Stefan Haufe

Summary

Country: Germany

Publications

  1. ncbi Modeling sparse connectivity between underlying brain sources for EEG/MEG
    Stefan Haufe
    Berlin Institute of Technology, Berlin 10623, Germany
    IEEE Trans Biomed Eng 57:1954-63. 2010
  2. ncbi Combining sparsity and rotational invariance in EEG/MEG source reconstruction
    Stefan Haufe
    Machine Learning Group, Department of Computer Science, TU Berlin, Franklinstr 28 29, D 10587 Berlin, Germany
    Neuroimage 42:726-38. 2008
  3. ncbi Localization of class-related mu-rhythm desynchronization in motor imagery based brain-computer interface sessions
    Stefan Haufe
    Berlin Institute of Technology, Franklinstr 28 29, D 10587, Germany
    Conf Proc IEEE Eng Med Biol Soc 2010:5137-40. 2010
  4. ncbi Large-scale EEG/MEG source localization with spatial flexibility
    Stefan Haufe
    Department of Computer Science, Berlin Institute of Technology, Berlin, Germany
    Neuroimage 54:851-9. 2011
  5. ncbi Single-trial analysis and classification of ERP components--a tutorial
    Benjamin Blankertz
    Berlin Institute of Technology, Machine Learning Laboratory, Berlin, Germany
    Neuroimage 56:814-25. 2011
  6. ncbi EEG potentials predict upcoming emergency brakings during simulated driving
    Stefan Haufe
    Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Franklinstraße 28 29, D 10587 Berlin, Germany
    J Neural Eng 8:056001. 2011
  7. ncbi Pre-stimulus sensorimotor rhythms influence brain-computer interface classification performance
    Cecilia L Maeder
    Berlin Institute of Technology, Machine Learning Laboratory, Germany
    IEEE Trans Neural Syst Rehabil Eng 20:653-62. 2012
  8. ncbi A critical assessment of connectivity measures for EEG data: a simulation study
    Stefan Haufe
    Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Franklinstr 28 29, 10587 Berlin, Germany
    Neuroimage 64:120-33. 2013
  9. ncbi Automatic classification of artifactual ICA-components for artifact removal in EEG signals
    Irene Winkler
    Machine Learning Laboratory, Berlin Institute of Technology, Franklinstr, 28 29, 10587 Berlin, Germany
    Behav Brain Funct 7:30. 2011

Detail Information

Publications9

  1. ncbi Modeling sparse connectivity between underlying brain sources for EEG/MEG
    Stefan Haufe
    Berlin Institute of Technology, Berlin 10623, Germany
    IEEE Trans Biomed Eng 57:1954-63. 2010
    ..We demonstrate the usefulness of SCSA with simulated data and compare it to a number of existing algorithms with excellent results...
  2. ncbi Combining sparsity and rotational invariance in EEG/MEG source reconstruction
    Stefan Haufe
    Machine Learning Group, Department of Computer Science, TU Berlin, Franklinstr 28 29, D 10587 Berlin, Germany
    Neuroimage 42:726-38. 2008
    ..Compared to its peers FVR was the only method that delivered correct location of the source in the somatosensory area of each hemisphere in accordance with neurophysiological prior knowledge...
  3. ncbi Localization of class-related mu-rhythm desynchronization in motor imagery based brain-computer interface sessions
    Stefan Haufe
    Berlin Institute of Technology, Franklinstr 28 29, D 10587, Germany
    Conf Proc IEEE Eng Med Biol Soc 2010:5137-40. 2010
    ..As a technical contribution, we extend S-FLEX to the multiple measurement case in a way that the activity of different frequency bins within the mu-band is coherently localized...
  4. ncbi Large-scale EEG/MEG source localization with spatial flexibility
    Stefan Haufe
    Department of Computer Science, Berlin Institute of Technology, Berlin, Germany
    Neuroimage 54:851-9. 2011
    ..Our approach based on single-trial localization of complex Fourier coefficients yields class-specific focal sources in the sensorimotor cortices...
  5. ncbi Single-trial analysis and classification of ERP components--a tutorial
    Benjamin Blankertz
    Berlin Institute of Technology, Machine Learning Laboratory, Berlin, Germany
    Neuroimage 56:814-25. 2011
    ....
  6. ncbi EEG potentials predict upcoming emergency brakings during simulated driving
    Stefan Haufe
    Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Franklinstraße 28 29, D 10587 Berlin, Germany
    J Neural Eng 8:056001. 2011
    ....
  7. ncbi Pre-stimulus sensorimotor rhythms influence brain-computer interface classification performance
    Cecilia L Maeder
    Berlin Institute of Technology, Machine Learning Laboratory, Germany
    IEEE Trans Neural Syst Rehabil Eng 20:653-62. 2012
    ..Our findings support the idea that exploiting information about the ongoing SMR might be the key to boosting performance in future SMR-BCI experiments and motor related tasks in general...
  8. ncbi A critical assessment of connectivity measures for EEG data: a simulation study
    Stefan Haufe
    Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Franklinstr 28 29, 10587 Berlin, Germany
    Neuroimage 64:120-33. 2013
    ..Integrating the insights of our study, we provide a guidance for measuring brain interaction from EEG data. Software for generating benchmark data is made available...
  9. ncbi Automatic classification of artifactual ICA-components for artifact removal in EEG signals
    Irene Winkler
    Machine Learning Laboratory, Berlin Institute of Technology, Franklinstr, 28 29, 10587 Berlin, Germany
    Behav Brain Funct 7:30. 2011
    ..Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts...