# EEG Emotion Classification Based on Graph Convolutional Network
## 4 Experiments and Discussions
### Comparison of Different Models  
In our comparison study, we include two baseline models: decision tree (DT) and support vector machine (SVM), which utilize DE as input. Additionally, we evaluate the outcomes of our approaches using the raw signal and DE as input formulation in comparison to the following models:  
* CNN-RNN [35] is a hybrid neural network that combines CNN and RNN to process the spatial and temporal features. * RACNN [47] is the regional-asymmetric convolutional neural network. It firstly extracts the time-frequency features using 1D CNN. Then, the asymmetrical regional features are captured from 2D CNN. * ACRNN [29] is an attention-based convolutional recurrent neural network. In the first stage, it applies CNN to extract spatial features where a channel-wise attention mechanism is employed to determine the importance of different channels. Next, the extracted features are fed into the RNN network that has an extended self-attention mechanism to determine the intrinsic importance of each sample. * DGCNN [27] is a model that utilizes a dynamical graph convolutional network, which maps multi-channel EEG signals into a graph structure by considering each channel as a node and the connection between them as edges. It allows the model to learn the dynamical structure of a graph so that the relationships among nodes are not constrained to geographical proximity. * CapsNet [33] uses the attention mechanism and capsule network to conduct multi-task learning. The attention mechanism is used to capture the importance of each channel. The capsule network consists of multiple capsule layers that not only learn the characteristics required for individual tasks but also the correlations between them. The contrasting results of the DEAP dataset are displayed in Table 2. Among our approaches, RGNet-GCN has outperformed all other methods. Additionally, in our approach, it can be observed that the second best results are yielded from RGNet-CNN and the worst ones come from RGNet-MLP. Such results demonstrate that GCN is more capable of learning regional features than CNN and MLP. When compared to other models, firstly, we can observe that each of the deep learning approaches presented in the table is superior to DT and SVM by a margin of at least 17%. The accuracy derived from the majority of models is under 98%, while our model, RGNet-GCN, is able to attain 98.61%, 98.63% and 98.71%, respectively. For the results of Dreamer dataset shown in Table 3, there is no significant margin between baseline models and some previous deep learning methods. Moreover, the accuracy derived from CNN-RNN is lower than SVM. The highest accuracy achieved by other methods does not exceed 98% for valence and arousal, while the results of our model are over 99% for almost all classes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Feature** & **Valence** & **Arousal** & **Dominance** \\ \hline DT & DE & 67.52/4.79 & 69.59/6.09 & 69.96/9.68 \\ SVM & DE & 71.05/6.11 & 72.18/6.89 & 71.85/8.38 \\ CNN-RNN & raw signal & 89.92/2.96 & 90.81/2.94 & 90.90/3.01 \\ ACRNN & raw signal & 93.72/3.21 & 93.38/3.73 & - \\ DGCNN & DE & 92.55/3.53 & 93.50/3.93 & 93.50/3.69 \\ RACNN & raw signal & 96.65/2.65 & 97.11/2.01 & - \\ MTCA-CapsNet & raw signal & 97.24/1.58 & 97.41/1.47 & 98.35/1.28 \\ \hline RGNet-MLP & raw signal & 98.09/1.66 & 96.99/2.98 & 97.36/2.58 \\ RGNet-CNN & raw signal & 98.21/1.55 & 97.21/2.69 & 97.49/2.35 \\ RGNet-GCN & raw signal & 98.61/1.24 & 98.63/1.26 & 98.71/1.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The comparison of different models on the average accuracy/std(%) of DEAP dataset. other models indicates that our model is able to outperform most of the popular methods for both datasets. Conceptualization, Z.F. and X.X. ; investigation, Z.F. and F.C. ; formal analysis, F.C. ; writing, Z.F. and Y.L. ; visualization, F.C. and X.X. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.