## 1 Introduction  
Emotion is a psychological state arising in response to internal and external stimuli [1]. It is a complex and subjective experience that significantly influences both work and life [2]. Human emotions can be assessed through dimensional affective models, such as the widely used valence-arousal-dominance model [3]. Valence refers to emotional positivity or negativity, Arousal indicates the intensity of the emotion, and dominance signifies the subjective sense of control [4]. Due to its excellent temporal resolution, resistance to deception, and independence from subjective consciousness [5], EEG signals have found widespread applications in emotional assessment for depression patients [6] and real-time emotional monitoring for drivers [7]. Emotion recognition from EEG signals primarily involves two steps: EEG feature extraction and emotional state classification [8]. EEG features are primarily extracted from the time domain and the frequency domain [8]. Time domain features typically describe the temporal characteristics of the signal and have intuitive physical meanings, such as mean, standard deviation, high-order crossover, kurtosis, etc. [9]. Frequency domain features characterize the frequency distribution properties of the signal, including power spectral density (PSD), differential entropy (DE), conditional entropy (CE), and so on [10]. For EEG emotional state classification, traditional machine learning methods have been used a lot in this field. [PERSON] et al. [11] constructed a dynamic functional brain network based on the SEED [12] dataset and used Support Vector Machines (SVM) to conduct emotion recognition studies. [PERSON] et al. [13] classified the extracted nonlinear EEG features based on the empirical mode decomposition (EMD) by feeding them into the Random Forest (RF) to classify the emotional states. However, machine learning methods are often deficient in acquiring deep features and have difficulty in capturing complex nonlinear relationships [14]. Deep learning also has more applications in the field of EEG emotion recognition due to its ability to handle complex tasks and large-scale data [15]. [PERSON] et al. [16] integrated temporal, spatial, and spectral information of EEG signals to cascade convolutional neural networks (CNNs) and Transformer in a new way for emotion recognition tasks. [PERSON] et al. [17] proposed a multilevel feature-guided capsule network (MLF-CapsNet) with multichannel emotion recognition based on whole-brain signals, but did not focus on the variability of different brain regions. [PERSON] et al. [18] utilized a multilayer perceptron (MLP) to perform emotion recognition experiments on different EEG bands, but the study did not address the application of combining multi-band EEG features for emotion recognition. [PERSON] et al. [19] divided the brain into eight regions and used a 1D convolutional LSTM network for emotion recognition, but they only spliced the signals from different brain regions for the experiments, ignoring the different roles and importance of the different brain regions for emotion recognition. For the above problems, this paper proposes the Capsule-Transformer method for EEG signal emotion recognition research. Firstly, the EEG signal channels are categorized into four brain regions (frontal lobe, temporal lobe, parietal lobe, and occipital lobe). Within each brain region's EEG signal channels, emotion features are extracted from different frequency bands, and these features are combined into a feature sequence, serving as the input for the Transformer. The Transformer can capture global information from the entire feature vector. Subsequently, emotional features from different brain regions are combined into primary capsules. Through a dynamic routing mechanism [17] of the capsule network, these primary capsules are aggregated into emotion capsules.