| Machine-learning-based diagnostics of EEG pathology LAW Gemein, RT Schirrmeister, P Chrabąszcz, D Wilson, J Boedecker, ... NeuroImage 220, 117021, 2020 | 210 | 2020 |
| A reusable benchmark of brain-age prediction from M/EEG resting-state signals DA Engemann, A Mellot, R Höchenberger, H Banville, D Sabbagh, ... Neuroimage 262, 119521, 2022 | 59 | 2022 |
| Deep riemannian networks for eeg decoding D Wilson, RT Schirrmeister, LAW Gemein, T Ball arXiv preprint arXiv:2212.10426, 2022 | 13 | 2022 |
| An extended clinical EEG dataset with 15,300 automatically labelled recordings for pathology decoding AK Kiessner, RT Schirrmeister, LAW Gemein, J Boedecker, T Ball NeuroImage: Clinical 39, 103482, 2023 | 12 | 2023 |
| Deep learning with convolutional neural networks for decoding and visualization of eeg pathology R Tibor Schirrmeister, L Gemein, K Eggensperger, F Hutter, T Ball arXiv e-prints, arXiv: 1708.08012, 2017 | 7 | 2017 |
| Brain Age Revisited: Investigating the State vs. Trait Hypotheses of EEG-derived Brain-Age Dynamics with Deep Learning LAW Gemein, RT Schirrmeister, J Boedecker, T Ball Imaging Neuroscience, 2024 | 2 | 2024 |
| P64. Deep learning for EEG diagnostics RT Schirrmeister, L Gemein, K Eggensberger, F Hutter, T Ball Clinical Neurophysiology 129 (8), e94, 2018 | 2 | 2018 |
| Supervised machine learning approaches applied in the diagnostic workup of spontaneous intracranial hypotension LM Kraus, C Fung, L Dieringer, L Gemein, T Ball, J Beck Brain and Spine 1, 100835, 2021 | | 2021 |
