Table 2 Recurrence quantitative analysis variables and their interpretation

Recurrence rate

RR

The probability that a system state recurs in a finite time. RR has been found useful for detecting evoked response potentials (ERPs) using single trials [30].

Determinism

DET

DET comes from repeating patterns in the system and is an indication of its predictability. Regular, deterministic signals, such as sine waves, will have higher DET values, while uncorrelated time series, such as chaotic processes and random numbers, will cause low DET.

Laminarity

LAM

Laminarity represents the frequency of occurrence of laminar states in the system without describing the length of these laminar phases. More frequent appearance of laminar states may relate to more frequent “seeds” for synchronized dynamics [46], which may be related to epileptiform spiking on an EEG trace.

Max line length

L_max

Lmax is related to the largest Lyapunov exponent of a chaotic signal, which is a dynamic complexity measure that describes the divergence of trajectories starting at nearby initial states, [47]. Lower values are typically associated with pathological conditions [43, 48].

Entropy derived from diagonal lines

L_entr

This measure of entropy is derived from the diagonal lines of the recurrence plot. It is related, but not identical to, other measures of entropy, such as the sample entropy used in previous studies [37]

Trapping time

TT

Trapping time is an estimate of the time that a system will remain in a given state - “trapped” state. Thus, lower TT values may be an indication of more frequent transitions between dynamical states and less system stability.