# Using the \(\beta\)/\(\alpha\) Ratio to Enhance Odor-Induced EEG Emotion Recognition
## 4 Discussion
More interestingly, inhalation of SEO significantly reduced the PSD of the \(\beta/\alpha\) ratio across all ROIs, while inhalation of BEO significantly increased the PSD of the \(\beta/\alpha\) ratio in the right temporal lobe. These findings suggest that inhaling SEO promotes brain relaxation, while inhaling BEO enhances focus, which is consistent with the self-reported results in this study. Additionally, they further support the potential of the \(\beta/\alpha\) ratio as an arousal marker. Our spectral analysis results show that, compared to the RS, inhalation of SEO significantly increases the PSD of the alpha1 rhythm in all ROIs, while the alpha2 rhythm shows a significant increase only in the frontal lobe. In contrast, the inhalation of BEO results in a significant increase in the PSD of both alpha1 and alpha2 rhythms exclusively in the frontal lobe. The differences between the alpha1 and alpha2 results may be due to the former reflecting general task demands, such as attention processes [9; 34], and the latter possibly reflecting specific task demands, such as semantic memory processes [10]. In the final model test, the comprehensive performance of the classifiers was ranked as follows: RF > SVM > DA. This suggests that more complex classifiers are better at identifying emotions induced by odors and reflected by EEG. RF (random forest) likely outperforms others [47] because it can more accurately capture nonlinear relationships between features by integrating multiple decision trees [48]. SVM (support vector machine), while capable of addressing nonlinear problems through kernel functions that map data to high-dimensional space, requires careful parameter tuning [49] and is more sensitive to feature engineering than RF [50]. Consequently, RF surpasses both the linear model (DA) and the kernel-driven SVM. This finding also supports the notion that \"the mapping relationship between EEG signals and emotional states is inherently nonlinear\". Additionally, when comparing models with the new \(\beta/\alpha\) features (DA-2, SVM-2, and RF-2), the performance indices were higher than those of models without these features (DA-1, SVM-1, and RF-1). This indicates that the \(\beta/\alpha\) ratio is directly linked to emotional arousal. Furthermore, the improvement in cross-model performance with the \(\beta/\alpha\) features suggests that the \(\beta/\alpha\) ratio is a universal, robust, and interpretable biomarker. By integrating this emotion marker with advancements in wearable device miniaturization technology [51; 52] and edge computing deployment frameworks [53], this framework enables the construction of an olfactory stimulation-driven EEG emotion recognition model. The model supports rapid and precise identification of diverse emotional states, enables real-time monitoring, and advances the application of olfactory-driven emotion recognition in such domains as aromotherapy, human-computer interaction, and multimedia. The limitations of this study include the following: (a) the absence of additional demographic data (e.g., educational level, ethnicity, and handedness) that may influence EEG signals, potentially limiting the generalizability of findings to broader populations; (b) the evaluation of the model relied solely on a single 80/20 split for training and test sets, which may constrain the generalizability of its performance; (c) controlled laboratory settings may not fully replicate real-world olfactory environments, thereby reducing the ecological validity of the results.