## 3 Numerical Results and Discussion
### EEG Signal Separation
#### 3.4.2 Influence of the Entropy Calculation Method on the Separation of EEG Signals  
In order to determine the optimal method for calculating entropy using A3 coefficients, the following entropies were tested: SVDEn, PermEn, SampEn, CoSiEn, FuzzyEn, DistEn, PhaseEn, BubbleEn, AttnEn, IncEn, NNetEn. The \(F_{m}\) values for various entropies are shown in Figure 15. In order to maximize \(F_{m}\), the entropy parameters were chosen in a manner that maximized the value of \(F_{m}\). In this case, FuzzyEn \(F_{m}\) = 140 seems to produce the best results. It is found that all three methods (SampEn, CoSiEn, FuzzyEn) yield a relatively high result when vectors that are similar to the built-in matrix are taken into consideration. The worst result comes from DistEn--\(F_{m}\) = 6.8. NNetEn shows that there is a sufficiently good separation of the signals from healthy and sick patients at \(F_{m}\) = 40.29.  
Figure 14: Distribution of \(F_{m}\) values for various types of signals calculated using the SVDEn feature. \(F_{m}\)—maximum F-ratio values for all 19 channels. The figure reveals the most informative types of signals.  
In Figure 16, the best F-ratio values for different entropy methods are shown for each channel. It was found that channel number 10 (Cz) was the most significant among the channels processed. Additionally, good separation can be observed in channels 7 (F8), 9 (C3), 15 (Pz), and 16 (P4). On channels 5, 11, 12, 13, and 18, NNetEn ranks among the leaders in terms of feature strength, and on channel 11, it shows the best results.  
In Figure 17, F-ratio distributions for Dataset 1 (Figure 17a) and Dataset 2 (Figure 17b) are shown based on the settings number and channel. The best reservoir filling methods for Dataset 1 are M3, M4, and M5, and for Dataset 2 M1, M3, and M4. In Dataset 1, channels 7, 8, 10, 11, 12, and 15 provide the best signal recognition, while in Dataset 2, channels 7, 9, 10, 13, 15, and 16 provide the best signal recognition.  
In terms of the single feature, NNetEn generally performed quite well in comparison with other entropies. The synergistic effect of standard entropies with NNetEn(D1,1, _Nset_ = 60) is shown in Figure 18. A synergistic effect was observed when almost all entropies were paired with NNetEn, increasing the total \(A_{\text{RKF}}\) value (Figure 18a). The dependence of \(K_{\text{syn}}\) on pair type can be seen in Figure 18b. The most effective synergistic effects were observed for IncEn, AttnEn, SampEn, and SVDEn.  
Figure 16: Distribution of the F-ratio value for various entropy calculation methods using A3 coefficients. The figure shows the performance of NNetEn as a feature for different channels.  
Figure 15: Distribution of the \(F_{m}\) value when using the A3 coefficients for various entropy calculation methods. The figure shows the ratio of entropy efficiencies.  
Figure 17: F-ratio distribution depending on the setting number and channel for Dataset 1 (**a**) and Dataset 2 (**b**). The most effective settings are in red.  
Figure 18: Synergistic effect of standard entropies with NNetEn(D1,1, _Nset_ = 60) shown based on accuracy \(A_{\text{RKF}}\) (**a**), and coefficient \(K_{\text{syr}}\) (**b**). The figure demonstrates an increase in the efficiency of classification, when using NNetEn as a paired feature to other entropies.