@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{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{ParraSpenceSajdaetal.2000,
  author    = {Parra, L. and Spence, C. and Sajda, P. and Ziehe, Andreas and M{\"u}ller, Klaus-Robert},
  title     = {Unmixing hyperspectral data},
  year      = {2000},
  language  = {en}
}
@article{WuebbelerZieheMackertetal.2000,
  author    = {W{\"u}bbeler, G. and Ziehe, Andreas and Mackert, B.-M. and M{\"u}ller, Klaus-Robert and Trahms, L. and Curio, Gabriel},
  title     = {Independent component analysis of noninvasively recorded cortical magnetic DC-fields in humans},
  year      = {2000},
  language  = {en}
}
@phdthesis{Ziehe2005,
  author    = {Ziehe, Andreas},
  title     = {Blind source separation based on joint diagonalization of matrices with applications in biomedical signal processing},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-5694},
  school      = {Universit{\"a}t Potsdam},
  year      = {2005},
  abstract  = {This thesis is concerned with the solution of the blind source separation problem (BSS). The BSS problem occurs frequently in various scientific and technical applications. In essence, it consists in separating meaningful underlying components out of a mixture of a multitude of superimposed signals. In the recent research literature there are two related approaches to the BSS problem: The first is known as Independent Component Analysis (ICA), where the goal is to transform the data such that the components become as independent as possible. The second is based on the notion of diagonality of certain characteristic matrices derived from the data. Here the goal is to transform the matrices such that they become as diagonal as possible. In this thesis we study the latter method of approximate joint diagonalization (AJD) to achieve a solution of the BSS problem. After an introduction to the general setting, the thesis provides an overview on particular choices for the set of target matrices that can be used for BSS by joint diagonalization. As the main contribution of the thesis, new algorithms for approximate joint diagonalization of several matrices with non-orthogonal transformations are developed. These newly developed algorithms will be tested on synthetic benchmark datasets and compared to other previous diagonalization algorithms. Applications of the BSS methods to biomedical signal processing are discussed and exemplified with real-life data sets of multi-channel biomagnetic recordings.},
  subject      = {Signaltrennung},
  language  = {en}
}
@article{ZieheKawanabeHarmeling2004,
  author    = {Ziehe, Andreas and Kawanabe, Motoaki and Harmeling, Stefan},
  title     = {Blind separation of post-nonlinear mixtures using linearizing transformations and temporal decorrelation},
  issn      = {1532-4435},
  year      = {2004},
  abstract  = {We propose two methods that reduce the post-nonlinear blind source separation problem (PNL-BSS) to a linear BSS problem. The first method is based on the concept of maximal correlation: we apply the alternating conditional expectation (ACE) algorithm-a powerful technique from nonparametric statistics-to approximately invert the componentwise nonlinear functions. The second method is a Gaussianizing transformation, which is motivated by the fact that linearly mixed signals before nonlinear transformation are approximately Gaussian distributed. This heuristic, but simple and efficient procedure works as good as the ACE method. Using the framework provided by ACE, convergence can be proven. The optimal transformations obtained by ACE coincide with the sought-after inverse functions of the nonlinearitics. After equalizing the nonlinearities, temporal decorrelation separation (TDSEP) allows us to recover the source signals. Numerical simulations testing "ACE-TD" and "Gauss-TD" on realistic examples are performed with excellent results},
  language  = {en}
}
@article{ZieheMuellerNolteetal.2000,
  author    = {Ziehe, Andreas and M{\"u}ller, Klaus-Robert and Nolte, G. and Mackert, B.-M. and Curio, Gabriel},
  title     = {Artifact reduction in magnetoneurography based on time-delayed second-order correlations},
  year      = {2000},
  language  = {en}
}