## 3 Materials and Methods
### Channel and Feature Selection  
The optimization method is applied for the selection of channels and features in this work as follows:  
(a) Feature Extraction  
A total of 42 input features are extracted from four sub-bands generated by DWT using different techniques in time-frequency, entropy, and complexity domains as shown in Table 1 panels (a), (b) and (c). All input features are extracted and used for the next step of channel selection. Figure 2: Examples of Theta, Alpha, Beta, and Gamma signals. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**(a)** & \\ \hline \hline \multicolumn{3}{c}{**Time-Frequency Domain Features**} \\ \hline \multicolumn{3}{c}{**Features Name**} \\ \hline Hjorth activity (HA) [23] & Representation of the signal power information \\ \hline Hjorth complexity (HC) [23] & Reflection of the bandwidth and the change in frequency \\ \hline Hjorth mobility (HM) [23] & The square root of the ratio between the variance of the signal’s first derivative and the variance of the signal \\ \hline Maximum of power spectral density (mPSD) [23] & The peak of spectral energy at a particular frequency \\ \hline Maximum frequency (MF) [23] & The maximum frequency corresponding to mPSD \\ \hline Lyapunov exponent of the uniformly sampled time-domain signal (LyaExp) [23] & Quantification of periodic behavior of chaotic systems \\ \hline First difference (FD) [14] & Representation of the relationship between the current data point and its preceding one, highlighting changes in the waveform’s dimensionality over time \\ \hline Second difference (SD) [14] & Representation of the relationship among 3 consecutive data points and is a measure that is highly sensitive to changes in the signal’s amplitude \\ \hline Normalized first difference (NFD) [14] & Reflection of the change between the current data and the previous data in the normalized EEG signal \\ \hline Normalized second difference (NSD) [14] & Description of the relationship between 3 sequential data points in the normalized EEG signal \\ \hline Mean absolute value (MEA) [42] & Measurement of the average magnitude of the signal \\ \hline Mean value of EEG amplitude (MA) [42] & The average value computed from all points in the signal \\ \hline Median value of EEG (Med) [42] & The middle value in a signal’s data set after arranging all values in ascending or descending order \\ \hline Average power (BPA) [42] & The mean of the squared amplitudes of the signal \\ \hline Energy (Ene) [42] & The sum of the squared magnitudes of all the signal’s components \\ \hline Slope sign change (SSC) [43] & Measurement of the number of times the slope of the waveform changes sign \\ \hline Zero crossing rate (ZCR) [43] & A measure of EEG waveform frequency cross the zero axis in a unit time \\ \hline \hline \multicolumn{3}{c}{**Entropy Domain Features**} \\ \hline \multicolumn{3}{c}{**Features Name**} \\ \hline Approximate entropy (AE) [23] & Quantification of fluctuation regularity and unpredictability \\ \hline Permutation entropy (PE) [23] & Analysis of the relative occurrence of each of these patterns \\ \hline Singular entropy (SSE) [23] & Computation of applying singular value decomposition to the trajectory, which is reconstructed a one-dimensional time series into a multidimensional phase space \\ \hline Shannon entropy (ShE) [23] & Measurement of uncertainty, commonly to evaluate the degree of chaos in EEG signals \\ \hline \hline \end{tabular}
\end{table} 
Table 1: **(a)** The descriptions of the time-frequency domain features. (b) The descriptions of the entropy domain features. (c) The descriptions of the complexity domain features.