Most Brain-Computer Interfaces (BCI) are based on machine learning and behave like black boxes, i.e., they cannot be interpreted. However, designing interpretable BCI would enable to discuss, verify or improve what the BCI has automatically learnt from brain signals, or possibly gain new insights about the brain. In this paper, we present an algorithm to design a fully interpretable BCI. It can explain what power in which brain regions and frequency bands corresponds to which mental state, using "if-then" rules expressed with simple words. Evaluations showed that this algorithm led to a truly interpretable BCI as the automatically derived rules were consistent with the literature. They also showed that we can actually verify and correct what an interpretable BCI has learnt so as to further improve it.