Detecting event-related recurrences by symbolic analysis: Applications to human language processing
arXiv:1410.5580 · doi:10.1098/rsta.2014.0089
Abstract
Quasistationarity is ubiquitous in complex dynamical systems. In brain dynamics there is ample evidence that event-related potentials reflect such quasistationary 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 quasistationary states as recurrence domains by means of recurrence analysis and subsequent symbolisation methods. As a result, recurrence domains are obtained as partition cells that can be further aligned and unified for different realisations. We address two pertinent problems of contemporary recurrence analysis and present possible solutions for them.
arXiv comment24 pages, 6 figures. Draft version to appear in Proc Royal Soc A
References in corpus (7)
- How to avoid potential pitfalls in recurrence plot based data analysis
- Heteroclinic Contours in Neural Ensembles and the Winnerless Competition Principle
- Line Structures in Recurrence Plots
- Mental States as Macrostates Emerging from EEG Dynamics
- Multiple phases in stochastic dynamics: geometry and probabilities
- Detecting recurrence domains of dynamical systems by symbolic dynamics
- Nonlinear denoising of transient signals with application to event related potentials