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 - Wübbeler, G. A1 - Ziehe, Andreas A1 - Mackert, B.-M. A1 - Müller, Klaus-Robert A1 - Trahms, L. A1 - Curio, Gabriel T1 - Independent component analysis of noninvasively recorded cortical magnetic DC-fields in humans Y1 - 2000 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 - Rätsch, Gunnar A1 - Schölkopf, B. A1 - Smola, Alexander J. A1 - Mika, Sebastian A1 - Onoda, T. A1 - Müller, Klaus-Robert T1 - Robust ensemble learning Y1 - 2000 SN - 0-262-19448-1 ER - TY - BOOK A1 - Rätsch, Gunnar A1 - Schölkopf, B. A1 - Mika, Sebastian A1 - Müller, Klaus-Robert T1 - SVM and boosting : one class T3 - GMD-Report Y1 - 2000 VL - 119 PB - GMD-Forschungszentrum Informationstechnik CY - Sankt Augustin ER - TY - BOOK A1 - Tsuda, Koji A1 - Sugiyama, Masashi A1 - Müller, Klaus-Robert T1 - Subspace information criterion for non-quadratice regularizers : model selection for sparse regressors T3 - GMD-Report Y1 - 2000 VL - 120 PB - GMD-Forschungszentrum Informationstechnik CY - Sankt Augustin ER - TY - JOUR A1 - Rätsch, Gunnar A1 - Schölkopf, B. A1 - Smola, Alexander J. A1 - Müller, Klaus-Robert A1 - Mika, Sebastian T1 - V-Arc : ensemble learning in the preence of outliers Y1 - 2000 ER - TY - JOUR A1 - Parra, L. A1 - Spence, C. A1 - Sajda, P. A1 - Ziehe, Andreas A1 - Müller, Klaus-Robert T1 - Unmixing hyperspectral data Y1 - 2000 ER - TY - JOUR A1 - Kohlmorgen, J. A1 - Müller, Klaus-Robert A1 - Rittweger, J. A1 - Pawelzik, K. T1 - Identification of nonstationary dynamics in physiological recordings Y1 - 2000 ER - TY - JOUR A1 - Mika, Sebastian A1 - Rätsch, Gunnar A1 - Weston, J. A1 - Schölkopf, B. A1 - Smola, Alexander J. A1 - Müller, Klaus-Robert T1 - Invariant feature extraction and classification in kernel spaces Y1 - 2000 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 - 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 - Blankertz, Benjamin A1 - Müller, Klaus-Robert A1 - Curio, Gabriel A1 - Vaughan, Theresa M. A1 - Schalk, Gerwin A1 - Wolpaw, Jonathan R. A1 - Schlogl, Alois A1 - Neuper, Christa A1 - Pfurtscheller, Gert A1 - Hinterberger, Thilo A1 - Schroder, Michael A1 - Birbaumer, Niels T1 - The BCI competition 2003 : Progress and perspectives in detection and discrimination of EEG single trials N2 - Interest in developing a new method of man-to-machine communication-a brain-computer interface (BCI)-has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms Y1 - 2004 SN - 0018-9294 ER - TY - JOUR A1 - Harmeling, Stefan A1 - Meinecke, Frank C. A1 - Müller, Klaus-Robert T1 - Injecting noise for analysing the stability of ICA components N2 - Usually, noise is considered to be destructive. We present a new method that constructively injects noise to assess the reliability and the grouping structure of empirical ICA component estimates. Our method can be viewed as a Monte-Carlo-style approximation of the curvature of some performance measure at the solution. Simulations show that the true root-mean-squared angle distances between the real sources and the source estimates can be approximated well by our method. In a toy experiment, we see that we are also able to reveal the underlying grouping structure of the extracted ICA components. Furthermore, an experiment with fetal ECG data demonstrates that our approach is useful for exploratory data analysis of real-world data. (C) 2003 Elsevier B.V. All rights reserved Y1 - 2004 SN - 0165-1684 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 - Sugiyama, Masashi A1 - Kawanabe, Motoaki A1 - Müller, Klaus-Robert T1 - Trading variance reduction with unbiasedness : the regularized subspace information criterion for robust model selection in kernel regression N2 - A well-known result by Stein (1956) shows that in particular situations, biased estimators can yield better parameter estimates than their generally preferred unbiased counterparts. This letter follows the same spirit, as we will stabilize the unbiased generalization error estimates by regularization and finally obtain more robust model selection criteria for learning. We trade a small bias against a larger variance reduction, which has the beneficial effect of being more precise on a single training set. We focus on the subspace information criterion (SIC), which is an unbiased estimator of the expected generalization error measured by the reproducing kernel Hilbert space norm. SIC can be applied to the kernel regression, and it was shown in earlier experiments that a small regularization of SIC has a stabilization effect. However, it remained open how to appropriately