Search Results - "Schirrmeister, Robin T."

Machine-learning-based diagnostics of EEG pathology by Gemein, Lukas A.W., Schirrmeister, Robin T., Chrabąszcz, Patryk, Wilson, Daniel, Boedecker, Joschka, Schulze-Bonhage, Andreas, Hutter, Frank, Ball, Tonio

“…Machine learning (ML) methods have the potential to automate clinical EEG analysis. They can be categorized into feature-based (with handcrafted features), and…”
Get full text

An extended clinical EEG dataset with 15,300 automatically labelled recordings for pathology decoding by Kiessner, Ann-Kathrin, Schirrmeister, Robin T., Gemein, Lukas A.W., Boedecker, Joschka, Ball, Tonio

“…•We extended the largest public clinical EEG dataset by a factor of five.•We utilized automatic labeling based on clinical reports.•The extended dataset size…”
Get full text

Hybrid Brain-Computer-Interfacing for Human-Compliant Robots: Inferring Continuous Subjective Ratings With Deep Regression by Fiederer, Lukas D. J., Völker, Martin, Schirrmeister, Robin T., Burgard, Wolfram, Boedecker, Joschka, Ball, Tonio

“…Appropriate robot behavior during human-robot interaction is a key part in the development of human-compliant assistive robotic systems. This study poses the…”
Get full text

Reaching the ceiling? Empirical scaling behaviour for deep EEG pathology classification by Kiessner, Ann-Kathrin, Schirrmeister, Robin T., Boedecker, Joschka, Ball, Tonio

“…Machine learning techniques, particularly deep convolutional neural networks (ConvNets), are increasingly being used to automate clinical EEG analysis, with…”
Get full text

Brain age revisited: Investigating the state vs. trait hypotheses of EEG-derived brain-age dynamics with deep learning by Gemein, Lukas A.W., Schirrmeister, Robin T., Boedecker, Joschka, Ball, Tonio

“…The brain’s biological age has been considered as a promising candidate for a neurologically significant biomarker. However, recent results based on…”
Get full text

A Large-Scale Evaluation Framework for EEG Deep Learning Architectures by Heilmeyer, Felix A., Schirrmeister, Robin T., Fiederer, Lukas D. J., Volker, Martin, Behncke, Joos, Ball, Tonio

“…EEG is the most common signal source for noninvasive BCI applications. For such applications, the EEG signal needs to be decoded and translated into…”
Get full text

Cross-Paradigm Pretraining of Convolutional Networks Improves Intracranial EEG Decoding by Behncke, Joos, Schirrmeister, Robin Tibor, Volker, Martin, Hammer, Jiri, Marusic, Petr, Schulze-Bonhage, Andreas, Burgard, Wolfram, Ball, Tonio

“…When it comes to the classification of brain signals in real-life applications, the training and the prediction data are often described by different…”
Get full text

Intracranial Error Detection via Deep Learning by Volker, Martin, Hammer, Jiri, Schirrmeister, Robin T., Behncke, Joos, Fiederer, Lukas D.J., Schulze-Bonhage, Andreas, Marusic, Petr, Burgard, Wolfram, Ball, Tonio

“…Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in…”
Get full text

The signature of robot action success in EEG signals of a human observer: Decoding and visualization using deep convolutional neural networks by Behncke, Joos, Schirrmeister, Robin T., Burgard, Wolfram, Ball, Tonio

“…The importance of robotic assistive devices grows in our work and everyday life. Cooperative scenarios involving both robots and humans require safe…”
Get full text

Brain Age Revisited: Investigating the State vs. Trait Hypotheses of EEG-derived Brain-Age Dynamics with Deep Learning by Gemein, Lukas AW, Schirrmeister, Robin T, Boedecker, Joschka, Ball, Tonio

“…The brain's biological age has been considered as a promising candidate for a neurologically significant biomarker. However, recent results based on…”
Get full text

Deep transfer learning for error decoding from non-invasive EEG by Volker, Martin, Schirrmeister, Robin T., Fiederer, Lukas D. J., Burgard, Wolfram, Ball, Tonio

“…We recorded high-density EEG in a flanker task experiment (31 subjects) and an online BCI control paradigm (4 subjects). On these datasets, we evaluated the…”
Get full text

Deep Transfer Learning for Error Decoding from Non-Invasive EEG by Völker, Martin, Schirrmeister, Robin T, Fiederer, Lukas D. J, Burgard, Wolfram, Ball, Tonio

“…We recorded high-density EEG in a flanker task experiment (31 subjects) and an online BCI control paradigm (4 subjects). On these datasets, we evaluated the…”
Get full text

Deep Learning for micro-Electrocorticographic ({\mu}ECoG) Data by Wang, Xi, Gkogkidis, C. Alexis, Schirrmeister, Robin T, Heilmeyer, Felix A, Gierthmuehlen, Mortimer, Kohler, Fabian, Schuettler, Martin, Stieglitz, Thomas, Ball, Tonio

“…Machine learning can extract information from neural recordings, e.g., surface EEG, ECoG and {\mu}ECoG, and therefore plays an important role in many research…”
Get full text

A large-scale evaluation framework for EEG deep learning architectures by Heilmeyer, Felix A, Schirrmeister, Robin T, Fiederer, Lukas D. J, Völker, Martin, Behncke, Joos, Ball, Tonio

“…EEG is the most common signal source for noninvasive BCI applications. For such applications, the EEG signal needs to be decoded and translated into…”
Get full text

Intracranial Error Detection via Deep Learning by Völker, Martin, Hammer, Jiří, Schirrmeister, Robin T, Behncke, Joos, Fiederer, Lukas D. J, Schulze-Bonhage, Andreas, Marusič, Petr, Burgard, Wolfram, Ball, Tonio

“…Deep learning techniques have revolutionized the field of machine learning and were recently successfully applied to various classification problems in…”
Get full text