## 4 Implementation and Results  
The DEAP dataset's characteristics are presented in Table 3. It consists of 32.dat files, composed of two parts: the data and the emotion labels classified on a numeric scale from 1 to 9. Regarding the labels, we are only interested in the first two of the four available variables, corresponding to valence and arousal.  
The one-hot encoding process was applied to convert the numerical variables to categorical data, proposing three criteria: median, mean, and fixed value. The first criterion, \"Median\", consists of finding the median value between the valence-arousal (VA) labels independently and categorizing these labels with values of 0 and 1, accordingly. Label 1 is assigned to values above the median, and label 0 to values below the median. The second criterion, \"Mean\", initially looks for the average value among the VA data independently. Once the average value is identified, the labels are standardized so that values equal to or greater than the average are labeled 1, and values below the average are labeled 0. Finally, the third criterion, \"Greater than 5\", proposes assigning the label 1 to all data greater than or equal to 5, and 0 to data less than 5.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Labels**} & \multicolumn{2}{c}{**Prediction**} \\ \cline{2-3}  & **Negative (0)** & **Positive (1)** \\ \hline Negative (0) & TN & FP \\ Positive (1) & FN & TP \\ \hline \hline \end{tabular} Nomenclature: true positives (TPs), true negatives (TNs), false positives (FPs), false negatives (FNs).  
\end{table}
Table 2: Confusion matrix example.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Description**} & **DEAP Dataset** \\ \hline EEG devise & Biosemi ActiveTwo \\ \hline Number of channels & 32 for EEG, 8 physiological signals \\ \hline Sampling & Original 512 Hz, downsampled samples of 128 Hz \\ \hline Number of subjects & 32 \\ \hline Stimulus & 40 musical videos (one minute each) \\ \hline Emotions &
\begin{tabular}{c} Valence, arousal, dominance, and liking (scale from 1 to 9, \\ familiarity from 1 to 5) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: DEAP dataset characteristics.  
Figure 3 shows the percentages of each categorization according to the proposed criteria. The \"Median\" criterion has 28% of classes labeled HAHV, 22% LAHV, 25.2% HALV, and 24.8% LALV. In the \"Mean\" criterion, 26.6% of categories correspond to the HAHV label, 23.7% to LAHV, 20% to HALV, and 29.6% to LALV. The difference between the \"Median\" and \"Mean\" criteria ranges from 1.7% to 5.2%. However, when comparing the results with the third criterion, \"Greater than\", we have 35.8% in HAHV, 23.1% in LAHV, 20.8% in HALV, and 20.3% in LALV, representing a significant difference of 7.8% and 9.2% in the HAHV label.  
Based on these results, several initial tests were carried out for the one-hot encoding process via assigning 80% and 20% to the train and test sets [27]. It was identified that the best option for the process was using the \"Median\" criterion. It separated the data so that there were samples of emotions from each VA quadrant both in the train and test sets.