Sven Dähne

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Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
Benjamin BlankertzTechnische Universität Berlin, Neurotechnology groupVerified email at tu-berlin.de
Stefan HaufeTechnische Universität Berlin, Physikalisch-Technische Bundesanstalt, Charité - UniversitätsmedizinVerified email at tu-berlin.de
Pieter-Jan KindermansStaff Research Scientist, Google DeepmindVerified email at google.com
Felix BiessmannEinstein Center Digital Future, Berlin University of Applied SciencesVerified email at bht-berlin.de
Frank C. MeineckeDirector of Applied Machine Learning, TomTomVerified email at tomtom.com
Kristof T. SchüttPfizer, Machine Learning ResearchVerified email at pfizer.com
Michael TangermannDonders Insitute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, andVerified email at donders.ru.nl
Maximilian AlberTU BerlinVerified email at tu-berlin.de
Kai GörgenBCCN Berlin, Science of Intelligence, Charite University Hospital BerlinVerified email at bccn-berlin.de
John-Dylan HaynesProfessor for Theory and Analysis of Large-Scale Brain Signals, Charite - Universitätsmedizin Berlin, GermanyVerified email at bccn-berlin.de
Johannes HöhneData Science @ BayerVerified email at bayer.com
Wojciech SamekProfessor at TU Berlin, Head of AI Department at Fraunhofer HHI, BIFOLD FellowVerified email at tu-berlin.de
Matthias Schultze-KraftPostDoc, Bernstein Center for Computational Neuroscience, BerlinVerified email at bccn-berlin.de
Vadim V NikulinPrincipal Investigator, Max Planck Institute for Human Cognitive and Brain SciencesVerified email at cbs.mpg.de
Dumitru ErhanDirector of Research @ Google DeepMindVerified email at google.com
Been KimGoogle DeepMindVerified email at csail.mit.edu
Martijn SchreuderANT NeuroVerified email at ant-neuro.com
Siamac FazliNazarbayev UniversityVerified email at nu.edu.kz
Han-Jeong HwangAssociate Professor, Dept of Electronics and Information Engineering, Korea University, SejongVerified email at korea.ac.kr

Sven Dähne

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machine learning data science neuroimaging neuroscience


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On the interpretation of weight vectors of linear models in multivariate neuroimaging S Haufe, F Meinecke, K Görgen, S Dähne, JD Haynes, B Blankertz, ... Neuroimage 87, 96-110, 2014	1412	2014
The (un) reliability of saliency methods PJ Kindermans, S Hooker, J Adebayo, M Alber, KT Schütt, S Dähne, ... Explainable AI: Interpreting, explaining and visualizing deep learning, 267-280, 2019	845	2019
iNNvestigate neural networks! M Alber, S Lapuschkin, P Seegerer, M Hägele, KT Schütt, G Montavon, ... Journal of machine learning research 20 (93), 1-8, 2019	460	2019
Learning how to explain neural networks: Patternnet and patternattribution PJ Kindermans, KT Schütt, M Alber, KR Müller, D Erhan, B Kim, S Dähne arXiv preprint arXiv:1705.05598, 2017	425	2017
The point of no return in vetoing self-initiated movements M Schultze-Kraft, D Birman, M Rusconi, C Allefeld, K Görgen, S Dähne, ... Proceedings of the national Academy of Sciences 113 (4), 1080-1085, 2016	307	2016
The Berlin brain-computer interface: progress beyond communication and control B Blankertz, L Acqualagna, S Dähne, S Haufe, M Schultze-Kraft, I Sturm, ... Frontiers in neuroscience 10, 530, 2016	255	2016
Investigating the influence of noise and distractors on the interpretation of neural networks PJ Kindermans, K Schütt, KR Müller, S Dähne arXiv preprint arXiv:1611.07270, 2016	155	2016
SPoC: A novel framework for relating the amplitude of neuronal oscillations to behaviorally relevant parameters S Dähne, FC Meinecke, S Haufe, J Höhne, M Tangermann, KR Müller, ... NeuroImage 86, 111-122, 2014	153	2014
Effect of higher frequency on the classification of steady-state visual evoked potentials DO Won, HJ Hwang, S Dähne, KR Müller, SW Lee Journal of neural engineering 13 (1), 016014, 2015	123	2015
Concurrent Adaptation of Human and Machine Improves Simultaneous and Proportional Myoelectric Control J Hahne, S Dähne, HJ Hwang, KR Müller, L Parra IEEE Transactions on Neural Systems and Rehabilitation Engineering 23 (4 …, 2015	117	2015
Learning From More Than One Data Source: Data Fusion Techniques for Sensorimotor Rhythm-Based Brain–Computer Interfaces S Fazli, S Dähne, W Samek, F Bießmann, KR Müller Proceedings of the IEEE 103 (6), 891 - 906, 2015	112	2015
Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data S Dähne, F Bießmann, W Samek, S Haufe, D Goltz, C Gundlach, ... Proceedings of the IEEE 103 (9), 1507 - 1530, 2015	110	2015
User-centered design in brain–computer interfaces—A case study M Schreuder, A Riccio, M Risetti, S Dähne, A Ramsay, J Williamson, ... Artificial intelligence in medicine 59 (2), 71-80, 2013	100	2013
Dimensionality reduction for the analysis of brain oscillations S Haufe, S Dähne, VV Nikulin NeuroImage 101, 583-597, 2014	97	2014
Pyff–a pythonic framework for feedback applications and stimulus presentation in neuroscience B Venthur, S Scholler, J Williamson, S Dähne, MS Treder, MT Kramarek, ... Frontiers in Neuroscience 4, 2010	82	2010
Natural stimuli improve auditory BCIs with respect to ergonomics and performance J Höhne, K Krenzlin, S Dähne, M Tangermann Journal of Neural Engineering 9 (4), 045003, 2012	79	2012
EEG predictors of covert vigilant attention A Martel, S Dähne, B Blankertz Journal of neural engineering 11 (3), 035009, 2014	75	2014
Multi-variate EEG analysis as a novel tool to examine brain responses to naturalistic music stimuli I Sturm, S Dähne, B Blankertz, G Curio PloS one 10 (10), e0141281, 2015	56	2015
Finding brain oscillations with power dependencies in neuroimaging data S Dähne, VV Nikulin, D Ramírez, PJ Schreier, KR Müller, S Haufe NeuroImage 96, 334-348, 2014	56	2014
Unsupervised classification of operator workload from brain signals M Schultze-Kraft, S Dähne, M Gugler, G Curio, B Blankertz Journal of neural engineering 13 (3), 036008, 2016	47	2016

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