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Search Results: 1 - 10 of 1975 matches for " Carsten Allefeld "
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Instantaneous oscillatory direction and phase for multivariate timeseries
Carsten Allefeld
Physics , 2008,
Abstract: This text describes a generalization of the analytic signal (Gabor, 1946) approach for the definition of instantaneous amplitude and phase to the case of multivariate signals. It was originally written as an appendix for another paper, where the determination of the locally dominant oscillatory direction (the instantaneous amplitude) described here is used as a preprocessing step for another kind of data analysis. The text is reproduced in a 'standalone' form because the procedure might prove useful in other contexts too, especially for the purpose of phase synchronization analysis (Rosenblum et al., 1996) between two (or more) multivariate sets of time series (Pascual-Marqui, 2007).
Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains
Carsten Allefeld,Stephan Bialonski
Physics , 2007, DOI: 10.1103/PhysRevE.76.066207
Abstract: Synchronization cluster analysis is an approach to the detection of underlying structures in data sets of multivariate time series, starting from a matrix R of bivariate synchronization indices. A previous method utilized the eigenvectors of R for cluster identification, analogous to several recent attempts at group identification using eigenvectors of the correlation matrix. All of these approaches assumed a one-to-one correspondence of dominant eigenvectors and clusters, which has however been shown to be wrong in important cases. We clarify the usefulness of eigenvalue decomposition for synchronization cluster analysis by translating the problem into the language of stochastic processes, and derive an enhanced clustering method harnessing recent insights from the coarse-graining of finite-state Markov processes. We illustrate the operation of our method using a simulated system of coupled Lorenz oscillators, and we demonstrate its superior performance over the previous approach. Finally we investigate the question of robustness of the algorithm against small sample size, which is important with regard to field applications.
Mental States as Macrostates Emerging from EEG Dynamics
Carsten Allefeld,Harald Atmanspacher,Jiri Wackermann
Physics , 2008, DOI: 10.1063/1.3072788
Abstract: Correlations between psychological and physiological phenomena form the basis for different medical and scientific disciplines, but the nature of this relation has not yet been fully understood. One conceptual option is to understand the mental as "emerging" from neural processes in the specific sense that psychology and physiology provide two different descriptions of the same system. Stating these descriptions in terms of coarser- and finer-grained system states (macro- and microstates), the two descriptions may be equally adequate if the coarse-graining preserves the possibility to obtain a dynamical rule for the system. To test the empirical viability of our approach, we describe an algorithm to obtain a specific form of such a coarse-graining from data, and illustrate its operation using a simulated dynamical system. We then apply the method to an electroencephalographic (EEG) recording, where we are able to identify macrostates from the physiological data that correspond to mental states of the subject.
Searchlight-based multi-voxel pattern analysis of fMRI by cross-validated MANOVA
Carsten Allefeld,John-Dylan Haynes
Quantitative Biology , 2014, DOI: 10.1016/j.neuroimage.2013.11.043
Abstract: Multi-voxel pattern analysis (MVPA) is a fruitful and increasingly popular complement to traditional univariate methods of analyzing neuroimaging data. We propose to replace the standard 'decoding' approach to searchlight-based MVPA, measuring the performance of a classifier by its accuracy, with a method based on the multivariate form of the general linear model. Following the well-established methodology of multivariate analysis of variance (MANOVA), we define a measure that directly characterizes the structure of multi-voxel data, the pattern distinctness $D$. Our measure is related to standard multivariate statistics, but we apply cross-validation to obtain an unbiased estimate of its population value, independent of the amount of data or its partitioning into 'training' and 'test' sets. The estimate $\hat D$ can therefore serve not only as a test statistic, but as an interpretable measure of multivariate effect size. The pattern distinctness generalizes the Mahalanobis distance to an arbitrary number of classes, but also the case where there are no classes of trials because the design is described by parametric regressors. It is defined for arbitrary estimable contrasts, including main effects (pattern differences) and interactions (pattern changes). In this way, our approach makes the full analytical power of complex factorial designs known from univariate fMRI analyses available to MVPA studies. Moreover, we show how the results of a factorial analysis can be used to obtain a measure of pattern stability, the equivalent of 'cross-decoding'.
Eigenvalue Decomposition as a Generalized Synchronization Cluster Analysis
Carsten Allefeld,Markus Müller,Jürgen Kurths
Physics , 2007, DOI: 10.1142/S0218127407019251
Abstract: Motivated by the recent demonstration of its use as a tool for the detection and characterization of phase-shape correlations in multivariate time series, we show that eigenvalue decomposition can also be applied to a matrix of indices of bivariate phase synchronization strength. The resulting method is able to identify clusters of synchronized oscillators, and to quantify their strength as well as the degree of involvement of an oscillator in a cluster. Since for the case of a single cluster the method gives similar results as our previous approach, it can be seen as a generalized Synchronization Cluster Analysis, extending its field of application to more complex situations. The performance of the method is tested by applying it to simulation data.
