  Transfer learning and meta-learning offer some of the most promising avenues
to unlock the scalability of healthcare and consumer technologies driven by
biosignal data. This is because current methods cannot generalise well across
human subjects' data and handle learning from different heterogeneously
collected data sets, thus limiting the scale of training data. On the other
side, developments in transfer learning would benefit significantly from a
real-world benchmark with immediate practical application. Therefore, we pick
electroencephalography (EEG) as an exemplar for what makes biosignal machine
learning hard. We design two transfer learning challenges around diagnostics
and Brain-Computer-Interfacing (BCI), that have to be solved in the face of low
signal-to-noise ratios, major variability among subjects, differences in the
data recording sessions and techniques, and even between the specific BCI tasks
recorded in the dataset. Task 1 is centred on the field of medical diagnostics,
addressing automatic sleep stage annotation across subjects. Task 2 is centred
on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across
both subjects and data sets. The BEETL competition with its over 30 competing
teams and its 3 winning entries brought attention to the potential of deep
transfer learning and combinations of set theory and conventional machine
learning techniques to overcome the challenges. The results set a new
state-of-the-art for the real-world BEETL benchmark.
