Detecting event-related recurrences by symbolic analysis: Applications to human language processing

Peter Beim Graben 1 Axel Hutt 2
1 Department of Computer Science [Berlin]
2 NEUROSYS - Analysis and modeling of neural systems by a system neuroscience approach
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : Quasistationarity is ubiquitous in complex dynamical systems. In brain dy-namics there is ample evidence that event-related potentials reflect such qua-sistationary states. In order to detect them from time series, several segmen-tation techniques have been proposed. In this study we elaborate a recentapproach for detecting quasistationary states as recurrence domains by meansof recurrence analysis and subsequent symbolisation methods. As a result,recurrence domains are obtained as partition cells that can be further alignedand unified for different realisations. We address two pertinent problems ofcontemporary recurrence analysis and present possible solutions for them.
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Peter Beim Graben, Axel Hutt. Detecting event-related recurrences by symbolic analysis: Applications to human language processing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Royal Society, The, 2014, 373, pp.20140089. ⟨10.1063/1.1819625⟩. ⟨hal-01077055⟩

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