# A Novel Method of Emotion Recognition from Multi-Band EEG Topology Maps Based on ERENet
## 3 Experiments and Results
### Comparison between ERENet and Other Methods
In order to obtain more reliable results, the experiments are repeated five times and different data distribution schemes are adopted for each experiment. The average results of the five experiments are taken as the final results of the task. Table 7 shows the experimental results of all methods. The experimental results in Table 7 show that on the two classification tasks (low/high valence, low/high arousal), ERENet achieves a high accuracy of 95.38% and 95.09% in the intra-subject classification scenario, 68.32% and 67.97% in the inter-subject classification scenario, and 88.47% and 87.52% in the mixed-subject classification scenario. It can be found that the emotion recognition results under different classification scenarios are significantly different, and the classification accuracy of the intra-subject classification scenario is higher than that of the between-subject and subject mixed classification scenarios for all methods, which is due to the variability in the EEG signals of different subjects. Moreover, compared with the traditional method, SVM, the recognition performance of the deep learning methods is better. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Accuracy (\%)**} \\ \cline{2-7}
**Method** & \multicolumn{2}{c}{**Intra-Subject**} & \multicolumn{2}{c}{**Inter-Subject**} & \multicolumn{2}{c}{**Mixed-Subject**} \\ \cline{2-7}  & **Valence** & **Arousal** & **Valence** & **Arousal** & **Valence** & **Arousal** \\ \hline SVM & 70.65 & 71.05 & 41.01 & 42.43 & 63.76 & 63.09 \\
1D-CNN & 74.33 & 71.12 & 45.89 & 43.12 & 65.21 & 63.03 \\
2D-CNN & 81.12 & 80.45 & 58.32 & 59.67 & 72.39 & 72.42 \\ EEGNet [35] & 85.67 & 85.03 & 64.04 & 61.85 & 78.56 & 78.71 \\ ShallowNet [31] & 87.18 & 86.39 & 63.12 & 61.31 & 79.11 & 78.32 \\ CapsNet [11] & 82.24 & 83.37 & 57.28 & 58.79 & 70.73 & 71.28 \\ EmoPercept [50] & 93.56 & 94.79 & 67.13 & 66.09 & 85.32 & 85.66 \\
3D-CNN [51] & 97.89 & 96.56 & 74.32 & 71.42 & 92.12 & 91.79 \\ ERENet (Ours) & 95.38 & 95.09 & 68.32 & 67.97 & 88.47 & 87.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The average accuracies of each method in intra-subject, inter-subject, and mixed-subject classification scenarios. Compared with the baseline methods, such as SVM, 1D-CNN and 2D-CNN, ERENet achieves better results. On the valence and arousal classification tasks, ERENet has an average improvement of 20.01% and 20.88% in the intra-subject classification scenario, 19.91% and 19.56% in the inter-subject classification scenario, and 21.35% and 21.34% in the mixed-subject classification scenario. The input of SVM is a high-dimensional vector, which cannot mine the spatial information between the electrodes, so the recognition performance is poor. Compared with ERENet, the classification accuracy of 1D-CNN and 2D-CNN is lower, because the two models ignore the correlation between frequency bands. Compared with five deep learning methods, not including 3D-CNN [51], ERENet achieves the highest recognition accuracy in three classification scenarios. Compared with EEGNet [35] and ShallowNet [31], the proposed model has similar parameters to theirs, but on the valence and arousal classification tasks, it has an average improvement of 8.96% and 9.38% in the intra-subject classification scenario, 4.74% and 6.39% in the inter-subject classification scenario, and 9.64% and 9.01% in the mixed-subject classification scenario. Compared with other deep learning models, such as CapsNet [11] and EmoPercept [50], the proposed model shows better performance. In the valence and arousal classification tasks, ERENet has an average improvement of 7.48% and 6.01% in the intra-subject classification scenario, 6.12% and 5.53% in the inter-subject classification scenario, and 10.45% and 9.05% in the mixed-subject classification scenario. Although the spatial information and frequency band characteristics of the EEG signals are also considered in CapsNet [11], the spatial representation in CapsNet [11] is not very effective.