Thorsten Dickhaus
Thorsten Dickhaus
Professor of Mathematical Statistics at University of Bremen
Verified email at - Homepage
Cited by
Cited by
Introduction to machine learning for brain imaging
S Lemm, B Blankertz, T Dickhaus, KR Müller
Neuroimage 56 (2), 387-399, 2011
Neurophysiological predictor of SMR-based BCI performance
B Blankertz, C Sannelli, S Halder, EM Hammer, A Kübler, KR Müller, ...
Neuroimage 51 (4), 1303-1309, 2010
Prevalence of polyneuropathy in pre-diabetes and diabetes is associated with abdominal obesity and macroangiopathy: the MONICA/KORA Augsburg Surveys S2 and S3
D Ziegler, W Rathmann, T Dickhaus, C Meisinger, A Mielck, ...
Diabetes care 31 (3), 464-469, 2008
Neuropathic pain in diabetes, prediabetes and normal glucose tolerance: the MONICA/KORA Augsburg Surveys S2 and S3
D Ziegler, W Rathmann, T Dickhaus, C Meisinger, A Mielck
Pain medicine 10 (2), 393-400, 2009
Psychological predictors of SMR-BCI performance
EM Hammer, S Halder, B Blankertz, C Sannelli, T Dickhaus, S Kleih, ...
Biological psychology 89 (1), 80-86, 2012
Simultaneous statistical inference
T Dickhaus
Springer, Heidelberg. MR3184277 https://doi. org/10 1007, 978-3, 2014
Predicting BCI performance to study BCI illiteracy
T Dickhaus, C Sannelli, KR Müller, G Curio, B Blankertz
BMC Neuroscience 10 (Suppl 1), P84, 2009
Large-scale EEG/MEG source localization with spatial flexibility
S Haufe, R Tomioka, T Dickhaus, C Sannelli, B Blankertz, G Nolte, ...
NeuroImage 54 (2), 851-859, 2011
On the false discovery rate and an asymptotically optimal rejection curve
H Finner, T Dickhaus, M Roters
Epigenetic quantification of tumor-infiltrating T-lymphocytes
J Sehouli, C Loddenkemper, T Cornu, T Schwachula, U Hoffmüller, ...
Epigenetics 6 (2), 236-246, 2011
Prevalence and risk factors of neuropathic pain in survivors of myocardial infarction with pre-diabetes and diabetes. The KORA Myocardial Infarction Registry
D Ziegler, W Rathmann, C Meisinger, T Dickhaus, A Mielck, ...
European Journal of Pain 13 (6), 582-587, 2009
Optimizing event-related potential based brain–computer interfaces: a systematic evaluation of dynamic stopping methods
M Schreuder, J Höhne, B Blankertz, S Haufe, T Dickhaus, M Tangermann
Journal of neural engineering 10 (3), 036025, 2013
Dependency and false discovery rate: asymptotics
H Finner, T Dickhaus, M Roters
On optimal channel configurations for SMR-based brain–computer interfaces
C Sannelli, T Dickhaus, S Halder, EM Hammer, KR Müller, B Blankertz
Brain topography 23, 186-193, 2010
Combining multiple hypothesis testing with machine learning increases the statistical power of genome-wide association studies
B Mieth, M Kloft, JA Rodríguez, S Sonnenburg, R Vobruba, ...
Scientific reports 6 (1), 36671, 2016
Basics of modern mathematical statistics
V Spokoiny, T Dickhaus
Springer, 2015
How to analyze many contingency tables simultaneously in genetic association studies
T Dickhaus, K Straßburger, D Schunk, C Morcillo-Suarez, T Illig, ...
Statistical applications in genetics and molecular biology 11 (4), 2012
The allele distribution in next-generation sequencing data sets is accurately described as the result of a stochastic branching process
V Heinrich, J Stange, T Dickhaus, P Imkeller, U Krüger, S Bauer, ...
Nucleic acids research 40 (6), 2426-2431, 2012
Differences in trends in estimated incidence of myocardial infarction in non-diabetic and diabetic people: Monitoring Trends and Determinants on Cardiovascular Diseases (MONICA …
A Icks, T Dickhaus, A Hörmann, M Heier, G Giani, B Kuch, C Meisinger
Diabetologia 52 (9), 1836-1841, 2009
Detecting Mental States by Machine Learning Techniques: The Berlin Brain–Computer Interface
B Blankertz, M Tangermann, C Vidaurre, T Dickhaus, C Sannelli, ...
Brain-Computer Interfaces, 113-135, 2010
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