## 4 Discussion  
Due to the complexity of human emotions, when expressing an emotion, EEG signals from different brain regions respond differently, and there are differences in EEG signals  
Figure 8: Comparison of the recognition accuracy of each subject using different methods on the dominance dimension of the DEAP dataset. Figure 6: Comparison of the recognition accuracy of each subject using different methods on the valence dimension of the DEAP dataset. Figure 7: Comparison of the recognition accuracy of each subject using different methods on the arousal dimension of the DEAP dataset. from different frequency bands [30; 31; 32]. Most of the previous emotion recognition studies are based on EEG signals from whole regions [15; 27; 33]. In this paper, emotional EEG features are extracted from different brain regions and frequency bands, and a more advanced emotion classification performance is realized based on the Capsule-Transformer method. Then, emotion recognition experiments were conducted on each brain region separately, and the results showed that the accuracy of emotion classification of EEG signals in the frontal lobe region was higher than that of other brain regions. [PERSON] et al. [34] found that the frontal and parietal lobes were significantly more strongly activated compared to other brain regions in all frequency bands, suggesting that emotional processing is more closely correlated with these regions. [PERSON] et al. [19] similarly concluded that emotional activity varies in different regions of the brain, and conducted a more detailed regional division of EEG signals, which showed large differences in emotion recognition results in different brain regions. To explore the correlation between frequency band features and emotion recognition, this paper conducted emotion recognition experiments on four EEG signal bands, respectively, and the results showed that Gamma band EEG features have the best emotion classification effect; the higher the frequency band, the stronger the correlation of the EEG signals with human emotions. [PERSON] et al. [35] concluded that high-frequency oscillations of EEG signals have a stronger ability to predict emotions compared to low-frequency oscillations. [PERSON] et al. [36] proposed a deep belief network-based method for detecting key frequency bands in EEG emotion recognition, and concluded that Beta and Gamma band EEG features contain more discriminative information conducive to emotion recognition. This is consistent with the viewpoint of this paper. Accurate and efficient EEG emotion recognition can help diagnose and assess patients with mood disorders such as depression and anxiety. By analyzing EEG signals in key brain regions and frequency bands, physicians can obtain objective information about patients' emotional states more quickly, which can help diagnose and differentiate different emotional disorders more accurately. The findings of this paper can provide information for clinical practice of emotion recognition. The generation of human emotions is a complex physiological activity, and the responses of emotions to EEG signals often vary greatly from person to person [37]. Currently, the stability of the Capsule-Transformer method in cross-subject EEG emotion recognition studies needs to be improved. In addition, the method relies on EEG signal preprocessing and feature extraction work. Manual feature extraction for EEG signals from different brain regions and frequency bands and inputting them into the network can effectively improve the accuracy of emotion recognition, but this increases the workload and complexity of the experiment. In the future, we will collect more emotional EEG data and, based on the method, explore the performance of emotion recognition in more complex scenarios, and explore the kinds of EEG features other than DE that are suitable for emotion recognition.