## 2 Related Work  
In the realm of emotion recognition, machine learning has traditionally been a popular approach for simple classification tasks [6]. Some prominent algorithms, including support vector machine (SVM), decision trees (DT), and k-nearest neighbour (KNN), have been successfully utilized in this field. [PERSON] [7] used the discrete wavelet transform (DWT) to extract temporal-frequency information and applied the maximum relevancy and minimum redundancy algorithm (mRMR) to select the most relevant features. In the literature [8; 9], wavelet transform (WT) was applied for sub-band EEG signal decomposition, and the processed smooth feature information was then fed into SVM for classification. This method demonstrated promising improvement in accuracy for EEG emotion-state recognition in machine learning. However, machine learning techniques still face significant limitations when processing nonlinear and indistinguishable data, which restrict their capability for more complex classification tasks. With the advent of deep learning, the machine learning limitations are gradually being overcome, and deep learning techniques are successfully moving in the direction of EEG emotions. Typically, deep-learning-based approaches focus on feature extraction from three dimensions: temporal, frequency, and spatial. For instance, in terms of the temporal information of EEG signals, [PERSON] [10] proposed a framework combining a stacked autoencoder (SAE) and long short-term memory neural networks (LSTM). SAE is employed to simulate the mixing process in EEG and to separate the source signals. Then, the source signals are framed, and frequency features are extracted and combined into chained data, followed by discriminative classification using LSTM. [PERSON] [11] designed the multimodal residual LSTM (MMResLSTM) network, using different LSTM layers to learn the temporal characteristics of different physiological signals and share parameters to achieve the information interaction of different modal data. References in the literature [12; 13; 14] introduced a temporal learning architecture that employs a 1D convolutional neural network (CNN) to extract temporal information from multichannel chained data. Studies in neuroscience and psychology have proven that the \(\delta\)-band (1-4 Hz), \(\theta\)-band (4-8 Hz), \(\alpha\)-band (8-13 Hz), \(\beta\)-band (13-30 Hz), and \(\gamma\)-band (30-50 Hz) of EEG [15] are associated with human emotions. Therefore, during the frequency domain feature extraction process, EEG signals are typically mapped onto these five frequency bands, and sub-band features are extracted. [PERSON] [16] extracted EEG signal differential entropy (DE) features on a sub-band and performed emotion recognition on a deep belief network (DBN). [PERSON] [17] extracted DE, power spectral density (PSD), differential asymmetry (DASM), and rational asymmetry (RASM) in four representative EEG datasets and trained them for classification by the proposed dynamic graph convolutional neural network (DGCNN). The results showed that the DE and PSD features could convey the most discriminative information. Researchers have been interested in exploring physical models between electrode positions to characterize spatial features in EEG signals. [PERSON] [18] proposed a method to generate an image by performing a polar coordinate projection of the channel DE features and using different interpolation methods to fill the blank space after the projection, thus proving that the spatial topology based on electrode arrangement is effective. [PERSON] [19] utilized graph theoretic ideas to model multichannel EEG signals and used DGCNN to explore the depth spatial information of neighbouring channels. Other studies [20; 21; 22; 23] constructed a connectivity matrix containing structural information of the brain to express features in different ways and then input the rearranged EEG signals into an end-to-end CNN model. The researchers have also considered various feature information combinations.