## 3 Experiment and Results
### Experimental Results
From the experimental results, the average accuracy performance of the proposed model was 92.4%, 93.1%, and 97.2% for the DEAP, MAHNOB-HCI, and SEED datasets, respectively, showing average classification accuracy performance that was statistically significantly higher than those of the other methods. Performance evaluation of the brain region, based on the emotional state, was performed using the proposed model, and the results are presented in Table 9. The results indicate that emotions can be recognized similarly in all brain regions. More specifically, in the DEAP database the prefrontal lobe (PF), frontal lobe (F), and parietal lobe (P) achieved better performance for arousal classification, which indicates the degree of emotional activation. In contrast, in the MAHNOB-HCI database, we demonstrated that PF, P, and the right parietal achieved better performance. It is estimated that the PF and F are located in the front of the brain and are connected to numerous brain regions that process emotion-related information; therefore, they are crucial to processing and regulating emotion-related information. These results were similar to the SEED database and were consistent with observations in neuroscience. Furthermore, PF, F, left temporal lobe, and right temporal lobe demonstrated superior performance in valence classification, which represents emotional \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Network** & **Training Time** & **Test Time Per** & **Network** & **Average** \\
**Parameters** & **(h:m:s)** & **EEG Data (ms)** & **Size (MB)** & **Accuracy (\%)** \\ \hline HSCFLM & 1,518,336 & 02:59:07 & 25 & 14.8 & 83.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Network parameters, training time, test time per EEG data, network size, and average recognition accuracy of the proposed model. visualizing internal actions, such as the correlation and interaction among input data, learned features, and output classes. Additionally, we plan to explore methods to enhance the robustness and domain adaptability of the model. This will involve simplifying the model's structure and parameters and reducing the complexity of the hierarchical deep-learning modules through visual verification. Second, the experimental results tend to be lower than those achieved in the field of image and speech recognition. This suggests that the imbalance between the volume of EEG data and the complexity of the model does not yield optimal performance in accurately identifying each emotion category. We will solve this problem by extending the EEG data to ensure a balanced database. Along with this, we will aim to adjust and improve the loss function to identify class samples by adding penalty coefficients to the misclassified class samples. Third, the EEG signals of DEAP and MAHNOB-HCI were recorded using 32 electrodes according to the international 10-20 system, whereas 62 electrodes were used to record the EEG of the SEED. Because the EEG equipment requires a strict laboratory environment, it is difficult to apply EEG-based emotion analysis in practice. Recently, to overcome this problem, portable and reliable EEG devices have been developed, which increases the likelihood of conducting large-scale experiments in real-world applications. Because the number of electrodes in portable EEG devices is relatively small, we intend to contribute to portable EEG devices that can be used in daily life by expanding our future work to explore the location of electrodes to efficiently recognize emotions using our model. Fourth, only a few experiments were conducted for two, three, and four emotional classes in this study. Therefore, various emotional classes must be captured by introducing additional emotional classes. Thus far, we can add an emotion class by extending [PERSON]'s emotional classification axis from two dimensions to multiple dimensions and extend a more granular emotion class using a classification method with a hierarchical structure.