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Stefan Haufe
Researcher at Charité
Publications - 98
Citations - 6600
Stefan Haufe is an academic researcher from Charité. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 31, co-authored 82 publications receiving 5336 citations. Previous affiliations of Stefan Haufe include Fraunhofer Society & Columbia University.
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Journal ArticleDOI
On the interpretation of weight vectors of linear models in multivariate neuroimaging.
Stefan Haufe,Frank C. Meinecke,Kai Görgen,Sven Dähne,John-Dylan Haynes,Benjamin Blankertz,Felix Bießmann,Felix Bießmann +7 more
TL;DR: It is demonstrated that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study, in contrast to the interpretation of backward model parameters.
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Single-Trial Analysis and Classification of ERP Components - a Tutorial
TL;DR: This tutorial proposes to use shrinkage estimators and shows that appropriate regularization of linear discriminant analysis (LDA) by shrinkage yields excellent results for single-trial ERP classification that are far superior to classical LDA classification.
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Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals
TL;DR: This work proposes a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data that is applicable for different electrode placements and supports the introspection of results.
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The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology
Benjamin Blankertz,Benjamin Blankertz,Michael Tangermann,Carmen Vidaurre,Siamac Fazli,Claudia Sannelli,Stefan Haufe,Cecilia Maeder,Lenny Ramsey,Lenny Ramsey,Irene Sturm,Gabriel Curio,Klaus-Robert Müller +12 more
TL;DR: Examples of novel BCI applications which provide evidence for the promising potential of BCI technology for non-medical uses are presented and distinct methodological improvements required to bring non- medical applications ofBCI technology to a diversity of layperson target groups are discussed.
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A critical assessment of connectivity measures for EEG data: a simulation study.
TL;DR: A theoretical framework is developed to characterize artifacts of volume conduction, which may still be present even in reconstructed source time series as zero-lag correlations, and to distinguish their time-delayed brain interaction.