## 3 Experiment and Results
### Experimental Results

Table 4: Experimental results of subject-independent emotion classification using the MAHNOB-HCI dataset. \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}{*}{CNN} & 69.7 & 66.7 & 70.1 & 65.3 & 64.7 & 65.9 \\  & (11.82) & (9.50) & (11.84) & (10.09) & (10.43) & (8.94) \\ \multirow{2}{*}{LSTM} & 75.2 & 72.3 & 75.3 & 71.1 & 69.7 & 71.6 \\  & (11.56) & (10.30) & (9.11) & (8.89) & (11.13) & (9.46) \\ \multirow{2}{*}{BGRU} & 76.8 & 74.4 & 77.2 & 73.2 & 71.5 & 73.1 \\  & (11.62) & (11.45) & (8.96) & (9.02) & (8.60) & (8.81) \\ \multirow{2}{*}{CRNN [24]} & 81.1 & 78.9 & 81.4 & 77.4 & 76.1 & 77.4 \\  & (9.21) & (11.31) & (11.01) & (11.04) & (10.43) & (10.58) \\ \multirow{2}{*}{HA-BGRU [25]} & 83.4 & 81.5 & 84.1 & 80.2 & 78.8 & 80.1 \\  & (10.26) & (10.04) & (9.42) & (9.48) & (10.12) & (11.34) \\ \multirow{2}{*}{R2G HA-BGRU} & 87.6 & 85.3 & 88.1 & 83.9 & 82.9 & 84.1 \\  & (9.73) & (9.46) & (11.66) & (10.54) & (11.14) & (10.92) \\ \multirow{2}{*}{R2G TF-Encoder} & 88.1 & 86.4 & 88.4 & 84.5 & 83.5 & 84.5 \\  & (10.05) & (8.37) & (9.55) & (10.95) & (11.29) & (11.14) \\ \multirow{2}{*}{R2G HSA-BGRU} & 88.4 & 87.1 & 89.3 & 85.1 & 84.2 & 85.2 \\  & (8.95) & (10.79) & (10.59) & (9.31) & (10.43) & (11.20) \\ \multirow{2}{*}{**HSCFLM**} & **92.1** & **90.5** & **92.3** & **88.5** & **87.4** & **88.3** \\  & **(9.16)** & **(9.81)** & **(8.94)** & **(8.52)** & **(8.35)** & **(9.76)** \\ \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} 
Table 3: Experimental results of subject-independent emotion classification using the DEAP dataset. For the two- and three-level emotion classifications using the DEAP dataset, as shown in Table 3, the classification accuracy was high in the order of dominance, valence, and arousal for most of the methods. Alternatively, it exhibited the lowest classification performance in terms of arousal. Similarly, two- and three-level classification using the MAHNOBHCI dataset, as presented in Table 4, showed the same tendency with the lowest classification performance in terms of arousal. This suggests that arousal may represent more complex EEG patterns than either dominance or valence. Additionally, the three-level emotion classification exhibited the highest performance in the SEED dataset among the three datasets. This may be related to the SEED dataset, which employs more spatiotemporal information from numerous local brain regions and more electrodes than the DEAP and MAHNOBHCI datasets. In all subject-independent experiments, the proposed method achieved the highest emotion classification accuracy. Table 6 shows the number of learnable parameters to obtain various feature vectors in the application of the proposed HSCFLM. During simulation by connecting the modules of the proposed method, the slopes of all parameters were calculated and optimized using the cross-entropy loss. The obtained optimal parameters were applied to the proposed architecture to provide the highest classification performance. In addition, Table 7 shows the network parameters, training time, test time per EEG data, network size, and average recognition accuracy performance. This represents the complexity of the model from a quantitative perspective. This information was obtained from hardware environments using Windows 10, Intel Core i5 8500 processor, NVIDIA GeForce GTX 1070 Ti GPU, DDR4 32 GB RAM, and software environments using TensorFlow 2.9.1 and Python 3.10.8.