# A Novel Method of Emotion Recognition from Multi-Band EEG Topology Maps Based on ERENet
## 1 Introduction
In the early days, many traditional machine learning methods were adopted to identify emotional states, mainly including the Bayesian network [22], K-nearest neighbor (KNN) [14; 23; 24], support vector machine (SVM) [14; 25; 26; 27], and hidden Markov model (HMM) [28]. In recent years, more and more deep-learning-based methods have been widely used in emotion recognition tasks due to the excellent performance of deep learning in the field of vision. [PERSON] et al. [29] proposed a self-organized graph neural network (SOGNN) for cross-subject EEG emotion recognition. [PERSON] et al. [30] proposed a new EEG classification method based on convolutional neural network (CNN), which considered the spatial configuration of EEG sensors and adopted a new EEG analysis model, 2D-IDR, to construct 2D image representations of EEG samples. [PERSON] et al. [31] proposed a series of deep ConvNets with different architectures for the end-to-end learning of EEGs and proved the effectiveness of ConvNets in spatial mapping learning features by visualizing the learned EEG features. [PERSON] et al. [32] proposed a convolution recurrent neural network (ACRNN) based on the attention mechanism for EEG emotion recognition, which uses the channel attention mechanism to learn the spatial features of multi-channel EEG signals in convolution layers and uses an extended self-attention mechanism to explore the temporal features of different time slices in an LSTM network. [PERSON] et al. [33] proposed a novel deep learning framework consisting of unsupervised long short-term memory (LSTM) with a channel attention autoencoder and CNN with an attention framework for performing the task of subject-independent emotion recognition. [PERSON] et al. [34] proposed a novel model named MDGCN-SRCNN, which combines the graph convolution network (GCN) and CNN and aims to fully learn spatial features at different levels and deep abstract features to distinguish different emotions. Compared with traditional machine learning methods, deep learning methods can better dig out the internal information of EEG signals and show great potential in emotion recognition based on multi-channel EEGs. However, most emotion recognition methods at present are limited to recognizing emotional states from time domain features, frequency domain features, and time-frequency domain features of EEG signals. Little research has been conducted on the spatial topological characteristics and frequency band characteristics of multi-channel EEG signals. There are some methods [32; 35; 36] that try to learn spatial features from a multi-channel EEG time series that is a 2D matrix, with one dimension representing the electrodes and the other dimension representing the time series. The EEG time slices only aggregate all of the electrode measurements together, and the correlation between EEG changes in different brain regions cannot be mined from them. [PERSON] et al. [37] found that the left hemisphere is more active in positive emotions and the right hemisphere is more active in negative emotions, which has also been confirmed by many studies [38; 39]. [PERSON] et al. [40] pointed out that for valence recognition, prefrontal asymmetry and parietal asymmetry are present in the alpha band; temporal asymmetry is observable in the gamma band; and for arousal recognition, prefrontal asymmetry in the alpha band and temporal asymmetry in the gamma band are present. The spatial information of EEG signals and the characteristic information of each frequency band contain significant information related to emotional states, which is of great significance for the discrimination of emotional states. Therefore, how to combine the frequency features of EEG signals with spatial information and frequency band characteristics is a key problem. In view of the above reasons, a new method of emotion recognition based on multi-band EEG topology maps is proposed in this paper.