3 citations · 4 across the 3 of their papers we have counts for
3 papers
TS-MoCo: Time-Series Momentum Contrast for Self-Supervised Physiological Representation Learning
Philipp Hallgarten, David Bethge, Ozan Özdenizci +2
Limited availability of labeled physiological data often prohibits the use of powerful supervised deep learning models in the biomedical machine intelligence domain. We approach th…
EEG2Vec: Learning Affective EEG Representations via Variational Autoencoders
David Bethge, Philipp Hallgarten, Tobias Grosse-Puppendahl +4
There is a growing need for sparse representational formats of human affective states that can be utilized in scenarios with limited computational memory resources. We explore whet…
Domain-Invariant Representation Learning from EEG with Private Encoders
David Bethge, Philipp Hallgarten, Tobias Grosse-Puppendahl +4
Deep learning based electroencephalography (EEG) signal processing methods are known to suffer from poor test-time generalization due to the changes in data distribution. This beco…