ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2015
Detecting event-related recurrences by symbolic analysis: applications to human language processing.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- What Differentiates Poor- and Good-Outcome Psychotherapy? A Statistical-Mechanics-Inspired Approach to Psychotherapy Research, Part Two: Network Analyses.Frontiers in psychology · 2020Article
- Metastable Resting State Brain Dynamics.Frontiers in computational neuroscience · 2019Article
- Transcranial Direct Current Stimulation (tDCS) Can Modulate EEG Complexity of Children With Autism Spectrum Disorder.Frontiers in neuroscience · 2018Article
- Enhancing dynamical signatures of complex systems through symbolic computation.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2015Article
- Metastable dynamics in heterogeneous neural fields.Frontiers in systems neuroscience · 2015Article
- Statistical Frequency-Dependent Analysis of Trial-to-Trial Variability in Single Time Series by Recurrence Plots.Frontiers in systems neuroscience · 2015Article
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2 authors.
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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.
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