  We introduce here the idea of Meta-Learning for training EEG BCI decoders.
Meta-Learning is a way of training machine learning systems so they learn to
learn. We apply here meta-learning to a simple Deep Learning BCI architecture
and compare it to transfer learning on the same architecture. Our Meta-learning
strategy operates by finding optimal parameters for the BCI decoder so that it
can quickly generalise between different users and recording sessions --
thereby also generalising to new users or new sessions quickly. We tested our
algorithm on the Physionet EEG motor imagery dataset. Our approach increased
motor imagery classification accuracy between 60% to 80%, outperforming other
algorithms under the little-data condition. We believe that establishing the
meta-learning or learning-to-learn approach will help neural engineering and
human interfacing with the challenges of quickly setting up decoders of neural
signals to make them more suitable for daily-life.
