# Recurrence Quantification Analysis as a Form of Postural Control Assessment: A Systematic Review
## 4 Discussion
### Impact of Embedding Parameters Selection  
The papers in this review are very different, even though they deal with the same issues. Unfortunately, many authors do not provide information on how the parameters needed for RQA analysis, that is, embedding dimension, time delay, and recurrence threshold, are calculated, which makes the procedures hardly reproducible. A similar problem applies to providing information on the program with which they performed the calculations. Among all 28 papers, only in 12 of them was it possible to find precise information on this issue. Most often, in nine studies [17; 20; 37; 44; 50; 53; 63; 64; 67], authors chose different versions of the Cross Recurrence Plot Toolbox 5.13 (R25, R13) developed by [PERSON] et al. [6; 12]. Both [PERSON] et al. [42] and [PERSON] et al. [45; 69] based their calculations on [PERSON] and [PERSON]'s [10] paper. [PERSON] et al. [65] chose the Recurrence Quantification Toolbox for MATLAB ([[http://au.mathworks.com/matlabcentral/fileexchange/46765-recurrence-quantification-analysis-rqa](http://au.mathworks.com/matlabcentral/fileexchange/46765-recurrence-quantification-analysis-rqa)]([http://au.mathworks.com/matlabcentral/fileexchange/46765-recurrence-quantification-analysis-rqa](http://au.mathworks.com/matlabcentral/fileexchange/46765-recurrence-quantification-analysis-rqa)), accessed on 29 April 2023) [66].  
Referring to the embedding parameters. In the first group, all authors provided relevant information. In the second group, there was no information in the two papers on determining embedding dimension and delay time. In the third group, this information was not present in 10 of the 12 studies. The embedding dimension in each paper was calculated using FNN. The lag time was calculated using the autocorrelation function in three studies [42; 50; 53]. Similarly, the AVD method was used in three papers [17; 42; 56]. In contrast, eleven papers used the AMIF method. Only [PERSON] et al. [42] used three (AVD, AMFI, and ACF) methods to determine the lag time. [PERSON] et al. [42] found similarly to [PERSON] et al. [15] that the autocorrelation and mutual information techniques used to select time lag values were inconsistent and sometimes yielded values that were too high. They showed that the average displacement method (AVD) developed for noisy datasets by [PERSON] et al. [38] was a preferred technique. [PERSON] et al. [17] addressed the determination of the recurrence threshold as one of the crucial parameters of RQA [70], as below its level, the recurrence is defined. Various methods have been used to determine the recurrence threshold. The most popular ones are the percentage of the maximum diameter of a geometric shape in phase space [37] or the percentage of the average distance between all data points in phase space [15]. Generally, this percentage is adjusted so that the %REC values are acceptable (around 1% up to 10%) [50]. Finally, [PERSON] et al. [17] demonstrated that fixing the recurrence rate threshold (5%) improves the reliability of RQA within sessions and can increase sensitivity in differentiating study groups. In summary, in any of the previously published papers, it is not specified which method is best for determining the time lag or recurrence threshold. Therefore, it is recommended to use several of them to check the reproducibility of the results. In contrast, the embedding dimension was always determined using the FNN method.