Felix Biessmann
Felix Biessmann
Einstein Center Digital Future, Berlin University of Applied Sciences
Verified email at - Homepage
Cited by
Cited by
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
Linear and nonlinear regression techniques for simultaneous and proportional myoelectric control
JM Hahne, F Biessmann, N Jiang, H Rehbaum, D Farina, FC Meinecke, ...
Neural Systems and Rehabilitation Engineering, IEEE Transactions on 22 (2 …, 2014
Analysis of Multimodal Neuroimaging Data
F Bießmann, S Plis, FC Meinecke, T Eichele, KR Müller
IEEE Reviews in Biomedical Engineering 4, 26 - 58, 2011
Automating large-scale data quality verification
S Schelter, D Lange, P Schmidt, M Celikel, F Biessmann, A Grafberger
Proceedings of the VLDB Endowment 11 (12), 1781-1794, 2018
On Challenges in Machine Learning Model Management
S Schelter, F Biessmann, T Januschowski, D Salinas, S Seufert, ...
Bulletin of the IEEE Computer Society Technical Committee on Data …, 2018
Decoding Three-Dimensional Trajectory of Executed and Imagined Arm Movements from Electroencephalogram Signals
JH Kim, F Biessmann, SW Lee
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2014
Temporal kernel CCA and its application in multimodal neuronal data analysis
F Bießmann, FC Meinecke, A Gretton, A Rauch, G Rainer, NK Logothetis, ...
Machine Learning 79 (1), 5-27, 2010
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
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
Quantifying Interpretability and Trust in Machine Learning Systems
P Schmidt, F Biessmann
AAAI-19 Workshop on Network Interpretability for Deep Learning, 2019
Transparency and trust in artificial intelligence systems
P Schmidt, F Biessmann, T Teubner
Journal of Decision Systems 29 (4), 260-278, 2020
Effects of stimulus type and of error-correcting code design on BCI speller performance
J Hill, J Farquhar, S Martens, F Bießmann, B Schölkopf
Advances in neural information processing systems 21, 2008
Deep Learning for Missing Value Imputation in Tables with Non-Numerical Data
F Biessmann, D Salinas, S Schelter, P Schmidt, D Lange
Proceedings of the 27th ACM International Conference on Information and …, 2018
DataWig: Missing Value Imputation for Tables.
F Biessmann, T Rukat, P Schmidt, P Naidu, S Schelter, A Taptunov, ...
J. Mach. Learn. Res. 20 (175), 1-6, 2019
Regularized linear discriminant analysis of EEG features in dementia patients
E Neto, F Biessmann, H Aurlien, H Nordby, T Eichele
Frontiers in aging neuroscience 8, 273, 2016
Stereoscopic depth increases intersubject correlations of brain networks
M Gaebler, F Biessmann, JP Lamke, KR Mueller, H Walter, S Hetzer
Neuroimage, 2014
Simultaneous and proportional control of 2D wrist movements with myoelectric signals
JM Hahne, H Rehbaum, F Biessmann, FC Meinecke, KR Müller, N Jiang, ...
2012 IEEE international workshop on machine learning for signal processing, 1-6, 2012
Integration of multivariate data streams with bandpower signals
S Dähne, F Biessmann, FC Meinecke, J Mehnert, S Fazli, KR Müller
IEEE Transactions on Multimedia 15 (5), 1001-1013, 2013
Learning to validate the predictions of black box classifiers on unseen data
S Schelter, T Rukat, F Biessmann
Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020
Functional and laminar dissociations between muscarinic and nicotinic cholinergic neuromodulation in the tree shrew primary visual cortex
A Bhattacharyya, F Bießmann, J Veit, R Kretz, G Rainer
European Journal of Neuroscience 35 (8), 1270-1280, 2012
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