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Robin T Schirrmeister

Showing results (1-10 of 9) with videos related to

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Frontiers in Robotics and AI|October 27, 2025
New avenues for understanding what deep networks learn from EEGRobin T Schirrmeister, Tonio Ball
Frontiers in Robotics and AI|September 11, 2025
EEG-CLIP: learning EEG representations from natural language descriptionsTidiane Camaret Ndir, Robin T Schirrmeister, Tonio Ball
Computers in Biology and Medicine|June 15, 2024
Reaching the ceiling? Empirical scaling behaviour for deep EEG pathology classificationAnn-Kathrin Kiessner, Robin T Schirrmeister, Joschka Boedecker, et al.
Imaging Neuroscience (Cambridge, Mass.)|August 13, 2025
Deep Riemannian Networks for end-to-end EEG decodingDaniel Wilson, Robin T Schirrmeister, Lukas A W Gemein, et al.
Imaging Neuroscience (Cambridge, Mass.)|August 13, 2025
Brain age revisited: Investigating the state vs. trait hypotheses of EEG-derived brain-age dynamics with deep learningLukas A W Gemein, Robin T Schirrmeister, Joschka Boedecker, et al.
Neuroimage. Clinical|August 6, 2023
An extended clinical EEG dataset with 15,300 automatically labelled recordings for pathology decodingAnn-Kathrin Kiessner, Robin T Schirrmeister, Lukas A W Gemein, et al.
Frontiers in Neurorobotics|October 26, 2019
Hybrid Brain-Computer-Interfacing for Human-Compliant Robots: Inferring Continuous Subjective Ratings With Deep RegressionLukas D J Fiederer, Martin Völker, Robin T Schirrmeister, et al.
Neuroimage|June 14, 2020
Machine-learning-based diagnostics of EEG pathologyLukas A W Gemein, Robin T Schirrmeister, Patryk Chrabąszcz, et al.
Journal of Cardiovascular Magnetic Resonance : Official Journal of the Society for Cardiovascular Magnetic Resonance|September 14, 2025
Cardiac Magnetic Resonance Imaging in the German National Cohort (NAKO): Automated Segmentation of Short-Axis Cine Images and Post-Processing Quality ControlPeter M Full, Robin T Schirrmeister, Manuel Hein, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Frontiers in Robotics and AI|October 27, 2025
New avenues for understanding what deep networks learn from EEGRobin T Schirrmeister, Tonio Ball
Frontiers in Robotics and AI|September 11, 2025
EEG-CLIP: learning EEG representations from natural language descriptionsTidiane Camaret Ndir, Robin T Schirrmeister, Tonio Ball
Computers in Biology and Medicine|June 15, 2024
Reaching the ceiling? Empirical scaling behaviour for deep EEG pathology classificationAnn-Kathrin Kiessner, Robin T Schirrmeister, Joschka Boedecker, et al.
Imaging Neuroscience (Cambridge, Mass.)|August 13, 2025
Deep Riemannian Networks for end-to-end EEG decodingDaniel Wilson, Robin T Schirrmeister, Lukas A W Gemein, et al.
Imaging Neuroscience (Cambridge, Mass.)|August 13, 2025
Brain age revisited: Investigating the state vs. trait hypotheses of EEG-derived brain-age dynamics with deep learningLukas A W Gemein, Robin T Schirrmeister, Joschka Boedecker, et al.
Neuroimage. Clinical|August 6, 2023
An extended clinical EEG dataset with 15,300 automatically labelled recordings for pathology decodingAnn-Kathrin Kiessner, Robin T Schirrmeister, Lukas A W Gemein, et al.
Frontiers in Neurorobotics|October 26, 2019
Hybrid Brain-Computer-Interfacing for Human-Compliant Robots: Inferring Continuous Subjective Ratings With Deep RegressionLukas D J Fiederer, Martin Völker, Robin T Schirrmeister, et al.
Neuroimage|June 14, 2020
Machine-learning-based diagnostics of EEG pathologyLukas A W Gemein, Robin T Schirrmeister, Patryk Chrabąszcz, et al.
Journal of Cardiovascular Magnetic Resonance : Official Journal of the Society for Cardiovascular Magnetic Resonance|September 14, 2025
Cardiac Magnetic Resonance Imaging in the German National Cohort (NAKO): Automated Segmentation of Short-Axis Cine Images and Post-Processing Quality ControlPeter M Full, Robin T Schirrmeister, Manuel Hein, et al.
Pageof 1