determine the degree of regularization in SIC. In this article, we derive an unbiased estimator of the expected squared error, between SIC and the expected generalization error and propose determining the degree of regularization of SIC such that the estimator of the expected squared error is minimized. Computer simulations with artificial and real data sets illustrate that the proposed method works effectively for improving the precision of SIC, especially in the high-noise-level cases. We furthermore compare the proposed method to the original SIC, the cross-validation, and an empirical Bayesian method in ridge parameter selection, with good results Y1 - 2004 SN - 0899-7667 ER - TY - JOUR A1 - Meinecke, Frank C. A1 - Ziehe, Andreas A1 - Kurths, Jürgen A1 - Müller, Klaus-Robert T1 - Measuring phase synchronization of superimposed signals N2 - Phase synchronization is an important phenomenon that occurs in a wide variety of complex oscillatory processes. Measuring phase synchronization can therefore help to gain fundamental insight into nature. In this Letter we point out that synchronization analysis techniques can detect spurious synchronization, if they are fed with a superposition of signals such as in electroencephalography or magnetoencephalography data. We show how techniques from blind source separation can help to nevertheless measure the true synchronization and avoid such pitfalls Y1 - 2005 SN - 0031-9007 ER - TY - JOUR A1 - Meinecke, Frank C. A1 - Harmeling, Stefan A1 - Müller, Klaus-Robert T1 - Inlier-based ICA with an application to superimposed images N2 - This paper proposes a new independent component analysis (ICA) method which is able to unmix overcomplete mixtures of sparce or structured signals like speech, music or images. Furthermore, the method is designed to be robust against outliers, which is a favorable feature for ICA algorithms since most of them are extremely sensitive to outliers. Our approach is based on a simple outlier index. However, instead of robustifying an existing algorithm by some outlier rejection technique we show how this index can be used directly to solve the ICA problem for super-Gaussian sources. The resulting inlier-based ICA (IBICA) is outlier-robust by construction and can be used for standard ICA as well as for overcomplete ICA (i.e. more source signals than observed signals). (c) 2005 Wiley Periodicals, Inc Y1 - 2005 SN - 0899-9457 ER - TY - JOUR A1 - Lemm, Steven A1 - Blankertz, Benjamin A1 - Curio, Gabriel A1 - Müller, Klaus-Robert T1 - Spatio-spectral filters for improving the classification of single trial EEG N2 - Data recorded in electroencephalogram (EEG)-based brain-computer interface experiments is generally very noisy, non-stationary, and contaminated with artifacts that can deteriorate discrimination/classification methods. In this paper, we extend the common spatial pattern (CSP) algorithm with the aim to alleviate these adverse effects. In particular, we suggest an extension of CSP to the state space, which utilizes the method of time delay embedding. As we will show, this allows for individually tuned frequency filters at each electrode position and, thus, yields an improved and more robust machine learning procedure. The advantages of the proposed method over the original CSP method are verified in terms of an improved information transfer rate (bits per trial) on a set of EEG-recordings from experiments of imagined limb movements Y1 - 2005 SN - 0018-9294 ER - TY - JOUR A1 - Laskov, Pavel A1 - Gehl, Christian A1 - Krüger, Stefan A1 - Müller, Klaus-Robert T1 - Incremental support vector learning: analysis, implementation and applications JF - Journal of machine learning research N2 - Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM learning, with the aim of providing a fast, numerically stable and robust implementation. A detailed analysis of convergence and of algorithmic complexity of incremental SVM learning is carried out. Based on this analysis, a new design of storage and numerical operations is proposed, which speeds up the training of an incremental SVM by a factor of 5 to 20. The performance of the new algorithm is demonstrated in two scenarios: learning with limited resources and active learning. Various applications of the algorithm, such as in drug discovery, online monitoring of industrial devices and and surveillance of network traffic, can be foreseen. KW - incremental SVM KW - online learning KW - drug discovery KW - intrusion detection Y1 - 2006 SN - 1532-4435 VL - 7 SP - 1909 EP - 1936 PB - MIT Press CY - Cambridge, Mass. ER -