## 3 Experiment and Results
### Results  
Tables 1-4 summarize the average EEG emotional stress state recognition performance results of the different methods. The proposed 3 DCGSA method achieved the highest recognition rate on all datasets, with accuracies of 96.68% for DEAP, 95.64% for VRE, 91.52% for two-level EDESC, and 90.12% for three-level EDESC. Although the recognition accuracies of SF3 DCRA and SFCSAN were lower than that of 3 DCGSA, it was superior to the other nine methods. This indicates that the methods applying the attention mechanism to each frequency band, primarily associated with EEG activity, resulted in a more effective recognition performance than the methods applying the attention mechanism to the raw EEG signal. HBGRUA, PLNTF, GTR, and 3 DRADNN, which combine deep neural networks and attention mechanisms, exhibited better recognition accuracy than EF-SVM and RF-FS, verifying that attention mechanisms positively and strongly affect deep neural networks. However, although the attention mechanism was reflected in the case of HBGRUA, the recognition accuracy was slightly lower than that of CRNN. RF-FS had the  
lowest recognition accuracy. In addition to recognition accuracy, the proposed 3 DCGSA provides the best performance in terms of precision and F1-measure. This confirms that the extraction of the discriminative EEG features of multiple frequency bands through a 3D convolutional gated self-attention network in order to map them onto the final attention representation, and that the learning of global frequency band information to yield better results, are valid methods of analysis. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **F-Measure** \\ \hline RF-FS & 79.84 & 75.64 & 76.03 \\ EF-SVM & 81.45 & 81.31 & 81.07 \\
2D-AlexNet & 81.83 & 83.51 & 82.11 \\ HBGRUA & 84.62 & 84.52 & 83.94 \\ CRNNN & 86.77 & 86.58 & 86.16 \\ SF3 DCNN & 87.13 & 86.94 & 86.52 \\ PLNTF & 88.42 & 86.89 & 86.75 \\ GTR & 89.67 & 89.77 & 88.65 \\
3 DRADNN & 91.46 & 91.27 & 90.85 \\ SFSCAN & 92.83 & 92.76 & 92.15 \\ SF3 DCRA & 94.52 & 94.62 & 93.50 \\
3 DCGSA & 96.68 & 96.77 & 96.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Stress recognition results using the DEAP dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **F-Measure** \\ \hline RF-FS & 75.79 & 77.47 & 77.09 \\ EF-SVM & 77.78 & 77.64 & 76.40 \\
2D-AlexNet & 79.57 & 79.38 & 78.96 \\ HBGRUA & 81.78 & 80.25 & 80.11 \\ CRNNN & 83.92 & 85.60 & 85.22 \\ SF3 DCNN & 85.63 & 85.56 & 84.96 \\ PLNTF & 86.53 & 86.62 & 86.24 \\ GTR & 87.97 & 87.87 & 87.29 \\
3 DRADNN & 89.59 & 91.27 & 90.89 \\ SFSCAN & 92.74 & 92.55 & 92.13 \\ SF3 DCRA & 93.61 & 94.9 & 93.91 \\
3 DCGSA & 95.64 & 95.57 & 94.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Stress recognition results using the VRE dataset. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **F-Measure** \\ \hline RF-FS & 74.52 & 77.60 & 76.20 \\ EF-SVM & 76.13 & 77.81 & 77.43 \\
2D-AlexNet & 78.56 & 78.65 & 78.27 \\ HBGRUA & 80.13 & 80.03 & 79.45 \\ CRNN & 81.03 & 82.71 & 82.33 \\ SF3 DCNN & 82.12 & 82.05 & 81.44 \\ PLNTF & 83.17 & 83.26 & 82.88 \\ GTR & 84.34 & 84.4 & 83.66 \\
3 DRADNN & 86.25 & 84.72 & 84.58 \\ SFSCAN & 87.47 & 87.28 & 86.86 \\ SF3 DCRA & 89.48 & 89.57 & 89.19 \\
3 DCGSA & 91.52 & 93.20 & 92.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Stress recognition results using the two-level EDESC dataset. The stress recognition rate, precision, and F1-measure in VRE were lower than those in DEAP because some participants were likely distracted and not entirely immersed in the virtual environment. Additionally, a comparison of the recognition rates, precisions, and F1-measures of the three datasets indicated that the EDESC dataset contained more sensitive data for stress recognition than the DEAP or VRE datasets.