Sven Dähne

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Indice H	26	21
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Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyEmail verificata su tu-berlin.de
Benjamin BlankertzTechnische Universität Berlin, Neurotechnology groupEmail verificata su tu-berlin.de
Stefan HaufeTechnische Universität Berlin, Physikalisch-Technische Bundesanstalt, Charité - UniversitätsmedizinEmail verificata su tu-berlin.de
Pieter-Jan KindermansStaff Research Scientist, Google DeepmindEmail verificata su google.com
Felix BiessmannEinstein Center Digital Future, Berlin University of Applied SciencesEmail verificata su bht-berlin.de
Frank C. MeineckeDirector of Applied Machine Learning, TomTomEmail verificata su tomtom.com
Kristof T. SchüttPfizer, Machine Learning ResearchEmail verificata su pfizer.com
Michael TangermannDonders Insitute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, andEmail verificata su donders.ru.nl
Kai GörgenBCCN Berlin, Science of Intelligence, Charite University Hospital BerlinEmail verificata su bccn-berlin.de
Maximilian AlberTU BerlinEmail verificata su tu-berlin.de
John-Dylan HaynesProfessor for Theory and Analysis of Large-Scale Brain Signals, Charite - Universitätsmedizin Berlin, GermanyEmail verificata su bccn-berlin.de
Johannes HöhneData Science @ BayerEmail verificata su bayer.com
Wojciech SamekProfessor at TU Berlin, Head of AI Department at Fraunhofer HHI, BIFOLD FellowEmail verificata su tu-berlin.de
Matthias Schultze-KraftPostDoc, Bernstein Center for Computational Neuroscience, BerlinEmail verificata su bccn-berlin.de
Vadim V NikulinPrincipal Investigator, Max Planck Institute for Human Cognitive and Brain SciencesEmail verificata su cbs.mpg.de
Dumitru ErhanDirector of Research @ Google DeepMindEmail verificata su google.com
Been KimGoogle DeepMindEmail verificata su csail.mit.edu
Martijn SchreuderANT NeuroEmail verificata su ant-neuro.com
Siamac FazliNazarbayev UniversityEmail verificata su nu.edu.kz
Han-Jeong HwangAssociate Professor, Dept of Electronics and Information Engineering, Korea University, SejongEmail verificata su korea.ac.kr

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machine learning data science neuroimaging neuroscience


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
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	1341	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	774	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	431	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	408	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	306	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	246	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	148	2014
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	146	2016
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	122	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	115	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	106	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	106	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	94	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	81	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	78	2012
EEG predictors of covert vigilant attention A Martel, S Dähne, B Blankertz Journal of neural engineering 11 (3), 035009, 2014	75	2014
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	57	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	50	2015
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	46	2016

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