17 research outputs found
Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains
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.Comment: Follow-up to arXiv:0706.3375. Journal submission 9 Jul 2007.
Published 19 Dec 200
die Theorie selbstreferentieller Systeme und der Konstruktivismus
Einleitung I. Maturana 1\. Der Organismus als autopoietisches System 2\. Die
Geschlossenheit des Nervensystems 3\. Kognition, Kommunikation, Beobachtung
4\. Erkenntnis II. Roth 1\. VerhÀltnis zu Maturana 2\. Neurobiologische
Befunde und Konsequenzen 3\. Die Unwirklichkeit der »RealitÀt« 4\. Die
KonstruktivitÀt des Wahrnehmungsapparats 5\. Physik als intendierte RealitÀt
III. Luhmann 1\. Systemtheorie 2\. Erkenntnistheoretische Ăberlegungen in den
»Sozialen Systemen« 3\. »Operativer Konstruktivismus«
BeobachtungâDifferenzâUmweltâMetatheorie SchluĂ: Konstruktivismus als naturale
OntologieDas Thema der Arbeit ist die Frage, welche Konsequenzen im Bereich der
Erkenntnistheorie sich aus denjenigen wissenschaftlichen AnsÀtzen ableiten
lassen, die am Begriff des Systems orientiert sind. Ihr Inhalt besteht in der
Darstellung systemtheoretischer Konzepte und ihrer erkenntnistheoretischen
Konsequenzen bei Maturana, Roth und Luhmann, sowie in deren Kritik auf der
Ebene der System- wie auch der Erkenntnistheorie, mit der Absicht, durch
eigene Ăberlegungen einen Beitrag zur KlĂ€rung und Fortentwicklung einer
systemtheoretisch angeleiteten Erkenntnistheorie zu leisten. Resultate sind,
daĂ die ĂŒberwiegend konstruktivistische erkenntnistheoretische Haltung der
drei Autoren sich nur bedingt mit systemtheoretischen Argumenten rechtfertigen
lĂ€Ăt, und daĂ die zugrundegelegte Theorie selbstreferentieller Systeme
generell noch nicht den Stand erreicht hat, auf dem sich zuverlĂ€ssig SchlĂŒsse
ziehen lassen. AbschlieĂend wird kurz die Idee von Systemtheorie als einer
»naturalen Ontologie« skizziert.Elektronische Version von 200
Eigenvalue Decomposition as a Generalized Synchronization Cluster Analysis
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.Comment: Submitted Oct 2005, accepted Jan 2006, "published" Oct 2007, actually
available Jan 200
Robust artifactual independent component classification for BCI practitioners
Objective. EEG artifacts of non-neural origin can be separated from neural signals by independent component analysis (ICA). It is unclear (1) how robustly recently proposed artifact classifiers transfer to novel users, novel paradigms or changed electrode setups, and (2) how artifact cleaning by a machine learning classifier impacts the performance of brainâcomputer interfaces (BCIs). Approach. Addressing (1), the robustness of different strategies with respect to the transfer between paradigms and electrode setups of a recently proposed classifier is investigated on offline data from 35 users and 3 EEG paradigms, which contain 6303 expert-labeled components from two ICA and preprocessing variants. Addressing (2), the effect of artifact removal on single-trial BCI classification is estimated on BCI trials from 101 users and 3 paradigms. Main results. We show that (1) the proposed artifact classifier generalizes to completely different EEG paradigms. To obtain similar results under massively reduced electrode setups, a proposed novel strategy improves artifact classification. Addressing (2), ICA artifact cleaning has little influence on average BCI performance when analyzed by state-of-the-art BCI methods. When slow motor-related features are exploited, performance varies strongly between individuals, as artifacts may obstruct relevant neural activity or are inadvertently used for BCI control. Significance. Robustness of the proposed strategies can be reproduced by EEG practitioners as the method is made available as an EEGLAB plug-in.EC/FP7/224631/EU/Tools for Brain-Computer Interaction/TOBIBMBF, 01GQ0850, Verbundprojekt: Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie fĂŒr Mensch-Maschine Interaktion - Teilprojekte A1, A3, A4, B4, W3, ZentrumDFG, 194657344, EXC 1086: BrainLinks-BrainTool
Mental states as macrostates emerging from brain electrical dynamics
Psychophysiological correlations 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 âemergingâ 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 recording, where we are able to identify macrostates from the physiological data that correspond to mental states of the subject
Valid population inference for information-based imaging: From the second-level t-test to prevalence inference
In multivariate pattern analysis of neuroimaging data, âsecond-levelâ 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 âinformation-likeâ 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, rendering it effectively equivalent to fixed-effects analysis. 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, âpermutation-based information prevalence inference using the minimum statisticâ, is described in detail and applied to empirical data
Searchlight-based multi-voxel pattern analysis of fMRI by cross-validated MANOVA
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 âdecodingâ 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 âtrainingâ and âtestâ sets. The estimate can therefore serve not only as a test statistic, but also 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 âcross-decodingâ
Testing for phase synchronization
We present different tests for phase synchronization which improve the procedures currently used in the literature. This is accomplished by using a twosamples test setup and by utilizing insights and methods from directional statistics and bootstrap theory. The tests differ in the generality of the situation in which they can be applied as well as in their complexity, including computational cost. A modification of the resampling technique of the bootstrap is introduced, making it possible to fully utilize data from time series. Contents