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Stroke is a neurological syndrome that may affect upper and lower limbs functions of post-stroke survivors. Brain-Computer Interfaces (BCIs) are becoming as a promising alter-native to help post-stroke patients rehabilitation, although there are very few associated studies and systems being applied in clinical environment. As a novelty, developing a motor imagery (MI) BCI based on pedal end-effector for motor rehabilitation, we propose to combine pedaling MI and passive pedaling into a Calibration phase. As a result, users would activate continuously their central and peripheral mechanisms linked to lower-limbs throughout BCI intervention. We hypothesize that this strategy enables to obtain a better classification model for our BCI by selecting those feature vectors corresponding to pedaling MI closer to real movements. Therefore, it is expected to have a more effective BCI intervention. Preliminary results show …