3 citations · 6 across the 4 of their papers we have counts for
4 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…
Exploiting Multiple EEG Data Domains with Adversarial Learning
David Bethge, Philipp Hallgarten, Ozan Özdenizci +3
Electroencephalography (EEG) is shown to be a valuable data source for evaluating subjects' mental states. However, the interpretation of multi-modal EEG signals is challenging, as…
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…