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Transfer learning and meta-learning offer some of the most promising avenuesto unlock the scalability of healthcare and consumer technologies driven bybiosignal data. This is because current methods cannot generalise well acrosshuman subjects' data and handle learning from different heterogeneouslycollected data sets, thus limiting the scale of training data. On the otherside, developments in transfer learning would benefit significantly from areal-world benchmark with immediate practical application. Therefore, we pickelectroencephalography (EEG) as an exemplar for what makes biosignal machinelearning hard. We design two transfer learning challenges around diagnosticsand Brain-Computer-Interfacing (BCI), that have to be solved in the face of lowsignal-to-noise ratios, major variability among subjects, differences in thedata recording sessions and techniques, and even between the specific BCI tasksrecorded in the dataset. Task 1 is centred on the field of medical diagnostics,addressing automatic sleep stage annotation across subjects. Task 2 is centredon Brain-Computer Interfacing (BCI), addressing motor imagery decoding acrossboth subjects and data sets. The BEETL competition with its over 30 competingteams and its 3 winning entries brought attention to the potential of deeptransfer learning and combinations of set theory and conventional machinelearning techniques to overcome the challenges. The results set a newstate-of-the-art for the real-world BEETL benchmark.

2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets