Felix Biessmann
Felix Biessmann
Einstein Center Digital Future, Beuth Hochschule für Technik
Verified email at beuth-hochschule.de - 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
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 (1-2), 5-27, 2010
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
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
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
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
Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance.
NJ Hill, J Farquhar, S Martens, F Bießmann, B Schölkopf
NIPS, 665-672, 2008
Quantifying Interpretability and Trust in Machine Learning Systems
P Schmidt, F Biessmann
AAAI-19 Workshop on Network Interpretability for Deep Learning, 2019
Stereoscopic depth increases intersubject correlations of brain networks
M Gaebler, F Biessmann, JP Lamke, KR Mueller, H Walter, S Hetzer
Neuroimage, 2014
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
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
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
DataWig: Missing Value Imputation for Tables.
F Biessmann, T Rukat, P Schmidt, P Naidu, S Schelter, A Taptunov, ...
J. Mach. Learn. Res. 20, 175:1-175:6, 2019
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
Transparency and trust in artificial intelligence systems
P Schmidt, F Biessmann, T Teubner
Journal of Decision Systems 29 (4), 260-278, 2020
Improved decoding of neural activity from fMRI signals using non-separable spatiotemporal deconvolutions
F Bießmann, Y Murayama, NK Logothetis, KR Müller, FC Meinecke
NeuroImage 61 (4), 1031-1042, 2012
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