The recent advances of Brain Computer Interfaces (BCI) systems, can provide effective assistance for real time prognosis systems for patients who suffered from epileptic seizures. This paper presents an EEG classification strategy for short-term epilepsy prognosis, using software for Brain-Computer Interface (BCI) systems. A training scenario is presented, where significant features are extracted and a classification algorithm is trained. The training procedure extracts knowledge in terms of a classification model, which is employed in a real-time testing. For the training of the classification scenario a five-classes dataset of EEG signals is employed in which two-classes have been recorded extracranially and the rest three intracranially including one class with epileptic seizure activity and two classes with seizure-free signals. Promising quantitative results are reported.