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Bießmann, F., Hill, N.J., Farquhar, J., and Schölkopf, B. (2007). Better Codes for the P300 Visual Speller [23]. Göttingen Meeting of the German Neuroscience Society

Bießmann, F. (2012). Data-driven analysis for multimodal neuroimaging [24]. Technische Universität Berlin

Bießmann, F., and Harth, A. (2010). Analysing Dependency Dynamics in Web Data [25]. Proceedings of AAAI Spring Symposium "Linked Data Meets Artificial Intelligence"

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 [26]. In Proc. of the 3rd Int’l Workshop on Perceptual Quality of Systems

Susemihl, A.K.,Meir, R., and Opper, M. (2011). Analytical results for the error in filtering of Gaussian processes [27]. Advances in Neural Information Processing Systems 24, 2303-2311.

Samek, W., Müller, K.-R., Kawanabe, M., and Vidaurre, C. (2012). Brain-computer interfacing in discriminative and stationary subspaces [28]. 34th annual international conference of the IEEE engineering in medicine and biology society (EMBS)

Wojcikiewicz, W., Vidaurre, C., and Kawanabe, M. (2011). Improving classification performance of BCIs by using stationary common spatial patterns and unsupervised bias adaptation [29]. Hybrid artificial intelligent systems. Springer Berlin / Heidelberg, 34-41.

Biessmann, F. (2011). Data-driven analysis for multimodal neuroimaging [30]. Technische Universität Berlin

Schoenfelder, V. (2013). Identification of Stimulus Cues in Tone-in-Noise Detection with Sparse Logistic Regression [31]. Technische Universität Berlin

Haeusler, C. (2014). The brain as Data Source - Model - Inspiration [32]. Freie Universität Berlin

Clemens, J. (2012). Neural computation in small sensory systems. Lessons on sparse and adaptive coding [33]. Humboldt-Universität zu Berlin

Görgen, K., Hebart, M.N., and Haynes, J.-D. (2012). The Decoding Toolbox (TDT): An easy-to-use decoding package for fMRI data [34]. Conference of the Human Brain Mapping Organization, Beijing, China

Görgen, K., Reverberi, C., and Haynes, J.-D. (2011). Decoding neural representations of rules and rule order [35]. Conference Proceedings of the IK2011 Meeting, Günne, Germany

Dold, H. (2013). On modelling data from visual psychophysics: A Bayesian graphical model approach [36]. Technische Universität Berlin

Hackmack, K. (2012). Decoding Multiple Sclerosis and Related Disease Parameters Using Structural Brain MRI and Multivariate Analysis Algorithms [37]. BCCN/ Charité Berlin

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