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 - 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 - TY - JOUR A1 - Blankertz, Benjamin A1 - Müller, Klaus-Robert A1 - Krusienski, Dean A1 - Schalk, Gerwin A1 - Wolpaw, Jonathan R. A1 - Schlögl, Alois A1 - Pfurtscheller, Gert A1 - Millan, José del R. A1 - Schröder, Michael A1 - Birbaumer, Niels T1 - The BCI competition III : validating alternative approaches to actual BCI problems N2 - A brain-computer interface (BCI) is a system that allows its users to control external devices with brain activity. Although the proof-of-concept was given decades ago, the reliable translation of user intent into device control commands is still a major challenge. Success requires the effective interaction of two adaptive controllers: the user's brain, which produces brain activity that encodes intent, and the BCI system, which translates that activity into device control commands. In order to facilitate this interaction, many laboratories are exploring a variety of signal analysis techniques to improve the adaptation of the BCI system to the user. In the literature, many machine learning and pattern classification algorithms have been reported to give impressive results when applied to BCI data in offline analyses. However, it is more difficult to evaluate their relative value for actual online use. BCI data competitions have been organized to provide objective formal evaluations of alternative methods. Prompted by the great interest in the first two BCI Competitions, we organized the third BCI Competition to address several of the most difficult and important analysis problems in BCI research. The paper describes the data sets that were provided to the competitors and gives an overview of the results. Y1 - 2006 UR - http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7333 U6 - https://doi.org/10.1109/Tnsre.2006.875642 SN - 1534-4320 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 - 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 -