Uncovering the structure of clinical EEG signals with self-supervised learning

@article{Banville2020UncoveringTS,
  title={Uncovering the structure of clinical EEG signals with self-supervised learning},
  author={Hubert J. Banville and Omar Chehab and Aapo Hyv{\"a}rinen and Denis Alexander Engemann and Alexandre Gramfort},
  journal={Journal of Neural Engineering},
  year={2020},
  volume={18}
}
Objective. Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG), where labeling can be costly in terms of specialized expertise and human processing time. Consequently, deep learning architectures designed to learn on EEG data have yielded relatively shallow models and performances at best similar to those of traditional feature-based… 

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