234 citations · 628 across the 22 of their papers we have counts for
25 papers
Deep Riemannian Networks for End-to-End EEG Decoding
Daniel Wilson, Robin Tibor Schirrmeister, Lukas Alexander Wilhelm Gemein +1
State-of-the-art performance in electroencephalography (EEG) decoding tasks is currently often achieved with either Deep-Learning (DL) or Riemannian-Geometry-based decoders (RBDs).…
A Framework for Preserving Privacy and Cybersecurity in Brain-Computer Interfacing Applications
Maryna Kapitonova, Philipp Kellmeyer, Simon Vogt +1
Brain-Computer Interfaces (BCIs) comprise a rapidly evolving field of technology with the potential of far-reaching impact in domains ranging from medical over industrial to artist…
When less is more: Simplifying inputs aids neural network understanding
Robin Tibor Schirrmeister, Rosanne Liu, Sara Hooker +1
How do neural network image classifiers respond to simpler and simpler inputs? And what do such responses reveal about the learning process? To answer these questions, we need a cl…
The spatial scale dimension of speech processing in the human brain
Philipp Kellmeyer, Roland Berkemeier, Tonio Ball
In the past three decades, neuroimaging has provided important insights into structure-function relationships in the human brain. Recently, however, the methods for analyzing funct…
Towards a Governance Framework for Brain Data
Marcello Ienca, Joseph J. Fins, Ralf J. Jox +17
The increasing availability of brain data within and outside the biomedical field, combined with the application of artificial intelligence (AI) to brain data analysis, poses a cha…
Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features
Robin Tibor Schirrmeister, Yuxuan Zhou, Tonio Ball +1
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 (e.g.,…