Deep learning with convolutional neural networks (deep ConvNets) has revolutionized
computer vision through end‐to‐end learning, that is, learning from the raw data. There is …

Generative adversarial networks (GANs) are recently highly successful in generative
applications involving images and start being applied to time series data. Here we describe EEG-…

Abstract Machine learning (ML) methods have the potential to automate clinical EEG
analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end …

Deep generative networks trained via maximum likelihood on a natural image dataset like
CIFAR10 often assign high likelihoods to images from datasets with different objects (eg, …

Recently, there is increasing interest and research on the interpretability of machine learning
models, for example how they transform and internally represent EEG signals in Brain-…

We recorded high-density EEG in a flanker task experiment (31 subjects) and an online BCI
control paradigm (4 subjects). On these datasets, we evaluated the use of transfer learning …

As autonomous service robots become more affordable and thus available for the general
public, there is a growing need for user-friendly interfaces to control these systems. Control …

The importance of robotic assistive devices grows in our work and everyday life. Cooperative
scenarios involving both robots and humans require safe human-robot interaction. One …

While tabular classification has traditionally relied on from-scratch training, a recent
breakthrough called prior-data fitted networks (PFNs) challenges this approach. Similar to large …

Automated clinical EEG analysis using machine learning (ML) methods is a growing EEG
research area. Previous studies on binary EEG pathology decoding have mainly used the …