# EEG Emotion Classification Based on Graph Convolutional Network
## 2 Literature Review
### Deep Learning Approach  
Recently, extensive research efforts have been devoted to deep learning techniques for EEG-based emotion identification due to the robustness and low requirement for prior knowledge. These techniques can be generally classified according to the type of network used, as those with similar architectures are prone to follow analogous ideas. In previous studies, a common class of deep learning model to address EEG-based emotion classification is long-short term memory (LSTM), which is typically designed to capture temporal dependencies with data sequences. Ref. [21] directly inputs EEG signal into the LSTM network by treating channels as features for each time frame. Similarly, Ref. [7] computes the discrete wavelet transform from the raw signal, followed by the extraction of statistical data. These extracted features are then fed into a network architecture that combines LSTM layers with dense layers for each individual channel. Ref. [15] furtherextends LSTM with a domain adversarial neural network. It involves the extraction of features from each hemisphere using an LSTM-based approach. The domain adversarial network is adopted here to address the challenge of cross-subject variability. These studies demonstrate the strength of LSTM in effectively capturing temporal characteristics from EEG data. A potential drawback of LSTM is that it may hinder the ability to learn the spatial connections among EEG channels. Another type of network widely adopted in emotion recognition with EEG signal is a convolutional neural network (CNN), which uses a shared-weight kernel to slide over data. It is primarily utilized in the area of image analysis due to its advantages for processing data with grid patterns. Ref. [22] examines the power of CNN in terms of architecture, design and training decisions. The results indicate that CNN is capable of learning highly discriminative features when given the proper conditions. Ref. [23] adopts a 3D convolution layer, which is able to learn spatial and temporal features simultaneously. It requires a 3D input representation for the EEG signal by appending consecutive frames together. Ref. [24] develops a compact convolution architecture for EEG-based brain-computer interfaces (BCI). It introduces separable and depthwise convolution, which can not only give extract interpretable features but also reduces the number of parameters. Ref. [14] uses multi-scale convolutional layers to extract temporal and spatial layers. It specifically considers the asymmetrical property in the frontal area of brain. These studies demonstrate that CNN is capable of processing both temporal and spatial aspects of EEG signals. However, an issue that often comes with CNN is the inflexibility when considering the relationships among channels or areas. As the nature of CNN is to presume the grid pattern of input data, it is challenging for CNN to investigate non-Euclidean connectivity. On the other hand, this problem can be handled by a graph-based approach. A graph neural network (GNN) is a class of networks that presents data in a graph structure. In EEG signal tasks, a graph is often constructed by treating each channel as a node, while the formulation of edges could vary. One of the options is to utilize the spatial proximity. Ref. [25] builds a 2D matrix to mark the relative position of electrodes. Then, the adjacency matrix is obtained by thresholding the shortest distance between a node and its neighbors. Ref. [26] establishes the connectivity based on the inverse square function of the physical distance. However, argued by [27], these spatial-based formulations may not represent the real functional connection between channels. To address this problem, it proposes a dynamical graph convolutions neural network that can dynamically learn the intrinsic relationship between nodes. In those works that employ GNN, the advantage of exploring topological structure makes it more adaptable to investigate the relationship between channels.