Dickhaus T - Search Results

1

On optimal channel configurations for SMR-based brain-computer interfaces.

Sannelli C, Dickhaus T, Halder S, Hammer EM, Müller KR, Blankertz B. Sannelli C, et al. Among authors: dickhaus t. Brain Topogr. 2010 Jun;23(2):186-93. doi: 10.1007/s10548-010-0135-0. Epub 2010 Feb 17. Brain Topogr. 2010. PMID: 20162347

2

Neurophysiological predictor of SMR-based BCI performance.

Blankertz B, Sannelli C, Halder S, Hammer EM, Kübler A, Müller KR, Curio G, Dickhaus T. Blankertz B, et al. Among authors: dickhaus t. Neuroimage. 2010 Jul 15;51(4):1303-9. doi: 10.1016/j.neuroimage.2010.03.022. Epub 2010 Mar 17. Neuroimage. 2010. PMID: 20303409 Free article.

3

Large-scale EEG/MEG source localization with spatial flexibility.

Haufe S, Tomioka R, Dickhaus T, Sannelli C, Blankertz B, Nolte G, Müller KR. Haufe S, et al. Among authors: dickhaus t. Neuroimage. 2011 Jan 15;54(2):851-9. doi: 10.1016/j.neuroimage.2010.09.003. Epub 2010 Sep 9. Neuroimage. 2011. PMID: 20832477

4

Localization of class-related mu-rhythm desynchronization in motor imagery based brain-computer interface sessions.

Haufe S, Tomioka R, Dickhaus T, Sannelli C, Blankertz B, Nolte G, Muller KR. Haufe S, et al. Among authors: dickhaus t. Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:5137-40. doi: 10.1109/IEMBS.2010.5626177. Annu Int Conf IEEE Eng Med Biol Soc. 2010. PMID: 21095811

5

Introduction to machine learning for brain imaging.

Lemm S, Blankertz B, Dickhaus T, Müller KR. Lemm S, et al. Among authors: dickhaus t. Neuroimage. 2011 May 15;56(2):387-99. doi: 10.1016/j.neuroimage.2010.11.004. Epub 2010 Dec 21. Neuroimage. 2011. PMID: 21172442 Review.

6

Psychological predictors of SMR-BCI performance.

Hammer EM, Halder S, Blankertz B, Sannelli C, Dickhaus T, Kleih S, Müller KR, Kübler A. Hammer EM, et al. Among authors: dickhaus t. Biol Psychol. 2012 Jan;89(1):80-6. doi: 10.1016/j.biopsycho.2011.09.006. Epub 2011 Sep 29. Biol Psychol. 2012. PMID: 21964375

7

Optimizing event-related potential based brain-computer interfaces: a systematic evaluation of dynamic stopping methods.

Schreuder M, Höhne J, Blankertz B, Haufe S, Dickhaus T, Tangermann M. Schreuder M, et al. Among authors: dickhaus t. J Neural Eng. 2013 Jun;10(3):036025. doi: 10.1088/1741-2560/10/3/036025. Epub 2013 May 20. J Neural Eng. 2013. PMID: 23685458

8

Binary classification with pFDR-pFNR losses.

Dickhaus T, Blankertz B, Meinecke FC. Dickhaus T, et al. Biom J. 2013 May;55(3):463-77. doi: 10.1002/bimj.201200054. Epub 2013 Feb 4. Biom J. 2013. PMID: 23378199

9

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies.

Mieth B, Kloft M, Rodríguez JA, Sonnenburg S, Vobruba R, Morcillo-Suárez C, Farré X, Marigorta UM, Fehr E, Dickhaus T, Blanchard G, Schunk D, Navarro A, Müller KR. Mieth B, et al. Among authors: dickhaus t. Sci Rep. 2016 Nov 28;6:36671. doi: 10.1038/srep36671. Sci Rep. 2016. PMID: 27892471 Free PMC article.

10

More Specific Signal Detection in Functional Magnetic Resonance Imaging by False Discovery Rate Control for Hierarchically Structured Systems of Hypotheses.

Schildknecht K, Tabelow K, Dickhaus T. Schildknecht K, et al. Among authors: dickhaus t. PLoS One. 2016 Feb 25;11(2):e0149016. doi: 10.1371/journal.pone.0149016. eCollection 2016. PLoS One. 2016. PMID: 26914144 Free PMC article.

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