5 citations · 9 across the 6 of their papers we have counts for
6 papers
EEG-D3: A Solution to the Hidden Overfitting Problem of Deep Learning Models
Siegfried Ludwig, Stylianos Bakas, Konstantinos Barmpas +5
Deep learning for decoding EEG signals has gained traction, with many claims to state-of-the-art accuracy. However, despite the convincing benchmark performance, successful transla…
Stochastic Resonance Improves the Detection of Low Contrast Images in Deep Learning Models
Siegfried Ludwig
Stochastic resonance describes the utility of noise in improving the detectability of weak signals in certain types of systems. It has been observed widely in natural and engineere…
Latent Alignment with Deep Set EEG Decoders
Stylianos Bakas, Siegfried Ludwig, Dimitrios A. Adamos +3
The variability in EEG signals between different individuals poses a significant challenge when implementing brain-computer interfaces (BCI). Commonly proposed solutions to this pr…
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets
Xiaoxi Wei, A. Aldo Faisal, Moritz Grosse-Wentrup +18
Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is bec…
Team Cogitat at NeurIPS 2021: Benchmarks for EEG Transfer Learning Competition
Stylianos Bakas, Siegfried Ludwig, Konstantinos Barmpas +5
Building subject-independent deep learning models for EEG decoding faces the challenge of strong covariate-shift across different datasets, subjects and recording sessions. Our app…
EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters
Siegfried Ludwig, Stylianos Bakas, Dimitrios A. Adamos +3
Patterns of brain activity are associated with different brain processes and can be used to identify different brain states and make behavioral predictions. However, the relevant f…