Valid population inference for information-based imaging: Information prevalence inference
Carsten Allefeld,Kai G?rgen,John-Dylan Haynes
Statistics , 2015,
Abstract: In multivariate pattern analysis of neuroimaging data, 'second-level' inference is often performed by entering classification accuracies into a t-test vs chance level across subjects. We argue that while the random effects analysis implemented by the t-test does provide population inference if applied to activation differences, it fails to do so in the case of classification accuracy or other 'information-like' measures, because the true value of such measures can never be below chance level. This constraint changes the meaning of the population-level null hypothesis being tested, which becomes equivalent to the global null hypothesis that there is no effect in any subject in the population. Consequently, rejecting it only allows to infer that there are some subjects in which there is an information effect, but not that it generalizes. This statement is supported by theoretical arguments as well as simulations. We review possible alternative approaches to population inference for information-based imaging, converging on the idea that it should not target the mean, but the prevalence of the effect in the population. One method to do so, 'permutation-based information prevalence inference using the minimum statistic', is described in detail and applied to empirical data.
Sequential dependencies between trials in free choice tasks
Carsten Allefeld,Chun Siong Soon,Carsten Bogler,Jakob Heinzle,John-Dylan Haynes
Quantitative Biology , 2013,
Abstract: In two previous experiments we investigated the neural precursors of subjects' "free" choices for one of two options (pressing one of two buttons, and choosing between adding and subtracting numbers). In these experiments the distribution of sequence lengths was taken as an approximate indicator of the randomness (or lack of sequential dependency) of the choice sequences. However, this method is limited in its ability to reveal sequential dependencies. Here we present a more detailed individual-subject analysis and conclude that despite of the presence of significant sequential dependencies the subjects' behavior still approximates randomness, as measured by an entropy rate (on pooled data) of 0.940 bit / trial and 0.965 bit / trial in the two experiments. We also provide the raw single-subject behavioral data.
Definitions and Measurement of Social Exclusion
—A Conceptual and Methodological Review
 [PDF]

Carsten Kronborg Bak
Advances in Applied Sociology (AASoci) , 2018, DOI: 10.4236/aasoci.2018.85025
Abstract: Poverty and inequality have long been the dominant categories to describe people’s living conditions, and they continue to reflect significant problems in Danish society, even though the risk of poverty in Denmark is low compared to other European countries. However, during the 1990s, social exclusion, as a “new” concept, has in many ways drawn attention away from poverty. The article raises the question of what social exclusion can contribute with as a concept and in what way it differs from other key concepts, for example, poverty and social capital. The overall aim of the article is to provide a broad overview analysis of a number of key scientific definitions. In addition, selected quantitative and qualitative studies on social exclusion, to problematize the lack of empirical studies of social exclusion using direct measures for social exclusion, are included. Far too often, this results in the raising of questions about to what extent it is social exclusion or other related terms being “measured” in various empirical studies. Mental health is used in the discussion on “measurement” of social exclusion as a critical case to point out shortcomings in existing empirical studies and to inject nuance into the discussion of information from qualitative studies on causal processes behind social exclusion.
Der zukünftige Bedarf an Pflegearbeitskr ften in Deutschland: Modellrechnungen für die Bundesl nder bis zum Jahr 2020
Carsten Pohl,Carsten Pohl
Comparative Population Studies , 2010,
Abstract: Durch den Anstieg des Geburtendefizits bei gleichzeitiger Zunahme der Lebenserwartung werden zukünftig relativ und absolut mehr ltere Menschen in Deutschland leben. Unter Verwendung von Modellrechnungen wird in diesen Beitrag die m gliche Entwicklung des Bedarfs an professionellen Pflegearbeitskr ften bis zum Jahr 2020 für die einzelnen Bundesl nder dargestellt. Aufgrund der Unterschiede im demografischen Wandel zwischen den Bundesl ndern wird sich auch der Pflegearbeitsmarkt nicht homogen im gesamten Bundesgebiet entwickeln. Mit dem Anstieg der Pflegebedürftigen von derzeit 2,25 Millionen auf voraussichtlich 2,9 Millionen bis zum Jahr 2020 in Deutschland insgesamt wird insbesondere die professionelle Pflege weiter an Bedeutung gewinnen. Die Nachfrage nach Pflegearbeitskr ften (in Vollzeit quivalenten) k nnte sich von derzeit 561.000 auf bis zu 900.000 bis zum Jahr 2020 erh hen. Die tats chliche Entwicklung des professio-nellen Arbeitsmarktes wird allerdings erheblich vom Engagement der pflegenden Angeh rigen abh ngen. Zudem spielen m gliche Produktivit tsfortschritte in der Erbringung von Pflegedienstleistungen eine Rolle, wie in verschiedenen Szenarien der Modellrechnungen gezeigt werden kann.
On Rational Approximations to Euler's Constant and to
Carsten Elsner
International Journal of Mathematics and Mathematical Sciences , 2009, DOI: 10.1155/2009/626489
Abstract: The author continues to study series transformations for the Euler-Mascheroni constant . Here, we discuss in detail recently published results of A. I. Aptekarev and T. Rivoal who found rational approximations to and
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