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The NeurIPS 2021 BEETL Competition: Benchmarks for EEG Transfer Learning + Q&A
Xiaoxi Wei · Vinay Jayaram · Sylvain Chevallier · Giulia Luise · Camille Jeunet · Moritz Grosse-Wentrup · Alexandre Gramfort · Aldo A Faisal

Fri Dec 10 02:45 AM -- 03:05 AM (PST) @
in Competition Track Day 4: Overviews + Breakout Sessions »
Event URL: https://beetl.ai/introduction »

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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