Towards an Effective Motor Imagery Based-BCI with Calibration Through Activation of Central and Peripheral Mechanisms of Lower-Limbs

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 that the proposed method may increase the BCI performance. For almost all participants was noted, during MI tasks, a power decreasing over the foot area (Cz location), corresponding mainly to beta frequency bands, specifically for both low (13 to 22 Hz) and high (23 to 30 Hz) beta bands.