234 citations · 247 across the 3 of their papers we have counts for
3 papers
Deep Riemannian Networks for End-to-End EEG Decoding
Daniel Wilson, Robin Tibor Schirrmeister, Lukas Alexander Wilhelm Gemein +1
State-of-the-art performance in electroencephalography (EEG) decoding tasks is currently often achieved with either Deep-Learning (DL) or Riemannian-Geometry-based decoders (RBDs).…
Machine-Learning-Based Diagnostics of EEG Pathology
Lukas Alexander Wilhelm Gemein, Robin Tibor Schirrmeister, Patryk Chrabąszcz +5
Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and end-to-end approache…
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…