  Brain Computer Interface (BCI) technologies have the potential to improve the
lives of millions of people around the world, whether through assistive
technologies or clinical diagnostic tools. Despite advancements in the field,
however, at present consumer and clinical viability remains low. A key reason
for this is that many of the existing BCI deployments require substantial data
collection per end-user, which can be cumbersome, tedious, and error-prone to
collect. We address this challenge via a deep learning model, which, when
trained across sufficient data from multiple subjects, offers reasonable
performance out-of-the-box, and can be customized to novel subjects via a
transfer learning process. We demonstrate the fundamental viability of our
approach by repurposing an older but well-curated electroencephalography (EEG)
dataset and benchmarking against several common approaches/techniques. We then
partition this dataset into a transfer learning benchmark and demonstrate that
our approach significantly reduces data collection burden per-subject. This
suggests that our model and methodology may yield improvements to BCI
technologies and enhance their consumer/clinical viability.
