# Using the \(\beta\)/\(\alpha\) Ratio to Enhance Odor-Induced EEG Emotion Recognition
## 3 Results
### Model Evaluation  
Based on the subjective (self-reported) and objective (EEG) results, inhalation of either SEO or BEO clearly differentiates emotions along the arousal dimension (inhaling SEO induces low-arousal emotions, while inhaling BEO induces high-arousal emotions). However, in the valence dimension, no significant difference is observed between the two (See Supplementary Table S1), as both SEO and BEO induce high-valence emotions. Therefore, we focus solely on the arousal dimension of emotions, with SEO labeled as low arousal (label: 0) and BEO labeled as high arousal (label: 1). We employed six models (DA-1, SVM-1, RF-1, DA-2, and SVM-2, RF-2) for binary classification. The hyperparameter optimization diagram for SVM-1 (Figure 4A) illustrates that when c is fixed, the validation set accuracy generally increases and stabilizes as g decreases. Notably, the highest accuracy (66.25%) occurs when c = 1 and g = 0.5. The hyperparameter optimization diagram for SVM-2 (Figure 4B) indicates a similar trend to SVM-1, with the difference being that validation set accuracy rises to a plateau as c increases within the range of 16-256. The highest accuracy (70%) is achieved when c = 16 and g = 0.5. The hyperparameter optimization diagrams for RF-1 and RF-2 (Figure 5) show that when the number of decision trees is fixed, the validation set accuracy and AUC generally increase and stabilize as the minimum number of leaves increases, with changes in AUC being particularly noticeable. For RF-1, the optimal values (accuracy = 0.7, AUC = 0.7406) are achieved with 20 decision trees and a minimum of 13 leaves. For RF-2, the optimal values (accuracy = 0.7625, AUC = 0.7594) are achieved with 30 decision trees and a minimum of 14 leaves. Figure 4: Schematic diagram of hyperparameter optimization of the support vector machine (SVM) model. (**A**) Hyperparameter optimization for SVM-1 involves constructing features based on traditional EEG frequency bands (\(\delta\), \(\theta\), \(\alpha\)1, \(\alpha\)2, \(\beta\)1, and \(\beta\)2). SVM-1 uses a grid search method and a validation set accuracy rate obtained through 5-fold cross-validation of the training set (Supplementary Figure S1) to identify the optimal hyperparameters. (**B**) For SVM-2 hyperparameter optimization, the \(\beta/\alpha\) ratio indicator is introduced during feature construction. All other conditions remain the same as in SVM-1. Figure 5: _Cont_. In the final evaluation of the test set (sample size = 20), we used the above hyperparameters for model prediction (Figure 6). The results from six metrics (accuracy, precision, sensitivity, specificity, ROC AUC, and F1 score) demonstrate that the performance of DA-1, SVM-1, and RF-1 models is lower than that of DA-2, SVM-2, and RF-2, indicating that incorporating the \(\beta/\alpha\) ratio into feature construction effectively enhances model performance. Furthermore, comparing the performance across different model types (DA, SVM, and RF), the results from the six metrics show that DA and SVM models perform similarly and poorly, while RF models excel across all six metrics, significantly outperforming DA and SVM. In summary, for feature construction, incorporating the \(\beta/\alpha\) ratio combined with the RF algorithm is an effective method for odor-induced EEG emotion recognition, compared to traditional EEG frequency bands (\(\delta\), \(\theta\), \(\alpha\)1, \(\alpha\)2, \(\beta\)1, and \(\beta\)2). Figure 5: Schematic diagram of hyperparameter optimization of the random forest (RF) model. (**A**) RF-1 hyperparameter optimization. RF-1 constructs features based on traditional EEG frequency bands (\(\delta\), \(\theta\), \(\alpha\)1, \(\alpha\)2, \(\beta\)1, and \(\beta\)2). RF-1 uses a grid search method and uses the sum of the validation set accuracy and the area under the ROC curve (AUC) obtained by 5-fold cross-validation of the training set as the target value to find the best hyperparameters.