4 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…