Posted in Former Members

Postdoctoral Research Associate, The City College of New York (2013-2014);

Postdoctoral Research Fellow, Columbia University (2014-Present)

2011-2013: Postdoc, Machine Learning Lab, Technische Universität Berlin, Germany
2006-2011: Ph. D. in Natural Sciences, Machine Learning Lab, Technische Universität Berlin, Germany
2000-2005: M. Sc. in Computer Science, Martin-Luther Universität Halle-Wittenberg, Germany

shaufe AT ccny DOT cuny DOT edu


Research interests

* Study of attention and engagement using hyperscanning paradigms and the analysis of inter-subject correlations in neuroimaging data.

* EEG-based functional brain connectivity analysis.
* Inverse modeling for EEG source localization.
* Brain-computer interfaces, mental state monitoring.
* Generally, development and application of machine learning and signal processing (e.g. source separation) tools for analyzing EEG data.

Journal publications

15. Haufe S, Meinecke FC, Görgen K, Dähne S, Haynes JD, Blankertz B, and Bießmann F, 2014. On the interpretation of weight vectors of linear models in multivariate neuroimaging. NeuroImage, 87:96–110.

14. Dähne S, Meinecke FC, Haufe S, Höhne J, Tangermann M, Müller KR, and Nikulin VV, 2014. SPoC: a novel framework for optimally relating the amplitude of neuronal oscillations to cognitive variables. NeuroImage, 86:111–122.
13. Schreuder M, Höhne J, Blankertz B, Haufe S, Dickhaus T, and Tangermann M, 2013. Optimizing event-related potential based brain-computer interfaces: a systematic evaluation of dynamic stopping methods. J Neural Eng, 10(3):036025.
12. Haufe S, Nikulin VV, Müller KR, and Nolte G, 2013. A critical assessment of connectivity measures for EEG data: a simulation study. NeuroImage, 64:120–133.
11. Maeder CL, Sannelli C, Haufe S and Blankertz B, 2012. Prestimulus sensorimotor rhythms influence brain-computer interface classification performance. IEEE Trans Neural Syst Rehabil Eng, 20:653–662.
10. Winkler I, Haufe S and Tangermann M, 2011. Automatic Classification of Artifactual ICA Components for Artifact Removal in EEG Signals. Behav Brain Funct, 7:30.
9. Haufe S, Treder MS, Gugler MF, Sagebaum M, Curio G, and Blankertz B, 2011. EEG potentials predict upcoming emergency brakings during simulated driving. J Neural Eng, 8(5):056001.
8. Blankertz B, Lemm S, Treder M, Haufe S, and Müller KR, 2011. Single-Trial Analysis and Classification of ERP Components – a Tutorial. NeuroImage, 56(2):814–825.
7. Haufe S, Tomioka R, Dickhaus T, Sannelli C, Blankertz B, Nolte G, and Müller KR, 2011. Large-Scale EEG/MEG Source Localization with Spatial Flexibility. NeuroImage, 54(2):851–859.
6. Blankertz B, Tangermann M, Vidaurre C, Fazli S, Sannelli C, Haufe S, Maeder C, Ramsey L, Sturm I, Curio G, and Müller KR, 2010. Non-Medical Uses of BCI Technology. Front Neuroscience, 4:198.
5. Zwolinski P, Roszkowski M, Zygierewicz Z, Haufe S, Nolte G, and Durka PJ, 2010. Open database of epileptic EEG with MRI and postoperational assessment of foci—a real world verification for the EEG inverse solutions. Neuroinformatics, 8(4):285–299.
4. Haufe S, Tomioka R, Nolte G, Müller KR, and Kawanabe M, 2010. Modeling sparse connectivity between underlying brain sources for EEG/MEG. IEEE Trans Biomed Eng, 57(8):1954–1963.
3. Schubert R, Haufe S, Blankenburg F, Villringer A, and Curio G, 2009. Now you’ll feel it – now you won’t: EEG rhythms predict the effectiveness of perceptual masking. J Cognitive Neurosci, 21(12):2407–2419.
2. Tangermann M, Winkler I, Haufe S, and Blankertz B, 2009. Classification of artifactual ICA components. Int J Bioelectromagnetism, 11(2):110–114.
1. Haufe S, Nikulin VV, Ziehe A, Müller KR, and Nolte G, 2008. Combining sparsity and rotational invariance in EEG/MEG source reconstruction. NeuroImage, 42(2):726–738.


Selected conference proceedings and book chapters

4. Haufe S, Nikulin VV, Müller KR, and Nolte G, 2012. Pitfalls in EEG-based brain connectivity analysis. In: Langs G, Rish I, Grosse-Wentrup M and Murphy B (Eds.), NIPS 2011 Workshop on Machine Learning and Interpretation in Neuroimaging, LNAI 7263:202-209, Springer Berlin / Heidelberg.
3. Haufe S, Nikulin VV and Nolte G, 2012. Alleviating the influence of weak data asymmetries on Granger-causal analyses. In: Cichocki A, Theis F, Yeredor A and Zibulevsky M (Eds.), Latent Variable Analysis and Signal Separation, LNCS 7191:25-33, Springer Berlin / Heidelberg.
2. Haufe S, Müller KR, Nolte G, and Krämer N, 2010. Sparse Causal Discovery in Multivariate Time Series. In: Guyon, I., Janzing, D., Schölkopf, B. (Eds.), Causality: Objectives and Assessment. Vol. 6 of JMLR W&CP. pp. 97–106.
1. Haufe S, Nikulin VV, Ziehe A, Müller KR, and Nolte G, 2009. Estimating vector fields using sparse basis field expansions. In: Koller D, Schuurmans D, Bengio Y, Bottou L (Eds.), Advances in Neural Information Processing Systems 21, pp. 617–624. MIT Press, Cambridge, MA.