The Benchmarks for EEG Transfer Learning (BEETL) is a competition that aims to stimulate the development of transfer and meta-learning algorithms applied to a prime example of what makes the use of biosignal data hard, EEG data. BEETL acts as a much-needed benchmark for domain adaptation algorithms in EEG decoding and provides a real-world stimulus goal for transfer learning and meta-learning developments for both academia and industry. Given the multitude of different EEG-based algorithms that exist, we offer two specific challenges: Task 1 is a cross-subject sleep stage decoding challenge reflecting the need for transfer learning in clinical diagnostics, and Task 2 is a cross-dataset motor imagery decoding challenge reflecting the need for transfer learning in human interfacing.