To address the time-consuming feature extraction and model training in the process of EEG emotion recognition, this paper proposes a method to rapidly train deep learning models for EEG emotion recognition with high accuracy and excellent performance. The DEAP EEG data set is used to quickly train and fit the deep learning model, so as to establish a new pre-trained model for EEG emotion recognition. In addition, it was found that the best training effect was achieved using a sample with a ratio of 25%, and the other test data could quickly fine-tune the original model. The experimental results proved the effectiveness of the method, and the accuracy of the pre-trained model could reach the highest 93.72% in the Valence emotion dimension.
