## 3 Methods
### Machine Learning
\begin{table}
\begin{tabular}{c c} \hline \hline
**Features** & **Description and Calculation Parameters** \\ \hline skewness (cd1), skewness (cd2), skewness (cd3), skewness (cd4) & Skewness was calculated according to Equation (3) for the successive details (cd1, cd2, cd3, cd4) of the wavelet transform obtained using the Mallat pyramid. The db5 wavelet was used, and the decomposition was performed at 4 levels. \\ kurtosis (cd1), kurtosis (cd2), kurtosis (cd3), kurtosis (cd4) & Kurtosis was calculated according to Equation (4) for the successive details (cd1, cd2, cd3, cd4) of the wavelet transform obtained using the Mallat pyramid. The db5 wavelet was used, and the decomposition was performed at 4 levels. \\ entropy (signal), entropy (cd1), entropy (cd4) & Entropy was calculated according to Equation (5) for the signal and the successive details (cd1, cd2, cd3, cd4) of the wavelet transform obtained using the Mallat pyramid. The db5 wavelet was used, and the decomposition was performed at 4 levels. \\ Lag (sigma;), dim (signal) & The lag and dim parameters were calculated for the EEG signal. These parameters describe fundamental parameters that allow for the construction of an attractor. Autocorrelation was used for calculating the lag parameter, while the Cao method for calculating the dim parameter. The largest Lyapunov exponent was calculated for the EEG signal. Lag and dim parameters were used in the construction of the attractor. The correlation dimension of the attractor was calculated in the attractor space constructed using the lag and dim parameters for the EEG signal. \\  & Equation (11) was used for the calculations. \\ fractalDimension (signal) & The largest Lyapunov exponent was calculated for the EEG signal. \\  & Equation (14) was used for the calculations. \\  & The energy of successive components imf1, imf2, imf3 of the EMD decomposition was calculated according to Equation (4). \\  & decomposition was calculated according to Equation (4). \\ var (imf1), var (imf2), var (imf3) & Entropy for successive components imf1, imf2, imf3 of the EMD decomposition was performed using the Interpolation method for envelope construction, which utilizes piecewise-cubic Hermite interpolating polynomials. \\ skewness (imf1), skewness (imf2), skewness (imf3) & Skewness for successive components imf1, imf2, imf3 of the EMD decomposition was calculated according to Equation (3). \\ kurtosis (imf1), kurtosis (imf2), kurtosis (imf3) & Kurtosis for successive components imf1, imf2, imf3 of the EMD decomposition was calculated according to Equation (4). \\ entropy (imf1), entropy (imf2)entropy (imf3) & Entropy for successive components imf1, imf2, imf3 of the EMD decomposition was calculated according to Equation (5). \\  & The spectrum of the EEG signal was calculated using discrete Fourier transformation. The spectral resolution was 0.5 Hz. The amplitude of the spectral peaks in the range of 0–86 Hz was selected. \\  & The autocorrelation of the signal was calculated using Equation (7). \\ autocorrelation (signal) & Coefficients corresponding to signal shifts in the range of 1–348 samples were selected. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._During the training of SVM, the optimization process is limited to handling only a few training samples called support vectors. Consequently, even though SVM can operate on a large number of training data, the training process is computationally efficient. These advantages make SVM, especially with the RBF kernel, a popular and effective tool in the field of machine learning, particularly for nonlinear and high-dimensional data [97, 98, 99, 100].