# A Novel Method of Emotion Recognition from Multi-Band EEG Topology Maps Based on ERENet
## 3 Experiments and Results
### Selection of Hyperparameters  
Different selections of hyperparameters will affect the performance of the model. Therefore, some controlled experiments on some hyperparameters are conducted for find  
Figure 6: (**a**) Traditional classification layer; (**b**) Weighted classification module. ing the best combination scheme of hyperparameters in the model. They are evaluated using the validation set, and the combination scheme of hyperparameters with the highest recognition accuracy can finally be adopted. The hyperparameters involved include the number and size of convolution kernels in the group convolution layer of the multi-band discrete parallel processing module, the number and size of convolution kernels in the group convolution layer of the multi-band information exchange and reorganization module, and the number of neurons in the fully connected layer of the weighted classification module. To ensure the reliability of the results, a 10-fold cross validation strategy is employed to evaluate the performance of the model under different combination schemes. The samples of all subjects are shuffled and divided into a training set and test set in the ratio of 9:1, and the divided test set is used to test the model in the next subsection. The samples in the training set are randomly divided into 10 equal data subsets, one of which is used as the verification set and the other nine subsets constitute the training set. The process is repeated 10 times, and the average accuracy of the 10 experiments is taken as the experimental result. Aiming to choose the number of convolution kernels that makes the model perform best, four groups of hyperparameter combination schemes are designed, as shown in Table 3.  
In Table 3, only the number of convolution kernels is different in the four groups of schemes, and the other hyperparameters are the same. Among them, the convolution kernels in module 1 and module 2 are set to 3 \(\times\) 3, and the number of neurons in the fully connected layers in module 3 is set to 5. The emotion recognition results of models with different numbers of convolution kernels are shown in Figure 7.  
It can be seen from the experimental results in Figure 7 that with the increase in the number of convolution kernels, the accuracy recognition of the models is gradually increasing. However, there is a problem: as the number of convolution kernels increases, the amount of model parameters and the computational complexity also increase, and the number of parameters of each scheme is about twice that of the previous scheme, as \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scheme**} & \multicolumn{2}{c}{**Group Convolution of Module 1**} & \multicolumn{2}{c}{**Group Convolution of Module 2**} & \multicolumn{2}{c}{**Number of Neurons**} \\  & \multicolumn{2}{c}{**Number of Kernels**} & \multicolumn{2}{c}{**Size of Kernels**} & \multicolumn{2}{c}{**Number of Kernels**} & \multicolumn{2}{c}{**Size of Kernels**} \\ \hline
1 & 8 & \(3\times 3\) & 8 & \(3\times 3\) & 5 \\
2 & 8 & \(3\times 3\) & 16 & \(3\times 3\) & 5 \\
3 & 16 & \(3\times 3\) & 16 & \(3\times 3\) & 5 \\
4 & 16 & \(3\times 3\) & 32 & \(3\times 3\) & 5 \\ \hline \hline \end{tabular} \({}^{1}\) Number of Kernels means the number of convolution kernels in each group of feature map after grouping.  
\end{table} 
Table 3: Hyperparameter combination schemes with different number of convolution kernels \({}^{1}\). Figure 7: Performance comparison of models with different numbers of convolution kernels. shown in Table 4. It can also be observed from Figure 7 that scheme 2, scheme 3, and scheme 4 have better recognition performance compared with scheme 1, and the difference in recognition accuracy of the three schemes is small. Therefore, scheme 2 with relatively few parameters is a more appropriate choice among the latter three schemes with better recognition performance.