- Modeling sparse connectivity between underlying brain sources for EEG/MEGStefan 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...
- Combining sparsity and rotational invariance in EEG/MEG source reconstructionStefan 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...
- Localization of class-related mu-rhythm desynchronization in motor imagery based brain-computer interface sessionsStefan 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...
- Large-scale EEG/MEG source localization with spatial flexibilityStefan 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...
- Optimizing event-related potential based brain-computer interfaces: a systematic evaluation of dynamic stopping methodsMartijn Schreuder
Machine Learning Laboratory, Berlin Institute of Technology, Marchstrasse 23, 10537, Berlin, Germany
J Neural Eng 10:036025. 2013..Despite their high potential for BCI systems at the patient's bedside, those methods are typically ignored in current BCI literature. The goal of the current study is to assess the benefit of these methods...
- A critical assessment of connectivity measures for EEG data: a simulation studyStefan 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...
- Single-trial analysis and classification of ERP components--a tutorialBenjamin Blankertz
Berlin Institute of Technology, Machine Learning Laboratory, Berlin, Germany
Neuroimage 56:814-25. 2011....
- SPoC: A novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parametersSven Dähne
Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Marchstr 23, 10587 Berlin, Germany Bernstein Center for Computational Neuroscience, Berlin, Germany Electronic address
Neuroimage 86:111-22. 2014....
- EEG potentials predict upcoming emergency brakings during simulated drivingStefan 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....
- Pre-stimulus sensorimotor rhythms influence brain-computer interface classification performanceCecilia 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...
- Automatic classification of artifactual ICA-components for artifact removal in EEG signalsIrene 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...