EEG brain-computer interfaces (BCI) require a calibration phase prior to the on-line control of the device, which is a difficulty for the practical development of this technology as it is user-, session- and task-specific. The large body of research in BCIs based on event-related potentials (ERP) use temporal features, which have demonstrated to be stable for each user along time, but do not generalize well among tasks different from the calibration task. This paper explores the use of low frequency features to improve the generalization capabilities of the BCIs using error-potentials. The results show that there exists a stable pattern in the frequency domain that allows a classifier to generalize among the tasks. Furthermore, the study also shows that it is possible to combine temporal and frequency features to obtain the best of both domains.