# Using the \(\beta\)/\(\alpha\) Ratio to Enhance Odor-Induced EEG Emotion Recognition
## 1 Introduction  
Emotion recognition has garnered increasing attention from researchers across various interdisciplinary fields. A primary scientific problem in affective computing lies in enabling computers to accurately process, identify, and interpret emotional information conveyed by humans [1]. Although facial expressions and vocal cues offer valuable insights, they are highly susceptible to individual and cultural differences, potentially reducing the accuracy of emotion recognition [2]. Therefore, it is essential to employ more direct and objective methods for assessing emotional states. The electroencephalography (EEG) method, which records the electrical activity of pyramidal neurons in the cerebral cortex as a physiological indicator [3], is characterized by being non-invasive, rapid, portable, possessing high temporal resolution, providing objective measurement of emotions, and being sensitive to emotional changes [4]. It has attracted significant attention from both academia and industry [5, 6, 7]. In emotion recognition research using EEG signals, features are typically extracted from the \(\delta\), \(\theta\), \(\alpha\), and \(\beta\) frequency bands. However, subdivided frequency bands (\(\alpha\)1, \(\alpha\)2, \(\beta\)1, and \(\beta\)2) contain more detailed emotional information [8; 9; 10], which can enhance model performance [11; 12]. Furthermore, the \(\beta\)/\(\alpha\) ratio is often regarded as an indicator of arousal, as a higher ratio is associated with greater attentional focus [13; 14; 15]. This ratio effectively represents emotions in the arousal dimension. Consequently, incorporating the \(\beta\)/\(\alpha\) ratio into the feature construction of emotion recognition models is a potential means to improve their performance. With the growing popularity of olfactory aromatherapy, individuals are increasingly using essential oils to regulate their mood, as this method is convenient, fast-acting, and safe [16]. Due to their distinct chemical compositions, different essential oils can elicit specific emotional responses. For instance, [PERSON] et al. [17] investigated the relationship between emotional behavior in mice and brain concentrations of (+)-\(\alpha\)-santalol, the primary volatile component of sandalwood essential oil (SEO). They observed its transfer to the brain, indicating that this compound exerts a sedative effect through pharmacological mechanisms. Similarly, [PERSON] et al. [18] examined the effects of inhaling bergamot essential oil (BEO) on depression-like behaviors and hippocampal neural plasticity in rats subjected to chronic unpredictable mild stress (CUMS). Their findings suggested that BEO alleviates depressive symptoms by preserving hippocampal neuronal plasticity, thereby enhancing mood. This approach of modulating emotions via olfactory stimulation has attracted significant attention from researchers in the field of emotion recognition [19; 20]. A comprehensive understanding of emotions is beneficial to emotional recognition research. In psychology and computer vision, emotions are classified as categorical or dimensional models [21; 22; 23]. In the categorical model, [PERSON] et al. [23] defined the basic human emotions as happiness, anger, disgust, fear, sadness, and surprise. [PERSON] et al. [24] utilized three neural network algorithms (ShallowFBCSPNet, Deep4 Net, and EEGNet4) to recognize emotions, including happiness, sadness, disgust, neutrality, and fear, in the SEED-V dataset. In contrast, the dimensional emotional model quantifies emotions along multiple dimensions, including valence, arousal, and dominance, thereby capturing more complex emotional states [22]. [PERSON] et al. [25] established a model based on pleasant, neutral, and unpleasant odors, finding that the model was particularly sensitive to unpleasant smells and exhibited good classification performance. However, the accuracy of the model decreased markedly when distinguishing between pleasant and neutral odors.