## 2 Related Work  
Deep learning is a key element in the research of EEG emotion recognition, and more and more excellent models are being applied to EEG emotion recognition with more in depth research in the field of deep learning. The ability of convolutional neural networks to learn local smooth structures and evolve them into multi-scale hierarchical patterns has led to great breakthroughs in processing tasks such as images, speech, and video [15]. [PERSON] [8] used a one-dimensional CNN to process temporal, frequency, and location information for EEG extraction. [PERSON] [9] converted EEG signals into multi-spectral topological image sequences and used a two-dimensional convolutional network for feature extraction. [PERSON] [16] undertook experiments on the DEAP dataset using CNN with good results. [PERSON] et al. [17] used a hierarchical convolutional neural network (HCNN)to verify the feasibility of this approach on the SEED dataset compared with traditional methods. [PERSON] et al. [12] deeply integrated the complexity of the EEG signal, the spatial structure of the brain, and the temporal context of emotion formation to obtain a 4D feature tensor fed into CNN to extract features and obtained better results. [PERSON] et al. [18] used CNN to handle a four-category emotion task. Convolutional neural networks have a great role in the feature extraction of EEG signals. One-dimensional convolutional networks are suitable for the direct processing of EEG signals [8; 17], but in the processing of information about images [9; 12; 16; 18], two-dimensional convolution is more effective. Recurrent neural networks (RNN) with good addressing capability for time series data are commonly used in natural language processing. In the development of deep learning, LSTM, which can better handle long-term dependencies, has become an effective scalable model for recurrent neural networks to solve the learning problem of sequence data. [PERSON] et al. [9] used LSTM to process the features extracted from CNN and achieved better results on the DEAP dataset. The accuracy was 90.62% in the high and low valence test, 86.13% in the high and low arousal test, and 86.23% in the liking test. [PERSON] et al. [18] used LSTM to fuse the spatial, frequency domain, and temporal features of the original EEG signal to make progress in a four-classification task. Many researchers currently are starting to use BiLSTM networks more and more, due to their ability to learn past and future information in time series and get better results than LSTM. [PERSON] et al. [10] proposed a bidirectional neural network for EEG-based image recognition. They used bi-directional long short-term memory (BiLSTM) as a feature encoder and then used independent component analysis (ICA) and support vector machine (SVM) to classify the features which achieved advanced results. [PERSON] et al. [11] proposed an end-to-end BiLSTM model with a neural attention mechanism for EEG-based target recognition tasks. [PERSON] et al. [12] used BiLSTM to serialize feature-specific information processed by CNN and then learn the relationship between past and future in the information, achieving significant improvement with an average accuracy of 94% in the DEAP dataset. [PERSON] et al. [19] used the BiLSTM model for experimental validation on both dichotomous and quadruple classification, with significant room for improvement on the quadruple classification task. In terms of model selection for dealing with EEG emotion recognition. The use of Conv-LSTM in the literature [20] has a good performance in processing time series, and advanced results were obtained on all three datasets. BiLSTM has more obvious advantages over LSTM for learning emotional information. The model proposed in this paper also utilizes the features of BiLSTM to achieve good results in EEG emotion recognition.