S
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.
Papers
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Proceedings Article
A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI
TL;DR: A linear state-space model to infer the effective connectivity in a distributed brain network based on simultaneously recorded EEG and fMRI data is developed and the results emphasize the importance of obtaining accurate spatial localization of ROIs from fMRI.
Posted ContentDOI
Relationship between Regional White Matter Hyperintensities and Alpha Oscillations in Older Adults
Deniz Kumral,Elena Cesnaite,Frauke Beyer,Simon M. Hofmann,Tilman Hensch,Christian Sander,Ulrich Hegerl,Stefan Haufe,Arno Villringer,A. Veronica Witte,Vadim V. Nikulin +10 more
TL;DR: Although an increase in alpha oscillations due to WMH can have a compensatory nature, it is suggested that an elevated alpha power is a consequence of WMH affecting a spatial organization of alpha sources.
Proceedings ArticleDOI
Localization of class-related mu-rhythm desynchronization in motor imagery based Brain-Computer Interface sessions
Stefan Haufe,Ryota Tomioka,Thorsten Dickhaus,Claudia Sannelli,Benjamin Blankertz,Guido Nolte,Klaus-Robert Müller +6 more
TL;DR: The approach is based on localization of single-trial Fourier coefficients using sparse basis field expansions (S-FLEX) and reveals focal sources in the sensorimotor cortices, a finding which can be regarded as a proof for the expected neurophysiological origin of the BCI control signal.
Proceedings ArticleDOI
An extendable simulation framework for benchmarking EEG-based brain connectivity estimation methodologies
TL;DR: An extendable simulation framework that enables researchers to test their analysis pipelines on customizable realistically simulated EEG data and define three simple criteria to measure source localization, connectivity detection and directionality estimation performance.
Posted ContentDOI
Robust Estimation of Noise for Electromagnetic Brain Imaging with the Champagne Algorithm
Chang Cai,Mithun Diwakar,Ali Hashemi,Ali Hashemi,Stefan Haufe,Kensuke Sekihara,Srikantan S. Nagarajan +6 more
TL;DR: The resulting algorithm, Champagne with noise learning, is quite robust to initialization and is computationally efficient, and in simulations, performance of the proposed noise learning algorithm is consistently superior to Champagne without noise learning.