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 | 1293 | 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 | 724 | 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 | 408 | 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 | 401 | 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 | 295 | 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 | 236 | 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 | 144 | 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 | 142 | 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 | 118 | 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 | 108 | 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 | 105 | 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 | 98 | 2013 |
Dimensionality reduction for the analysis of brain oscillations S Haufe, S Dähne, VV Nikulin NeuroImage 101, 583-597, 2014 | 89 | 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 | 80 | 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 | 77 | 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 | 48 | 2015 |
Patternnet and patternlrp–improving the interpretability of neural networks PJ Kindermans, KT Schütt, M Alber, KR Müller, S Dähne arXiv preprint arXiv:1705.05598 3, 2017 | 45 | 2017 |