Deep feature learning for EEG recordings S Stober, A Sternin, AM Owen, JA Grahn arXiv preprint arXiv:1511.04306, 2015 | 184 | 2015 |
Transfer learning for speech recognition on a budget J Kunze, L Kirsch, I Kurenkov, A Krug, J Johannsmeier, S Stober arXiv preprint arXiv:1706.00290, 2017 | 177 | 2017 |
Using convolutional neural networks to recognize rhythm stimuli from electroencephalography recordings S Stober, DJ Cameron, JA Grahn Advances in neural information processing systems 27, 2014 | 135 | 2014 |
Automatic prostate and prostate zones segmentation of magnetic resonance images using DenseNet-like U-net N Aldoj, F Biavati, F Michallek, S Stober, M Dewey Scientific reports 10 (1), 14315, 2020 | 111 | 2020 |
Deep learning based on event-related EEG differentiates children with ADHD from healthy controls A Vahid, A Bluschke, V Roessner, S Stober, C Beste Journal of clinical medicine 8 (7), 1055, 2019 | 86 | 2019 |
Moving beyond ERP components: a selective review of approaches to integrate EEG and behavior DA Bridwell, JF Cavanagh, AGE Collins, MD Nunez, R Srinivasan, ... Frontiers in human neuroscience 12, 106, 2018 | 83 | 2018 |
Designing gaze-supported multimodal interactions for the exploration of large image collections S Stellmach, S Stober, A Nürnberger, R Dachselt Proceedings of the 1st conference on novel gaze-controlled applications, 1-8, 2011 | 75 | 2011 |
Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control A Vahid, M Mückschel, S Stober, AK Stock, C Beste Communications biology 3 (1), 112, 2020 | 71 | 2020 |
Towards Music Imagery Information Retrieval: Introducing the OpenMIIR Dataset of EEG Recordings from Music Perception and Imagination. S Stober, A Sternin, AM Owen, JA Grahn ISMIR, 763-769, 2015 | 56 | 2015 |
Classifying EEG Recordings of Rhythm Perception. S Stober, DJ Cameron, JA Grahn ISMIR, 649-654, 2014 | 44 | 2014 |
Towards Query by Singing/Humming on Audio Databases. A Duda, A Nürnberger, S Stober ISMIR, 331-334, 2007 | 44 | 2007 |
Exploration of interpretability techniques for deep covid-19 classification using chest x-ray images S Chatterjee, F Saad, C Sarasaen, S Ghosh, V Krug, R Khatun, R Mishra, ... Journal of imaging 10 (2), 2024 | 38 | 2024 |
The hubness phenomenon: Fact or artifact? T Low, C Borgelt, S Stober, A Nürnberger Towards Advanced Data Analysis by Combining Soft Computing and Statistics …, 2013 | 37 | 2013 |
Learning discriminative features from electroencephalography recordings by encoding similarity constraints S Stober 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 35 | 2017 |
Musicgalaxy: A multi-focus zoomable interface for multi-facet exploration of music collections S Stober, A Nürnberger Exploring Music Contents: 7th International Symposium, CMMR 2010, Málaga …, 2011 | 34 | 2011 |
Adaptive music retrieval–a state of the art S Stober, A Nürnberger Multimedia Tools and Applications 65, 467-494, 2013 | 33 | 2013 |
MusicGalaxy–an adaptive user-interface for exploratory music retrieval S Stober, A Nürnberger Proc. of 7th Sound and Music Computing conference (SMC’10), 2010 | 27 | 2010 |
Towards user-adaptive structuring and organization of music collections S Stober, A Nürnberger International Workshop on Adaptive Multimedia Retrieval, 53-65, 2008 | 27 | 2008 |
Prednet and predictive coding: A critical review RP Rane, E Szügyi, V Saxena, A Ofner, S Stober Proceedings of the 2020 international conference on multimedia retrieval …, 2020 | 25 | 2020 |
Neuron activation profiles for interpreting convolutional speech recognition models A Krug, R Knaebel, S Stober NeurIPS Workshop on Interpretability and Robustness in Audio, Speech, and …, 2018 | 25 | 2018 |