K
Klaus-Robert Müller
Researcher at Technical University of Berlin
Publications - 799
Citations - 98394
Klaus-Robert Müller is an academic researcher from Technical University of Berlin. The author has contributed to research in topics: Artificial neural network & Computer science. The author has an hindex of 129, co-authored 764 publications receiving 79391 citations. Previous affiliations of Klaus-Robert Müller include Korea University & University of Tokyo.
Papers
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Book ChapterDOI
A Maxmin Approach to Optimize Spatial Filters for EEG Single-Trial Classification
TL;DR: The proposed maxmin CSP method significantly improves the classical CSP approach in multiple BCI scenarios and can transform the respective complex mathematical program into a simple generalized eigenvalue problem and thus obtain robust spatial filters very efficiently.
Book ChapterDOI
Support Vector Machines
Konrad Rieck,Sören Sonnenburg,Sebastian Mika,Christin Schäfer,Pavel Laskov,David M. J. Tax,Klaus-Robert Müller +6 more
TL;DR: In this paper, the authors introduce basic concepts and ideas of the Support Vector Machines (SVM) and formulate the learning problem in a statistical framework, where a special focus is put on the concept of consistency, which leads to the principle of structural risk minimization.
Journal ArticleDOI
Unification of sparse Bayesian learning algorithms for electromagnetic brain imaging with the majorization minimization framework.
Ali Hashemi,Chang Cai,Gitta Kutyniok,Klaus-Robert Müller,Srikantan S. Nagarajan,Stefan Haufe +5 more
TL;DR: In this paper, the authors propose a novel method called LowSNR-BSI that achieves favorable source reconstruction performance in low signal-to-noise-ratio (SNR) settings.
Posted Content
Explaining Predictions of Non-Linear Classifiers in NLP
TL;DR: In this paper, the authors apply LRP for the first time to natural language processing (NLP) and use it to explain the predictions of a convolutional neural network (CNN) trained on a topic categorization task.
Proceedings ArticleDOI
Common Spatial Pattern Patches: Online evaluation on BCI-naive users
TL;DR: The novel Common Spatial Patterns Patches (CSPP) technique is proposed as a good candidate to improve the co-adaptive calibration of BCI systems, and the evaluation of CSPP in online operation is presented for the first time.