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Antons, J.-N., Schleicher, R., Wolf, I., Porbadnigk, A.K., Blankertz, B., Möller, S., and Curio, G. (2010). Neural Correlates of Speech Degradation – Subjective Ratings and Brain Activation in Case of Signal-correlated Noise. In Proc. of the 3rd Int’l Workshop on Perceptual Quality of Systems


Bießmann, F., Papaioannou, J.M., Harth, A., Jugel, M.L., Müller, K.-R., and Braun, M. (2012). Quantifying spatiotemporal dynamics of twitter replies to news feeds. Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing

Bießmann, F., Plis, S.M., Meinecke, F.C., Eichele, T., and Müller, K.R. (2011). Analysis of Multimodal Neuroimaging Data. IEEE Reviews in Biomedical Engineering, 26-58.

Bießmann, F., Murayama, Y., Logothetis, N.K., Müller, K.R., and Meinecke, F.C. (2011). Non-Separable Spatiotemporal Deconvolutions Improve Decoding of Neural Activity from fMRI Signals. NIPS Workshop "Machine Learning and Interpretation in Neuroimaging"

Bießmann, F., and Harth, A. (2010). Analysing Dependency Dynamics in Web Data. Proceedings of AAAI Spring Symposium "Linked Data Meets Artificial Intelligence"

Bießmann, F., Dähne, S., Meinecke, F.C., Blankertz, B., Görgen, K., Müller, K.-R., and Haufe, S. (2012). On the interpretability of linear multivariate neuroimaging analyses: Filters, patterns and their relationship. NIPS Workshop on Machine Learning and Inference in Neuroimaging

Bodiroža, S., Stern, H.I., and Edan, Y. (2012). Dynamic gesture vocabulary design for intuitive human-robot dialog. Proceedings of the 2012 7th ACM/IEEE International Conference on Human-Robot Interaction, 111-12.

Bodiroža, S., Doisy, G., and Hafner, V.V. (2013). Position-invariant, real-time gesture recognition based on dynamic time warping. Proceedings of the 2013 8th ACM/IEEE International Conference on Human-Robot Interaction, 87-8.



D'Albis, T., Haenicke, J., Strube-Bloss, M.F., Schmuker, M., Menzel, R., and Nawrot, M.P. (2011). Learning-induced changes at the single neuron level predict behavioral performance in the honeybee. Bernstein Conference proceedings [T24]

Dähne, S., Höhne, J., Schreuder, M., and Tangermann, M. (2011). Slow feature analysis - A tool for extraction of discriminating event-related potentials in brain-computer interfaces. Artificial Neural Networks and Machine Learning ICANN, 36-43.

Dähne, S., Höhne, J., and Tangermann, M. (2011). Adaptive classification improves control performance in ERP-based BCIs. Proceedings of the 5th International BCI Conference 2011, 92-95.

Dähne, S., Höhne, J., Haufe, S., Meinecke, F., Tangermann, M., Nikulin, V., and Müller, K.-R. (2012). Multi-variate correlation of power spectral density. In Proceedings of the HBM Conference 2012, Beijing, China, June 2012

Dähne, S., Wilbert, N., and Wiskott, L. (2009). Learning Complex Cell Units from Simulated Prenatal Retinal Waves with Slow Feature Analysis. BMC Neuroscience 2009, P129.

Doisy, G., Bodiroža, S., and Jetvić, A. (2013). Spatially unconstrained, gesture-based humanrobot interaction. Proceedings of the 2013 8th ACM/IEEE International Conference on Human-Robot Interaction, 117-118.

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