Deep learning with convolutional neural networks for EEG decoding and visualization
About
PLEASE READ AND CITE THE REVISED VERSION at Human Brain Mapping: http://onlinelibrary.wiley.com/doi/10.1002/hbm.23730/full Code available here: https://github.com/robintibor/braindecode
Robin Tibor Schirrmeister, Jost Tobias Springenberg, Lukas Dominique Josef Fiederer, Martin Glasstetter, Katharina Eggensperger, Michael Tangermann, Frank Hutter, Wolfram Burgard, Tonio Ball• 2017
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Seizure Forecasting | CHB-MIT Scalp EEG | Sensitivity (Sn)100 | 54 | |
| EEG signal classification | MAMEM-SSVEP-II | Accuracy56.93 | 29 | |
| Emotion Recognition | MEEG Valence | Accuracy79.77 | 26 | |
| Classification | BCI IV-2a | Accuracy32.7 | 25 | |
| Motor Imagery | PhysioNet-MI | Accuracy61.82 | 20 | |
| EEG Classification | BCIC IV-2a (test) | Accuracy72.49 | 18 | |
| Motor Imagery decoding | BCI Competition IV 2a I (cross-subject) | Average Accuracy80.15 | 17 | |
| EEG Classification | SSVEP (test) | Accuracy56.93 | 17 | |
| EEG Classification | MI (test) | Accuracy61.84 | 17 | |
| EEG Classification | ERN (test) | AUC71.86 | 17 |
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