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Detecting event-related recurrences by symbolic analysis: Applications to human language processing
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.
Cited literature [10 references]
https://hal.inria.fr/hal-01077055
Contributor : Axel Hutt <>
Submitted on : Thursday, December 18, 2014 - 11:12:39 AM
Last modification on : Monday, June 15, 2020 - 12:00:33 PM
Long-term archiving on: : Friday, April 14, 2017 - 2:07:17 PM
Contributor : Axel Hutt <>
Submitted on : Thursday, December 18, 2014 - 11:12:39 AM
Last modification on : Monday, June 15, 2020 - 12:00:33 PM
Long-term archiving on: : Friday, April 14, 2017 - 2:07:17 PM
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