- On the interpretation of weight vectors of linear models in multivariate neuroimagingStefan Haufe
Fachgebiet Maschinelles Lernen, Technische Universitat Berlin, Germany Bernstein Focus Neurotechnology, Berlin, Germany Electronic address
Neuroimage 87:96-110. 2014..We hope that this work raises awareness for an often encountered problem and provides a theoretical basis for conducting better interpretable multivariate neuroimaging analyses...
- 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...
- Open database of epileptic EEG with MRI and postoperational assessment of foci--a real world verification for the EEG inverse solutionsPiotr Zwoliński
Memorial Child Hospital, Warsaw, Poland
Neuroinformatics 8:285-99. 2010..It seems to offer a possibility of tracing the spatial evolution of seizures in time...
- 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....
- 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...
- 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...
- 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...
- 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...
- 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....
- Now you'll feel it, now you won't: EEG rhythms predict the effectiveness of perceptual maskingRuth Schubert
Neurophysics Group, Department of Neurology and Clinical Neurophysiology, Charite University Medicine Berlin, Berlin, Germany
J Cogn Neurosci 21:2407-19. 2009....
- Finding brain oscillations with power dependencies in neuroimaging dataSven Dähne
Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Berlin, Germany Bernstein Center for Computational Neuroscience, Berlin, Germany Electronic address
Neuroimage 96:334-48. 2014..In the analysis of real EEG recordings, we demonstrate excellent unsupervised discovery of meaningful power-to-power couplings, within as well as across subjects and frequency bands. ..
- 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...
- Telemedical Interventional Monitoring in Heart Failure (TIM-HF), a randomized, controlled intervention trial investigating the impact of telemedicine on mortality in ambulatory patients with heart failure: study designFriedrich Koehler
Department of Cardiology and Angiology, Centre for Cardiovascular Telemedicine, Charite Universitatsmedizin Berlin, Campus Mitte, Berlin, Germany
Eur J Heart Fail 12:1354-62. 2010..The mean follow-up was 21.5±7.2 months, with a minimum of 12 months. Perspective The study will provide important prospective outcome data on the impact of telemedical management in patients with CHF...