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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.
Author Information
Xiaoxi Wei (Imperial College London)
Vinay Jayaram (Facebook Reality Labs)
Sylvain Chevallier (LISV)
Giulia Luise (University College London)
Camille Jeunet (CNRS)
Moritz Grosse-Wentrup (University of Vienna)
Alexandre Gramfort (INRIA)
Aldo A Faisal (Imperial College London)
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