@article{HarmelingDornhegeTaxetal.2006,
  author    = {Harmeling, Stefan and Dornhege, Guido and Tax, David and Meinecke, Frank C. and M{\"u}ller, Klaus-Robert},
  title     = {From outliers to prototypes : Ordering data},
  issn      = {0925-2312},
  doi       = {10.1016/j.neucom.2005.05.015},
  year      = {2006},
  abstract  = {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.},
  language  = {en}
}
@article{MuellerVigarioMeineckeetal.2004,
  author    = {M{\"u}ller, Klaus-Robert and Vigario, R. and Meinecke, Frank C. and Ziehe, Andreas},
  title     = {Blind source separation techniques for decomposing event-related brain signals},
  issn      = {0218-1274},
  year      = {2004},
  abstract  = {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},
  language  = {en}
}
@article{HarmelingMeineckeMueller2004,
  author    = {Harmeling, Stefan and Meinecke, Frank C. and M{\"u}ller, Klaus-Robert},
  title     = {Injecting noise for analysing the stability of ICA components},
  issn      = {0165-1684},
  year      = {2004},
  abstract  = {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},
  language  = {en}
}
@article{MeineckeHarmelingMueller2005,
  author    = {Meinecke, Frank C. and Harmeling, Stefan and M{\"u}ller, Klaus-Robert},
  title     = {Inlier-based ICA with an application to superimposed images},
  issn      = {0899-9457},
  year      = {2005},
  abstract  = {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},
  language  = {en}
}
@article{MeineckeZieheKurthsetal.2005,
  author    = {Meinecke, Frank C. and Ziehe, Andreas and Kurths, J{\"u}rgen and M{\"u}ller, Klaus-Robert},
  title     = {Measuring phase synchronization of superimposed signals},
  issn      = {0031-9007},
  year      = {2005},
  abstract  = {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},
  language  = {en}
}
@article{NolteMeineckeZieheetal.2006,
  author    = {Nolte, Guido and Meinecke, Frank C. and Ziehe, Andreas and M{\"u}ller, Klaus-Robert},
  title     = {Identifying interactions in mixed and noisy complex systems},
  doi       = {10.1103/Physreve.73.051913},
  year      = {2006},
  abstract  = {We present a technique that identifies truly interacting subsystems of a complex system from multichannel data if the recordings are an unknown linear and instantaneous mixture of the true sources. The method is valid for arbitrary noise structure. For this, a blind source separation technique is proposed that diagonalizes antisymmetrized cross- correlation or cross-spectral matrices. The resulting decomposition finds truly interacting subsystems blindly and suppresses any spurious interaction stemming from the mixture. The usefulness of this interacting source analysis is demonstrated in simulations and for real electroencephalography data},
  language  = {en}
}