encephalography (EEG) focus on two types of paradigms: phase locked methods, in which the amplitude of the signal is used as the feature for classification, i.e. event related potentials; and second order methods, in which the feature of interest is the power of the signal, i.e event related (de)synchronization. The process of deciding which paradigm to use is ad hoc and is driven by knowledge of neurological findings. Here we propose a unified method in which the algorithm learns the best first and second order spatial and temporal features for classification of EEG based on a bilinear model. The efficiency of the method is demonstrated in simulated and real EEG from a benchmark data set for Brain Computer Interface.