## 3 Materials and Methods
### Channel and Feature Selection
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline Dispersion entropy (DE) [44] & Quantification of the regularity of a time series \\ \hline Fuzzy entropy (FE) [45] & Measurement of the degree of similarity between two vectors based on their shape \\ \hline Renyi entropy (RE) [46] & Measurement of the uncertainty in a probability distribution using a parametric family of indices \\ \hline Sample entropy (SE) [47] & A metric of the underlying or complexity \\ \hline Maximum of Spectral entropy (Spe) [47] & Measurement of the irregularity or complexity of the signal’s power distribution across frequencies \\ \hline Tsallis entropy (TE) [48] & Description of the physical behavior of a system and aids in distinguishing between bursts, continuous rhythms, and spikes in EEG signals \\ \hline \hline \multicolumn{3}{p{113.8 pt}}{**(c)**} \\ \hline \multicolumn{3}{p{113.8 pt}}{**Complexity Domain Features**} \\ \hline \multicolumn{3}{p{113.8 pt}}{**Features Name**} \\ \hline Co-dimension (CoD) [23] & Identification of the number of independent variables required to characterize the system’s dynamics \\ \hline C0 complexity (C0) [23] & The proportion of stochastic components in a signal, assumed to have both regular and stochastic elements \\ \hline Auto regressive model (AR) [49] & Modeling the characteristics and information present in a signal, where each sample is calculated by adding up the weighted previous samples \\ \hline Higuchi (HFD) [50] & Definition of the minimum number of coordinates required to identify any point in phase space \\ \hline Hurst (H) [50] & Measurement of the degree of self-similarity and predictability within a time series under this nonlinear statistical model \\ \hline Root mean square of amplitude (AM) [42] & Representation of the square root of the average of the sum of the signal’s squared values \\ \hline Variance (Var) [42] & A measure of signal data dispersion, determined by the average of the squared differences from the mean \\ \hline Skewness (Sw) [50] & Measurement of the asymmetry of a real-valued distribution function relative to its mean \\ \hline Phase space reconstruction (Psr) [51] & Transformation of time series properties into topological features of a geometric object, preserving the original space’s topological properties \\ \hline Hilbert transform (Hilb) [51] & Representing frequency as a phase change rate \\ \hline Complexity measure (CM) [52] & Measurement of the complexity and irregularity in EEG data \\ \hline Covariance calculation (Cvbin) [53] & Quantification of the variance in the EEG signal and the corresponding binary signal \\ \hline Frequency calculation (Frqbin) [53] & The count of binary signal transitions between ’0’ and ’1’, followed by division the result by 10 to obtain transitions for 1s \\ \hline Area calculation (Abin) [53] & Representation of the maximum between the sum of binary signal values and the sum of inverted binary signal values \\ \hline Kurtosis (Kurt) [50] & Measurement of the peakedness or flatness of a signal relative to its normal distribution \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._(b) Channel and Feature Selection  
The original EEG dataset is collected over the AgCl wet electrodes and Biosemi ActiveTwo system [39], which possibly represent high correlation of the EEG signals. Hence, the final detection performance of human emotions is decreased due to the low-quality feature extracted from the above EEG signals. Moreover, few irrelevant features are certainly included in the TIF extracted from improper channels, which results in a decrease in the final performance of the emotion detection algorithm. Therefore, the GA-based channel and feature ranking methods are employed for the selection of the most informative channels and features in terms of emotion detection [20]. The chromosome includes a string of 32 binary bits representing 32 channels in which 1 and 0 bits are assigned to channel presence and absence, respectively.