234 citations · 508 across the 23 of their papers we have counts for
3 papers · 1 filter
Deep Learning for micro-Electrocorticographic (μECoG) Data
Xi Wang, C. Alexis Gkogkidis, Robin T. Schirrmeister +6
Machine learning can extract information from neural recordings, e.g., surface EEG, ECoG and μECoG, and therefore plays an important role in many research and clinical applications…
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…
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-…