  We present a simple deep learning-based framework commonly used in computer
vision and demonstrate its effectiveness for cross-dataset transfer learning in
mental imagery decoding tasks that are common in the field of Brain-Computer
Interfaces (BCI). We investigate, on a large selection of 12 motor-imagery
datasets, which ones are well suited for transfer, both as donors and as
receivers. Challenges. Deep learning models typically require long training
times and are data-hungry, which impedes their use for BCI systems that have to
minimize the recording time for (training) examples and are subject to
constraints induced by experiments involving human subjects. A solution to both
issues is transfer learning, but it comes with its own challenge, i.e.,
substantial data distribution shifts between datasets, subjects and even
between subsequent sessions of the same subject. Approach. For every pair of
pre-training (donor) and test (receiver) dataset, we first train a model on the
donor before training merely an additional new linear classification layer
based on a few receiver trials. Performance of this transfer approach is then
tested on other trials of the receiver dataset. Significance. First, we lower
the threshold to use transfer learning between motor imagery datasets: the
overall framework is extremely simple and nevertheless obtains decent
classification scores. Second, we demonstrate that deep learning models are a
good option for motor imagery cross-dataset transfer both for the reasons
outlined in the first point and because the framework presented is viable in
online scenarios. Finally, analysing which datasets are best suited for
transfer learning can be used as a reference for future researchers to
determine which to use for pre-training or benchmarking.
