M
Martin Glasstetter
Researcher at University of Freiburg
Publications - 12
Citations - 1854
Martin Glasstetter is an academic researcher from University of Freiburg. The author has contributed to research in topics: Wearable computer & Computer science. The author has an hindex of 4, co-authored 10 publications receiving 994 citations. Previous affiliations of Martin Glasstetter include University Medical Center Freiburg.
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Journal ArticleDOI
Deep learning with convolutional neural networks for EEG decoding and visualization.
Robin Tibor Schirrmeister,Jost Tobias Springenberg,Lukas D. J. Fiederer,Martin Glasstetter,Katharina Eggensperger,Michael Tangermann,Frank Hutter,Wolfram Burgard,Tonio Ball +8 more
TL;DR: This study shows how to design and train convolutional neural networks to decode task‐related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG‐based brain mapping.
Posted Content
Deep learning with convolutional neural networks for brain mapping and decoding of movement-related information from the human EEG.
Robin Tibor Schirrmeister,Jost Tobias Springenberg,Lukas D. J. Fiederer,Martin Glasstetter,Katharina Eggensperger,Michael Tangermann,Frank Hutter,Wolfram Burgard,Tonio Ball +8 more
TL;DR: This study shows how to design and train ConvNets to decode movement-related information from the raw EEG without handcrafted features and highlights the potential of deep convolutional neural networks combined with advanced visualization techniques for EEG-based brain mapping.
Journal ArticleDOI
Signal quality and patient experience with wearable devices for epilepsy management
Mona Nasseri,Ewan S. Nurse,Martin Glasstetter,Sebastian Böttcher,Nicholas M. Gregg,Aiswarya Laks Nandakumar,Boney Joseph,Tal Pal Attia,Pedro Viana,Pedro Viana,Elisa Bruno,Andrea Biondi,Mark J. Cook,Gregory A. Worrell,Andreas Schulze-Bonhage,Matthias Dümpelmann,Dean R. Freestone,Mark P. Richardson,Benjamin H. Brinkmann +18 more
TL;DR: Current wearable devices can provide high‐quality data and are acceptable for routine use, but continued development is needed to improve data quality, consistency, and management, as well as acceptability to patients.
Journal ArticleDOI
Wearable devices for seizure detection: Practical experiences and recommendations from the Wearables for Epilepsy And Research (WEAR) International Study Group
Elisa Bruno,Sebastian Böttcher,Sebastian Böttcher,Pedro Viana,Pedro Viana,Marta Amengual-Gual,Boney Joseph,Nino Epitashvili,Matthias Dümpelmann,Martin Glasstetter,Andrea Biondi,Kristof Van Laerhoven,Tobias Loddenkemper,Mark P. Richardson,Andreas Schulze-Bonhage,Benjamin H. Brinkmann +15 more
TL;DR: The Wearables for Epilepsy And Research (WEAR) International Study Group identified a set of methodology standards to guide research on wearable devices for seizure detection as discussed by the authors, and formed an international consortium of experts from clinical research, engineering, computer science and data analytics at the beginning of 2020.
Journal ArticleDOI
Signal quality and power spectrum analysis of remote ultra long-term subcutaneous EEG
Pedro Viana,Pedro Viana,Line Sofie Remvig,Jonas Duun-Henriksen,Martin Glasstetter,Matthias Dümpelmann,Ewan S. Nurse,Isabel Pavão Martins,Andreas Schulze-Bonhage,Dean R. Freestone,Benjamin H. Brinkmann,Troels W. Kjaer,Mark P. Richardson +12 more
TL;DR: The spectral characteristics of minimally-invasive, ultra long-term sqEEG are similar to scalp EEG, while the signal is highly stationary, reinforcing the suitability of this system for chronic implantation on diverse clinical applications, from seizure detection and forecasting to brain-computer interfaces.