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

TL;DR

This paper introduces a symbolic recurrence analysis method to detect quasistationary states in brain dynamics, aiding in understanding event-related potentials and their recurrence patterns.

Contribution

It advances recurrence analysis by proposing a novel symbolic approach for identifying and aligning recurrence domains across multiple realizations.

Findings

Effective detection of quasistationary states in brain signals.

Improved alignment of recurrence domains across trials.

Potential applications in analyzing event-related potentials.

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.