## 3 Results
### Stratified k-Fold Model Results  
For cross-validation, a stratified five-fold randomized shuffle was implemented. The metrics were calculated for each fold and then averaged to obtain an overall mean value across all folds. This procedure was performed using the StratifiedKFold method from the Scikit-learn library in Python. Table 5 presents a summary of all metrics for the top three models in the evaluated datasets. A notable finding is the consistent performance of the Random Forest classifier, which achieved an accuracy of more than 88% in all three datasets. Compared to the train-test split approach, these results showed an improvement not only in accuracy but also in all metrics, with recall, precision, and F1 scores exceeding 87% for all datasets. The highest classification performance was observed in the emotional dataset, where all metrics exceeded 91%, and no evidence of overfitting was detected, as shown in Figure 2. Furthermore, the precision of the combined dataset of 88% is not far from 92% achieved in the other datasets. Taking into account the significantly larger sample size and diverse stimuli in the combined dataset, this result can be considered more generalizable, representing \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**IA Model Name** & **Validation Accuracy** & **Validation Precision** & **Validation Recall** & **Validation F1-Score** \\ \hline Random Forest & **0.76** & **0.77** & **0.76** & **0.76** \\ Logistic Regression & 0.62 & 0.62 & 0.62 & 0.62 \\ AdaBoost Classifier & 0.60 & 0.60 & 0.59 & 0.60 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance results of the Combined Stimuli database in the validation group for the best classification models. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**IA Model Name** & **Validation Accuracy** & **Validation Precision** & **Validation Recall** & **Validation F1-Score** \\ \hline Random Forest & **0.81** & **0.81** & **0.81** & **0.81** \\ Multi-Layer Perceptron & 0.67 & 0.67 & 0.66 & 0.67 \\ AdaBoost Classifier & 0.67 & 0.68 & 0.66 & 0.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance results of the neutral stimulus database in the validation group for the best classification models. the overall success of the models evaluated. The set of metrics obtained was superior to those from the train-test split and comparable datasets. The confusion matrices shown in Figure 2 provide visual evidence of possible overfit comparisons between trials. The upper row displays the results of the stratified \(k\)-fold method for Random Forest across each dataset. In these matrices, the classes of true negatives and true positives (represented by the principal diagonal) are prominently distinguished by a high majority of values and color intensity. In contrast, the lower row shows the results of the random forest train-test split method in each dataset. Here, noticeable issues such as low quality in false positive cases are evident, as seen in instances in Figure 2d,e. This is characterized by a lack of majority classification in this segment of  
Figure 2: Confusion matrices for the Random Forest classifiers (RF) presented in this paper, comparing the classification performance of the stratified \(k\)-fold (SKF) method across each dataset (**Row 1**) with the classification results from the train–test split (TTs) test group (**Row 2**). (**a**) Combined Stimuli dataset SKf RF, (**b**) Emotional Stimuli dataset SKf RF, (**c**) Neutral Stimuli dataset SKf RF, (**d**) Combined Stimuli dataset TTs RF, (**e**) Emotional Stimuli dataset TTs RF, and (**f**) Neutral Stimuli dataset TTs RF.