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

Understand

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.