title: Detecting event-related recurrences by symbolic analysis: applications to human language processing.
abstract: Quasi-stationarity is ubiquitous in complex dynamical systems. In brain dynamics, there is ample evidence that event-related potentials (ERPs) reflect such quasi-stationary states. In order to detect them from time series, several segmentation techniques have been proposed. In this study, we elaborate a recent approach for detecting quasi-stationary states as recurrence domains by means of recurrence analysis and subsequent symbolization methods. We address two pertinent problems of contemporary recurrence analysis: optimizing the size of recurrence neighbourhoods and identifying symbols from different realizations for sequence alignment. As possible solutions for these problems, we suggest a maximum entropy criterion and a Hausdorff clustering algorithm. The resulting recurrence domains for single-subject ERPs are obtained as partition cells reflecting quasi-stationary brain states.
authors.affiliation: Department of German Studies and Linguistics, Humboldt- Universität zu Berlin, Unter den Linden 6, 10099 Berlin, Germany Bernstein Center for Computational Neuroscience Berlin, 10115 Berlin, Germany peter.beim.graben@hu-berlin.de.
authors.forename: Peter
authors.initials: P
authors.lastname: Beim Graben
authors.affiliation: Team Neurosys, INRIA CR Nancy, 54602 Villers-les-Nancy Cedex, France.
authors.forename: Axel
authors.initials: A
authors.lastname: Hutt
keywords: brain microstates; electroencephalography; language processing; recurrence analysis; symbolic dynamics