## 3 Experiment and Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Module**} & **Number of Network** & **Number of** & **Number of** \\  & **Parameters** & **Additions** & **Multiplications** \\ \hline Regional & Temporal Encoding & 350,976 & 124,489 & 13,568,576 \\ Brain-Level & Spatial Encoding & 527,616 & 249,864 & 627,200 \\ Encoding & Global Brain-Level & 639,744 & 269,097 & 3,805,440 \\ Encoding and Classification & Total & 1,518,336 & 643,450 & 18,001,216 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Configuration of the proposed model. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Four-Level CL**} & **Three-Level CL** \\  & **(HAHV vs. LAHV vs. HALV vs. LALV)** & **(VAL)** \\ \cline{2-4}  & **DEAP** & **MAHNOB-HCI** & **SEED** \\ \hline HA-BGRU [25] & 74.9 & 75.2 & 81.9 \\  & (10.86) & (8.76) & (11.59) \\ R2G HA-BGRU & 79.4 & 79.0 & 85.8 \\  & (11.03) & (11.46) & (12.10) \\ R2G TF-Encoder & 78.9 & 79.8 & 86.8 \\  & (8.11) & (11.83) & (9.89) \\ R2G HSA-BGRU & 79.5 & 80.4 & 87.3 \\  & (10.16) & (10.34) & (9.58) \\
**HSCFLM** & **83.2** & **83.9** & **90.9** \\  & **(9.04)** & **(9.86)** & **(10.15)** \\ \hline \hline \end{tabular} The standard deviation is provided in parentheses. CL and VAL denote classification and valence, respectively.  
\end{table} 
Table 5: Subject-independent experimental results in four-level classification using the DEAP, MAHNOB-HCI dataset and three-level classification using the SEED dataset. As shown in Tables 3-5, the R2G HA-BGRU, R2G TF-Encoder, R2G HSA-BGRU, and the proposed HSCFLM method exhibited superior performance in all types of experiments on the DEAP, MAHNOB-HCI, and SEED datasets. This indicates that learning spatiotemporal dependencies within and among brain regions by dividing the brain into areas is more effective for recognizing emotions than extracting temporal and spatial features from multichannel EEG signals without dividing the brain into regions. Evidently, the R2G HSA-BGRU and HSCFLM methods, which utilize a hybrid structure combining BGRU and self-attention, present superior performance compared to the R2G TF-Encoder method. This suggests that the hybrid structure can more accurately capture regional and global dependencies from EEG signals to extract high-level global EEG features. Furthermore, upon comparing the performance of the R2G HSA-BGRU and HSCFLM methods, it can be observed that the method that considers both temporal and spatial information improves the accuracy of emotion recognition compared with the method that extracts only temporal information. This approach considers brain activity patterns and complex interactions to better understand and reflect complex patterns of emotions. As a further experiment, we obtained a classification accuracy of 79.1% on the DEAP dataset by dividing the brain region into four primary regions (frontal, parietal, temporal, and occipital lobes) and measuring the performance of the four-level classification of the proposed model. This result was approximately 3.4% lower than the result in Table 4 obtained by dividing the brain into nine regions. Based on this result, it was found that the analysis of various brain regions can improve the accuracy of emotional classification. To evaluate the performance of the proposed model, a within-subject experiments on emotion classification was also conducted, and the results of these experiments are presented in Table 8. Table 8 contains the results of four-level emotion classification experiments using the DEAP and MAHNOB-HCI datasets and three-level emotion classification experiments using the SEED dataset. In addition, a paired t-test was performed for the proposed method and other neural network models to evaluate the statistical difference in emotion classification performance. EEG data for 10, 8, and 5 subjects in the DEAP, MAHNOB-HCI, and SEED datasets, respectively, were applied. A _p_-value less than 0.05 indicates that the difference is statistically significant.