| 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 | 795 | 2010 |
| Introduction to machine learning for brain imaging S Lemm, B Blankertz, T Dickhaus, KR Müller Neuroimage 56 (2), 387-399, 2011 | 794 | 2011 |
| 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 | 625 | 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 | 351 | 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 | 329 | 2012 |
| Simultaneous statistical inference T Dickhaus With applications in the life sciences. Springer, 2014 | 175 | 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 | 152 | 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 | 128 | 2011 |
| On the false discovery rate and an asymptotically optimal rejection curve H Finner, T Dickhaus, M Roters | 123 | 2009 |
| Epigenetic quantification of tumor-infiltrating T-lymphocytes J Sehouli, C Loddenkemper, T Cornu, T Schwachula, U Hoffmüller, ... Epigenetics 6 (2), 236-246, 2011 | 117 | 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 | 105 | 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 | 102 | 2013 |
| Dependency and false discovery rate: asymptotics H Finner, T Dickhaus, M Roters | 102 | 2007 |
| 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 | 87 | 2016 |
| 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 | 87 | 2010 |
| Basics of modern mathematical statistics V Spokoiny, T Dickhaus Springer, 2015 | 75 | 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 | 63 | 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 | 58 | 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 | 51* | 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 | 36 | 2010 |
Benjamin BlankertzTechnische Universität Berlin, Neurotechnology groupVerified email at tu-berlin.de
