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 - 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 - Laub, Julian A1 - Roth, Volker A1 - Buhmann, Joachim A1 - Müller, Klaus-Robert T1 - On the information and representation of non-Euclidean pairwise data N2 - Two common data representations are mostly used in intelligent data analysis, namely the vectorial and the pairwise representation. Pairwise data which satisfy the restrictive conditions of Euclidean spaces can be faithfully translated into a Euclidean vectorial representation by embedding. Non-metric pairwise data with violations of symmetry, reflexivity or triangle inequality pose a substantial conceptual problem for pattern recognition since the amount of predictive structural information beyond what can be measured by embeddings is unclear. We show by systematic modeling of non-Euclidean pairwise data that there exists metric violations which can carry valuable problem specific information. Furthermore, Euclidean and non-metric data can be unified on the level of structural information contained in the data. Stable component analysis selects linear subspaces which are particularly insensitive to data fluctuations. Experimental results from different domains support our pattern recognition strategy. Y1 - 2006 UR - http://www.sciencedirect.com/science/journal/00313203 U6 - https://doi.org/10.1016/j.patcog.2006.04.016 SN - 0031-3203 ER - 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 - 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 - 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 -