5 citations · 9 across the 5 of their papers we have counts for
5 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…
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…