TY - JOUR A1 - Kawanabe, Motoaki A1 - Blanchard, Gilles A1 - Sugiyama, Masashi A1 - Spokoiny, Vladimir G. A1 - Müller, Klaus-Robert T1 - A novel dimension reduction procedure for searching non-Gaussian subspaces N2 - In this article, we consider high-dimensional data which contains a low-dimensional non-Gaussian structure contaminated with Gaussian noise and propose a new linear method to identify the non-Gaussian subspace. Our method NGCA (Non-Gaussian Component Analysis) is based on a very general semi-parametric framework and has a theoretical guarantee that the estimation error of finding the non-Gaussian components tends to zero at a parametric rate. NGCA can be used not only as preprocessing for ICA, but also for extracting and visualizing more general structures like clusters. A numerical study demonstrates the usefulness of our method Y1 - 2006 UR - http://www.springerlink.com/content/105633/ U6 - https://doi.org/10.1007/11679363_19 SN - 0302-9743 ER - TY - JOUR A1 - Onoda, T. A1 - Rätsch, Gunnar A1 - Müller, Klaus-Robert T1 - An asymptotic analysis and improvement of AdaBoost in the binary classification case (in Japanese) Y1 - 2000 ER - TY - JOUR A1 - Montavon, Gregoire A1 - Braun, Mikio L. A1 - Krüger, Tammo A1 - Müller, Klaus-Robert T1 - Analyzing local structure in Kernel-Based learning JF - IEEE signal processing magazine Y1 - 2013 U6 - https://doi.org/10.1109/MSP.2013.2249294 SN - 1053-5888 VL - 30 IS - 4 SP - 62 EP - 74 PB - Inst. of Electr. and Electronics Engineers CY - Piscataway ER - TY - JOUR A1 - Ziehe, Andreas A1 - Müller, Klaus-Robert A1 - Nolte, G. A1 - Mackert, B.-M. A1 - Curio, Gabriel T1 - Artifact reduction in magnetoneurography based on time-delayed second-order correlations Y1 - 2000 ER - TY - JOUR A1 - Müller, Klaus-Robert A1 - Vigario, R. A1 - Meinecke, Frank C. A1 - Ziehe, Andreas T1 - Blind source separation techniques for decomposing event-related brain signals N2 - Recently blind source separation (BSS) methods have been highly successful when applied to biomedical data. This paper reviews the concept of BSS and demonstrates its usefulness in the context of event-related MEG measurements. In a first experiment we apply BSS to artifact identification of raw MEG data and discuss how the quality of the resulting independent component projections can be evaluated. The second part of our study considers averaged data of event-related magnetic fields. Here, it is particularly important to monitor and thus avoid possible overfitting due to limited sample size. A stability assessment of the BSS decomposition allows to solve this task and an additional grouping of the BSS components reveals interesting structure, that could ultimately be used for gaining a better physiological modeling of the data Y1 - 2004 SN - 0218-1274 ER - TY - JOUR A1 - Dornhege, Guido A1 - Blankertz, Benjamin A1 - Curio, Gabriel A1 - Müller, Klaus-Robert T1 - Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms N2 - Noninvasive electroencephalogram (EEG) recordings provide for easy and safe access to human neocortical processes which can be exploited for a brain-computer interface (BCI). At present, however, the use of BCIs is severely limited by low bit-transfer rates. We systematically analyze and develop two recent concepts, both capable of enhancing the information gain from multichannel scalp EEG recordings: 1) the combination of classifiers, each specifically tailored for different physiological phenomena, e.g., slow cortical potential shifts, such as the premovement Bereitschaftspotential or differences in spatio-spectral distributions of brain activity (i.e., focal event-related desynchronizations) and 2) behavioral paradigms inducing the subjects to generate one out of several brain states (multiclass approach) which all bare a distinctive spatio-temporal signature well discriminable in the standard scalp EEG. We derive information-theoretic predictions and demonstrate their relevance in experimental data. We will show that a suitably arranged interaction between these concepts can significantly boost BCI performances Y1 - 2004 ER - TY - JOUR A1 - Dornhege, Guido A1 - Blankertz, Benjamin A1 - Krauledat, Matthias A1 - Losch, Florian A1 - Curio, Gabriel A1 - Müller, Klaus-Robert T1 - Combined optimization of spatial and temporal filters for improving brain-computer interfacing JF - IEEE transactions on bio-medical electronics N2 - Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output de ice by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability rates of multichannel EEG single-trials. The evaluation of 60 experiments involving 22 different subjects demonstrates the significant superiority of the proposed algorithm over to its classical counterpart: the median classification error rate was decreased by 11%. Apart from the enhanced classification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms. KW - brain-computer interface KW - common spatial patterns KW - EEG KW - event-related desynchronization KW - single-trial-analysis Y1 - 2006 U6 - https://doi.org/10.1109/TBME.2006.883649 SN - 0018-9294 VL - 53 IS - 11 SP - 2274 EP - 2281 PB - IEEE CY - New York ER - TY - JOUR A1 - Zien, Alexander A1 - Rätsch, Gunnar A1 - Mika, Sebastian A1 - Schölkopf, Bernhard A1 - Lengauer, Thomas A1 - Müller, Klaus-Robert T1 - Engineering support vector machine kernels that recognize translation initiation sites Y1 - 2000 SN - 1367-4803 ER - TY - JOUR A1 - Lemm, Steven A1 - Curio, Gabriel A1 - Hlushchuk, Yevhen A1 - Müller, Klaus-Robert T1 - Enhancing the signal-to-noise ratio of ICA-based extracted ERPs N2 - When decomposing single trial electroencephalography it is a challenge to incorporate prior physiological knowledge. Here, we develop a method that uses prior information about the phase-locking property of event-related potentials in a regularization framework to bias a blind source separation algorithm toward an improved separation of single-trial phase-locked responses in terms of an increased signal-to-noise ratio. In particular, we suggest a transformation of the data, using weighted average of the single trial and trial-averaged response, that redirects the focus of source separation methods onto the subspace of event-related potentials. The practical benefit with respect to an improved separation of such components from ongoing background activity and extraneous noise is first illustrated on artificial data and finally verified in a real-world application of extracting single-trial somatosensory evoked potentials from multichannel EEG-recordings Y1 - 2006 UR - http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=10 U6 - https://doi.org/10.1109/Tbme.2006.870258 SN - 0018-9294 ER - TY - JOUR A1 - Harmeling, Stefan A1 - Dornhege, Guido A1 - Tax, David A1 - Meinecke, Frank C. A1 - Müller, Klaus-Robert T1 - From outliers to prototypes : Ordering data N2 - We propose simple and fast methods based on nearest neighbors that order objects from high-dimensional data sets from typical points to untypical points. On the one hand, we show that these easy-to-compute orderings allow us to detect outliers (i.e. very untypical points) with a performance comparable to or better than other often much more sophisticated methods. On the other hand, we show how to use these orderings to detect prototypes (very typical points) which facilitate exploratory data analysis algorithms such as noisy nonlinear dimensionality reduction and clustering. Comprehensive experiments demonstrate the validity of our approach. Y1 - 2006 UR - http://www.sciencedirect.com/science/journal/09252312 U6 - https://doi.org/10.1016/j.neucom.2005.05.015 SN - 0925-2312 ER -