# 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  
Aiming to verify the effectiveness of proposed method, the recognition performance of ERENet is compared with three baseline methods and five deep learning methods. The three baseline methods include SVM, 1D-CNN, and 2D-CNN. The linear function is adopted as the kernel function with a penalty parameter of one in SVM, and the input feature vector is the splicing of all channel features, that is, 4 \(\times\) 32 = 128 dimensions. Both 1D-CNN and 2D-CNN are composed of an input layer, two convolution modules, a flattening layer, two fully connected layers, and an output layer. Each convolution module is composed of a convolution layer, a pooling layer, and a Relu activation layer. In 1D-CNN, the input data are a vector with the length of 32 and the channel number of four. The first convolution layer uses 32 kernels of 1 \(\times\) 16 with a stride of one, and the boundaries \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 in the**} \\ \cline{2-6}  & **Number of Kernels** & **Size of Kernels** & **Number of Kernels** & **Size of Kernels** & **Fully Connected Layers of** \\ \hline
1 & 8 & \(5\times 5\) & 16 & \(5\times 5\) & 0 \\
2 & 8 & \(5\times 5\) & 16 & \(5\times 5\) & 5 \\
3 & 8 & \(5\times 5\) & 16 & \(5\times 5\) & 10 \\
4 & 8 & \(5\times 5\) & 16 & \(5\times 5\) & 20 \\ \hline \hline \end{tabular}  
* Number of Kernels means the number of convolution kernels in each group of feature map after grouping.  
\end{table} 
Table 6: Hyperparameter combination schemes with different numbers of neurons in the fully connected layer 1. Figure 9: Performance comparison of models with different numbers of neurons in the fully connected layer. are padded with zero. Some parameters of the second convolution layer are the same as those of the first convolution layer, except that the number of convolution kernels is set to 128. The max pooling layer with a kernel of 1 \(\times\) 2 and a stride of 1 is adopted after the convolution layer. The number of neurons in the two fully connected layers are 128 and 32, respectively, and the Sigmoid activation function is used in the output layer. In 2D-CNN, the shape of the input data is the same as those of ERENet, and the size of the convolution kernel and the size of the pooling kernel are 5 \(\times\) 5 and 2 \(\times\) 2, respectively. Apart from these parameters, the other parameters are the same as 1D-CNN. The five deep learning methods include EEGNet [35], ShallowNet [31], CapsNet [11], EmoPercept [50], and 3D-CNN [51]. All methods undergo the same preprocessing as ERENet. The performance of all methods in this study is evaluated in three classification scenarios: intra-subject, inter-subject, and mixed-subject. For intra-subject classification, also known as subject-dependent classification, the training data and test data are from a single subject. In this classification scenario, the samples of a single subject are randomly divided into the training set and test set at a ratio of 9:1 for experiments. For inter-subject classification, also known as subject-independent classification, the training data and test data are from several subjects. One subject is selected as the target subject, and the data of this subject is used as the test set, while the other subjects are regarded as the source subjects, and their data are used as the training set. The results of intra-subject and inter-subject classification are the average accuracies of 32 subjects. For mixed-subject classification, the training data and test data are also from several subjects. Unlike inter-subject classification, the source subjects and target subject are not divided in mixed-subject classification, and the samples of all subjects are shuffled and divided into the training set and test set at a ratio of 9:1.