## 4 Implementation and Results
### Selection of Feature Extraction Methods and Classification Algorithms
#### 4.1.1 Feature Extraction Methods  
In response to question Q1, regarding the most commonly used feature extraction methods from the different domains, the results are shown in Figures 4-7. Figure 4 shows the feature extraction methods in the time domain found in our literature review vs. the number of papers that used them. Statistical methods are the most used, followed by ShEn, HFD, SampEn, and HP methods. In Figure 5, the statistical procedures found in the SLR are shown. In total, 26% of articles employ Stddev as a statistical method, 19% use the mean method, and 17% use its AFSD_N variants. Meanwhile, 12% corresponds to the kurtosis method, and 7% to the skewness method. Based on the frequency of use of methods in the time domain, the most commonly used approaches were selected: Shannon entropy, sample entropy, Higuchi fractal dimension, Hjorth parameters, and statistical methods. Some of the selected statistical methods were the standard deviation, mean, mean absolute deviation, and kurtosis, corresponding to percentages greater than 12%. Additionally, specific methods in the time domain with lower percentages were considered, as they present good results in the evaluation metrics of classification algorithms. Among the techniques included in the time domain are two variants of entropy (approximate and permutation), the Petrosian fractal dimension method, energy, root mean square,  
Figure 3: Criteria for categorizing emotional quadrants. High arousal–high valence (HAHV), high arousal–low valence (HALV), low arousal–low valence (LALV), and low arousal–high valence (LAHV). line length, average power, zero crossing, higher-order crossing, and the statistical techniques: variance, skewness, and maximum. Figure 4: Feature extraction methods in the time domain. Energy (Eng), root mean square (RMS), line length (LinLen), average power (Avg), Shannon entropy (ShEn), approximate entropy (ApEn), sample entropy (SampEn), permutation entropy (PerEn), Higuchi fractal dimension (HFD), Petrosian fractal dimension (PFD), Hjorth parameters (HP), zero crossing (ZeCr), higher-order crossing (HOC), empirical mode decomposition (EMD), higher-order spectral (HOS), Katz’s fractal dimension (KFD), statistics (ST). Figure 5: Statistical feature extraction methods in the time domain. Figure 6 shows the frequency-domain methods found in the SLR. We found that 22% of articles employ the PSD method for feature extraction, 15% use the FFT method or one of its variations, and 11% use the SE, RASM, and DASM methods, followed by the PR method with 7%. The selected methods in the frequency domain are power spectral density, fast Fourier transform, rational asymmetry, differential asymmetry, and spectral entropy, which correspond to percentages greater than or equal to 11% of the frequency of use. Additionally, the power ratio method, which corresponds to 7%, was selected due to its good results in the evaluation metrics of classification algorithms. Finally, Figure 7, feature extraction methods in the time-frequency domain, shows the feature extraction methods in the time-frequency domain found in the SLR. In total, 68% of articles use the DWT method, followed by the WEng and WEnt methods with 14% each. The selected techniques in the time-frequency domain are discrete wavelet transform and wavelet entropy, which correspond to the highest percentages of frequency of use. The analysis involved the creation of four vectors with features from the time, frequency, and time-frequency domains, which were used for the experiments in Section 4.3. Spatial features in EEG (electroencephalography) refer to the attributes and information that can be derived from the distribution and variation of EEG signal amplitudes across different electrode sites on the scalp. In the DEAP dataset, EEG electrodes are placed  
Figure 6: Feature extraction methods in the frequency domain. Figure 7: Feature extraction methods in the time–frequency domain.