  BCI Motor Imagery datasets usually are small and have different electrodes
setups. When training a Deep Neural Network, one may want to capitalize on all
these datasets to increase the amount of data available and hence obtain good
generalization results. To this end, we introduce a spatial graph signal
interpolation technique, that allows to interpolate efficiently multiple
electrodes. We conduct a set of experiments with five BCI Motor Imagery
datasets comparing the proposed interpolation with spherical splines
interpolation. We believe that this work provides novel ideas on how to
leverage graphs to interpolate electrodes and on how to homogenize multiple
datasets.
