3 citations · 3 across the 2 of their papers we have counts for
3 papers
Deep Invertible Networks for EEG-based brain-signal decoding
Robin Tibor Schirrmeister, Tonio Ball
In this manuscript, we investigate deep invertible networks for EEG-based brain signal decoding and find them to generate realistic EEG signals as well as classify novel signals ab…
Deep Transfer Learning for Error Decoding from Non-Invasive EEG
Martin Völker, Robin T. Schirrmeister, Lukas D. J. Fiederer +2
We recorded high-density EEG in a flanker task experiment (31 subjects) and an online BCI control paradigm (4 subjects). On these datasets, we evaluated the use of transfer learnin…
Deep learning with convolutional neural networks for decoding and visualization of EEG pathology
Robin Tibor Schirrmeister, Lukas Gemein, Katharina Eggensperger +2
We apply convolutional neural networks (ConvNets) to the task of distinguishing pathological from normal EEG recordings in the Temple University Hospital EEG Abnormal Corpus. We us…