1 citations · 1 across the 1 of their papers we have counts for
3 papers
Uncovering the structure of clinical EEG signals with self-supervised learning
Hubert Banville, Omar Chehab, Aapo Hyvärinen +2
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,…
Self-supervised representation learning from electroencephalography signals
Hubert Banville, Isabela Albuquerque, Aapo Hyvärinen +3
The supervised learning paradigm is limited by the cost - and sometimes the impracticality - of data collection and labeling in multiple domains. Self-supervised learning, a paradi…
Manifold-regression to predict from MEG/EEG brain signals without source modeling
David Sabbagh, Pierre Ablin, Gael Varoquaux +2
Magnetoencephalography and electroencephalography (M/EEG) can reveal neuronal dynamics non-invasively in real-time and are therefore appreciated methods in medicine and neuroscienc…