## 3 Experiment and Results
### Experimental Results  
For the various experiments evaluating the performance of the proposed model, as shown in Tables 1 and 2, the labels of the DEAP and MAHNOB-HCI datasets were redefined for several levels of emotion classes based on a rating value of 1-9 in terms of valence, arousal, and dominance. Additionally, by combining each of the two classes for valence and arousal presented in Table 1, the classes for the four-level emotion classification were defined as follows: high valence and high arousal (HVHA), low valence and high arousal (LVHA), low valence and low arousal (LVLA), and high valence and low arousal (HVLA). Because the SEED dataset is labeled as negative (\(-\)1), neutral (0), or positive (1), it was only applied to the three-level emotion classification experiment in terms of valence without relabeling. Tables 3-5 show the results of two to four levels of emotion classification using the three datasets. All emotion classification results in Tables 3-5 were calculated using leave-one-subject-out (LOSO) cross-validation evaluation [40]. This evaluation method sets the EEG data of one subject as a test set and uses the EEG data of all the remaining subjects as a training set. This process was repeated until each participant's data were used as a test set at least once. That is, the test and training data were always selected from different \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rating Values** & **Valence** & **Arousal** & **Dominance** \\ \hline \(1\leq\) RVs \(\leq\) 5 & Low & Low & Low \\
6 \(\leq\) RVs \(\leq\) 9 & High & High & High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Emotion classes for two-level emotion classification on the DEAP and MAHNOB-HCI datasets. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rating Values** & **Valence** & **Arousal** & **Dominance** \\ \hline \(1\leq\) RVs \(\leq\) 3 & Negative & Activated & Controlled \\
4 \(\leq\) RVs \(\leq\) 6 & Neutral & Moderate & Moderate \\
7 \(\leq\) RVs \(\leq\) 9 & Positive & Deactivated & Overpowered \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Emotion classes for three-level emotion classification on the DEAP and MAHNOB-HCI datasets. subjects. This approach can be used to evaluate the generalizability of the proposed model to new subjects by learning general patterns. The final classification performance results were obtained by calculating the average of all the cross-validation folds. First, as shown in Tables 3-5, the accuracy of the emotion classification gradually decreased as the number of classes increased from two to four in terms of valence, arousal, and dominance in the DEAP and MAHNOB-HCI datasets. This is because as the number of emotion classes to be distinguished increases, the number and complexity of patterns that the model needs to learn increases. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**Two-Level CL**} & \multicolumn{4}{c}{**Three-Level CL**} \\ \cline{2-7}  & **VAL** & **ARO** & **DOM** & **VAL** & **ARO** & **DOM** \\ \hline \multirow{2}{*}{HA-BGRU [25]} & 84.6 & 82.7 & 84.3 & 80.2 & 79.8 & 80.7 \\  & (10.90) & (8.25) & (9.67) & (8.66) & (9.60) & (9.61) \\ \multirow{2}{*}{R2G HA-BGRU} & 88.6 & 87.1 & 88.3 & 84.5 & 84.4 & 84.8 \\  & (8.57) & (8.64) & (9.95) & (11.30) & (10.45) & (9.48) \\ \multirow{2}{*}{R2G TF-Encoder} & 89.2 & 87.8 & 88.9 & 85.1 & 84.9 & 85.4 \\  & (8.07) & (10.74) & (8.86) & (10.32) & (11.27) & (10.90) \\ \multirow{2}{*}{R2G HSA-BGRU} & 90.1 & 88.5 & 89.5 & 85.8 & 85.6 & 86.1 \\  & (10.22) & (10.95) & (9.65) & (9.89) & (8.42) & (11.13) \\ \multirow{2}{*}{**HSCFLM**} & **93.3** & **91.6** & **92.8** & **88.9** & **89.1** & **89.4** \\  & **(9.74)** & **(10.71)** & **(8.99)** & **(10.62)** & **(8.89)** & **(10.38)** \\ \hline \hline \end{tabular} The standard deviation is provided in parentheses. CL, VAL, ARO, and DOM denote classification, valence, arousal, and dominance, respectively.  
\end{table}