  In the context of Brain-Computer Interfaces, we propose an adaptive method
that reaches offline performance level while being usable online without
requiring supervision. Interestingly, our method does not require retraining
the model, as it consists in using a frozen efficient deep learning backbone
while continuously realigning data, both at input and latent spaces, based on
streaming observations. We demonstrate its efficiency for Motor Imagery brain
decoding from electroencephalography data, considering challenging
cross-subject scenarios. For reproducibility, we share the code of our
experiments.
