## 1 Introduction  
Emotion plays an important role in daily human life and influences all aspects. Emotion is an indispensable and important role for humans that affects people all the time, including human decision-making, speech, sleep, health, communication, and various other characteristics. Emotion recognition is often implemented based on facial expressions, speech, and physiological signals, while physiological signals more accurately reflect fluctuations in human emotional states [1] and are often used in the field of human-computer interaction. In recent years, emotion recognition based on electroencephalography (EEG) [2] in physiological signals has had widespread applications because of its non-invasive, easy-to-use, and inexpensive characteristics. EEG is an electrical signal of the human brain epidermis, which has nonlinear and non-smooth characteristics, while feature extraction and classification of such signals have been a challenge for researchers. Many researchers have proposed their feature extraction algorithms based on traditional methods [3; 4; 5; 6] emotion recognition methods, and deep learning emotion recognition methods based on convolutional neural networks [7], deep learning networks [6; 8], long and short-term memory networks [9; 10; 11; 12], graph convolutional networks [9; 12; 13], attention mechanisms [14], etc. In recent years, graph convolutional neural networks (GCNN), long short-term memory neural networks (LSTM), and attention mechanisms have begun to be applied increasingly in the field. However, we are facing the problem of how to improve the integration of these different functional networks and apply them to EEG-based emotion recognition. To address the above problems, we propose a deep learning model-based emotion recognition method. The method first uses common spatial pattern to process the EEG signal to get better spatial information, and then continuous wavelet transform transforms the filtered signal into a time-frequency map as the input of the deep model, which incorporates convolutional aspects. The model integrates convolutional neural networks and bidirectional long- and short-term memory networks and adds a multi-headed self-attentive mechanism on top of that. In the hybrid model, feature information is extracted using multiple CNNs, future, and past time series information is learned, and temporal features are extracted using BiLSTM, while better feature information is given by assigning weights to sentiment feature information using a multi-headed self-attentive mechanism. Finally, we conducted extensive experiments on DEAP dataset and the experimental results showed that the method has better classification performance than the existing classification methods. The main contributions of this paper are as follows:  
We propose a deep learning emotion recognition method for EEG signals, which is a deep learning framework consisting of a hybrid model of CNN, BiLSTM, and MHSA. The CNN is used to smooth and down-sample the data, then the BiLSTM extracts the future and past emotion features from the data for learning, and finally, the multi-headed attention mechanism is implemented to improve the emotion recognition accuracy by weighing the key information of the EEG emotion features. * We propose a method for converting EEG signals into time-frequency maps as model data input. The method uses CSP filtering to extract the spatial information of EEG signals, and then CWT is used to convert the filtered information into time-frequency maps as model input data, which makes the CNN-BiLSTM-MHSA better for extracting feature information. * We conducted extensive experiments on the DEAP dataset and achieved higher accuracy contrasted to other deep learning models and traditional methods for both binary classification tasks of valence, arousal, dominance, and liking, as well as quadruple classification tasks of valence-arousal states.