# A Novel Method of Emotion Recognition from Multi-Band EEG Topology Maps Based on ERENet
## 1 Introduction  
In social life, emotion plays an increasingly important role; it not only reflects people's attitude towards objective things, but it also affects people's communication and behavior [1]. The accurate and timely expression and recognition of emotions are the basis of interpersonal communication. However, there are some people in daily life who cannot clearly express their emotions or emotional needs, such as patients with advanced Alzheimer's disease, patients with facial neuritis, autistic patients, and patients with depression. They are in a closed emotional state due to physical or psychological barriers. Emotion recognition can help these patients to convey their emotions in their social environment in real time. The patients' families can more accurately understand their inner thoughts, and doctors can properly evaluate their emotional state and formulate appropriate treatment plans according to their personal conditions, which can benefit their lives and their health. Therefore, research on human emotion recognition is of great significance, and has become an important research hotspot in neuroscience, psychology, computer science, cognitive science, and other fields. Several methods may be used for emotion recognition, which can be divided into two categories: non-physiological signals and physiological signals. Non-physiological signals include facial expressions, body movements, speech, and language, while physiological signals include EEG signals, ECG signals, Galvanic skin response signals, EMG signals, etc. However, there are some people who cannot express their emotions through facial expressions or language, such as patients with severe facial neuritis. Besides, non-physiological signals can be deliberately disguised and do not always reflect people's objective physiological emotions. Therefore, few studies have been conducted on emotion recognition based on non-physiological signals. Physiological characteristics, such as EEG signals, are difficult to be manipulated or changed manually, which can reflect the real objective physiological emotions of people. Additionally, EEG signals respond faster to emotional changes andhave a higher time resolution [2]. Moreover, EEG signals are directly related to neuronal activities, which can provide more information in the process of emotion generation. Therefore, it has also become a powerful support for emotion recognition technology. In recent years, with the emergence of EEG acquisition devices with wearability, portability, and ease of use, it has become possible to record and analyze multi-channel EEG signals, which further promotes the development of emotion recognition research based on EEG signals. Due to the low signal-to-noise ratio and non-stationary characteristics of EEG signals, many studies have been conducted on how to extract key identifiable features from EEG signals for emotion recognition. The commonly used features mainly include time domain features, frequency domain features, and time-frequency domain features. Time domain features are mainly signal statistics in the time domain, such as amplitude [3], mean [4], and standard deviation [5]. In addition, differential entropy (DE) [6; 7], event-related potentials (ERP) [8], high-order cross features (HOC) [9], non-stationary index (NSI) [9; 10] and fractal dimension (FD) [10] have also been proved to be effective time-domain features. In frequency domain analysis, fast Fourier transform (FFT) [11], Welch [12], and other methods are used to extract the power spectrum and power spectral density (PSD) [13; 14] in EEG signals. In addition, the auto-regressive coefficient (AR) [15] has also been introduced for EEG emotion recognition. Time frequency feature extraction methods mainly include the short-time Fourier transform (SIFT) [16; 17; 18; 19], wavelet packet transform [20], and empirical mode decomposition (EMD) [21].