# EEG Emotion Classification Based on Graph Convolutional Network
## 4 Experiments and Discussions
### Experiment on Region Representation  
To study how to generate effective regional features that contribute to emotion recognition, we firstly conduct experiments from the perspective of region encoders and region strategies. The region encoders utilized in experiments are shown in Figure 2. In addition to the GCN region encoder presented in Section 3.2, we introduce two other types of region encoders for comparison:  
* Multi-layer perceptron (MLP) is one of the most common networks. The input \(X^{\prime}_{R_{i}}\) is firstly flattened into a one-dimensional vector, then the multi-layer perceptron is applied: \[Y_{R_{i}}=MLP_{R_{i}}(Flatten(X^{\prime}_{R_{i}})),\] (18) to generate the output \(Y_{R_{i}}\in\mathbb{R}^{F_{out}}\). * Convolutional neural network (CNN) is a class of neural network that is widely used in emotion recognition tasks. It utilizes kernel to move data into a grid pattern. Say the kernel size is \(m\), then we can derive: \[y_{i}=g(\sum_{j}w_{ij}x^{\prime}_{j}+b_{i}),\] (19) where \(x_{j}\) is the \(j\)th feature; \(w_{ij}\) is the kernel weight; \(b_{j}\) is the offset of the \(j\)th feature; \(g(\cdot)\) is the activation function. As can be observed in Figure 2, each of the three types of regional encoders has its own specific structure to handle input features from different perspectives. We intend to examine which structure works the best in generating the regional features. In addition to region encoders, the strategy to define regions is also a critical factor. We provide three different strategies to define the division of brain regions, as shown in Figure 4. Each strategy determines which electrodes are grouped together to form a single region \(R_{i}\), as stated in Section 3.2. Note that the figure is illustrated based on full 10-20 systems while, in practice, we only consider the electrodes that are given by the dataset. The first one is based on the common division of a human brain [50], where the cortex can be segmented into frontal, temporal, parietal and occipital lobes. Despite the fact that the central lobe does not actually exist in the human brain, we still use it as a distinct region based on 10-20 systems to help identify the location of recorded brain activity more precisely [51]. The other two procedures are further divided into more granular pieces, where, in the second one, left, center and right parts are split into different regions and in the third one only symmetric electrode sites are grouped. We adopt these strategies in accordance with earlier studies implying that the asymmetries in brain activity may have a great impact on emotion identification [52; 53]. The main difference between those two strategies is that we set different levels of subdivision to discover which one is best at preserving the distinctive features of asymmetric difference. The results of applying region strategies on different region encoders are shown in Table 1, which records the average accuracy of valence, arousal and dominance on DEAP and Dreamer datasets. An observation obtained in both datasets is that the second and first strategy achieved the best and worst results, respectively. Such findings suggest that the second strategy gives a proper division where the functionality of each region and the interaction among them can be sufficiently captured by our model. In contrast, the first strategy splits the area in a coarser way, making it difficult to preserve the hemisphere structure, while groups in the third strategy show a more dispersed pattern that resembles channel-wise characteristics rather than region-wise features. For the DEAP dataset, we can see that for the second and third region division strategy, the results derived from the MLP region encoder are usually the lowest and GCN can achieve the best. It demonstrates that compared to MLP and CNN, GCN is better at capturing the regional features byconsidering the internal topological structure.