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  - 
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  - 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  - 
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  - 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  - 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  - 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  - 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  - 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  - 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  - 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  -