234 citations · 628 across the 22 of their papers we have counts for
6 papers · 1 filter
A large-scale evaluation framework for EEG deep learning architectures
Felix A. Heilmeyer, Robin T. Schirrmeister, Lukas D. J. Fiederer +3
EEG is the most common signal source for noninvasive BCI applications. For such applications, the EEG signal needs to be decoded and translated into appropriate actions. A recently…
Cross-paradigm pretraining of convolutional networks improves intracranial EEG decoding
Joos Behncke, Robin Tibor Schirrmeister, Martin Völker +5
When it comes to the classification of brain signals in real-life applications, the training and the prediction data are often described by different distributions. Furthermore, di…
The role of robot design in decoding error-related information from EEG signals of a human observer
Joos Behncke, Robin Tibor Schirrmeister, Wolfram Burgard +1
For utilization of robotic assistive devices in everyday life, means for detection and processing of erroneous robot actions are a focal aspect in the development of collaborative…
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals
Kay Gregor Hartmann, Robin Tibor Schirrmeister, Tonio Ball
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-…
Training Generative Reversible Networks
Robin Tibor Schirrmeister, Patryk Chrabąszcz, Frank Hutter +1
Generative models with an encoding component such as autoencoders currently receive great interest. However, training of autoencoders is typically complicated by the need to train…
Intracranial Error Detection via Deep Learning
Martin Völker, Jiří Hammer, Robin T. Schirrmeister +6
Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in noninvasive electroencephalo…