## 3 Experiments
### Comparison with Existing Methods  
In this paper, we compare the classification accuracy of the Capsule-Transformer method with seven representative EEG signal emotion recognition methods, including three traditional and four deep learning methods, and all of them are manually reproduced. The three traditional methods are KNN [23], SVM [24], and MLP [25]. The SVM is nonlinear and uses a Gaussian kernel function. The data inputs for KNN and SVM are DE features \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Label**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Accuracy (\%)**} & \multicolumn{2}{c}{**Precision**} & \multirow{2}{*}{**Recall (\%)**} & \multirow{2}{*}{**F1 (\%)**} \\  & & & & & \\ \hline \multirow{3}{*}{Valence} & CapsNet & 84.25 & 85.37 & 86.32 & 84.81 \\  & Transformer & 91.36 & 92.28 & 91.67 & 90.89 \\  & Ours & **96.75** & **97.22** & **96.64** & **96.59** \\ \hline \multirow{3}{*}{Arousal} & CapsNet & 85.31 & 84.27 & 86.62 & 85.29 \\  & Transformer & 91.67 & 90.83 & 92.49 & 91.22 \\  & Ours & **96.88** & **96.75** & **97.18** & **96.63** \\ \hline \multirow{3}{*}{Dominance} & CapsNet & 84.22 & 85.63 & 84.81 & 84.53 \\  & Transformer & 92.77 & 92.43 & 92.57 & 91.59 \\ \cline{1-1}  & Ours & **96.25** & **95.93** & **96.36** & **96.18** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The comparison of Capsule–Transformer, Transformer, and CapsNet. Figure 5: The comparison of emotion recognition in different frequency bands. extracted from four frequency bands (\(\theta\), \(\alpha\), \(\beta\), \(\gamma\)), and the data inputs for MLP are the raw EEG signals after preprocessing. The four deep learning methods are parallel convolutional recurrent neural network (CNN-RNN) [26], continuous convolutional neural network (Conti-CNN) [27], depthwise convolutional Transformer encoder (DCoT) [28], and the spatial-frequency convolutional self-attention network (SFCSAN) [29]. The CNN-RNN combines the advantages of convolutional neural networks and recurrent neural networks to effectively extract spatial and temporal features of EEG signals. The Conti-CNN combines features of different frequency band signals to construct 3D EEG signal cubes as input and perform emotion recognition. The DCoT combines depthwise convolution and Transformer encoders to capture the dependence of emotion recognition on each EEG channel. The SFCSAN combines spatial and frequency band information to fully use the spatial and frequency domain information of EEG signals for emotion recognition. CNN-RNN takes the preprocessed raw EEG signals as the data input to the model, and the data inputs for Conti-CNN, DCoT, and SFCSAN are DE features extracted from the four frequency bands (\(\theta\), \(\alpha\), \(\beta\), \(\gamma\)), respectively. In addition, all methods used the same data preprocessing method, training parameters, and sliding window length, which facilitated the fairness of the comparison experiments. As shown in Table 5, compared to the traditional KNN, SVM, and MLP methods, the Capsule-Transformer method has a large advantage, with a large increase in the average classification accuracy in all three dimensions. In addition, compared to the other four deep learning methods, the Capsule-Transformer method still shows some advantages. First, compared with the two CNN-based methods (CNN-RNN, Conti-CNN), the Capsule-Transformer method improves the average classification accuracy in each of the three dimensions by about 10%. Second, compared to DCoT using the channel attention mechanism, SFCSAN using convolutional self-attention, the Capsule-Transformer model still achieves better classification performance on the three classification tasks. Figures 6-8 give the line graphs of the results of 32 subjects' emotion recognition based on the different methods mentioned above in the three classification tasks of valence, arousal, and dominance, where the horizontal axis is the number of subjects, and the vertical axis is the accuracy rate of emotion classification under each dimension.