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The analysis of observed time series from nonlinear systems is usually doneby making a time-delay reconstruction to unfold the dynamics on amulti-dimensional state space. An important aspect of the analysis is thechoice of the correct embedding dimension. The conventional procedure used forthis is either the method of false nearest neighbors or the saturation of someinvariant measure, such as, correlation dimension. Here we examine this issuefrom a complex network perspective and propose a recurrence network basedmeasure to determine the acceptable minimum embedding dimension to be used forsuch analysis. The measure proposed here is based on the well knownKullback-Leibler divergence commonly used in information theory. We show thatthe measure is simple and direct to compute and give accurate result for shorttime series. To show the significance of the measure in the analysis ofpractical data, we present the analysis of two EEG signals as examples.

Determining the minimum embedding dimension for state space reconstruction through recurrence networks