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  - Kawanabe, Motoaki
A1  - Müller, Klaus-Robert
T1  - Estimating functions for blind separation when sources have variance dependencies
N2  - A blind separation problem where the sources are not independent, but have variance dependencies is discussed. For this scenario Hyvarinen and Hurri (2004) proposed an algorithm which requires no assumption on distributions of sources and no parametric model of dependencies between components. In this paper, we extend the semiparametric approach of Amari and Cardoso (1997) to variance dependencies and study estimating functions for blind separation of such dependent sources. In particular, we show that many ICA algorithms are applicable to the variance-dependent model as well under mild conditions, although they should in principle not. Our results indicate that separation can be done based only on normalized sources which are adjusted to have stationary variances and is not affected by the dependent activity levels. We also study the asymptotic distribution of the quasi maximum likelihood method and the stability of the natural gradient learning in detail. Simulation results of artificial and realistic examples match well with our theoretical findings
Y1  - 2005
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  -