33 citations · 59 across the 5 of their papers we have counts for
6 papers
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…
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…
The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks
Joos Behncke, Robin Tibor Schirrmeister, Wolfram Burgard +1
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 im…
Brain Responses During Robot-Error Observation
Dominik Welke, Joos Behncke, Marina Hader +6
Brain-controlled robots are a promising new type of assistive device for severely impaired persons. Little is however known about how to optimize the interaction of humans and brai…