BiCCT: A Compact Convolutional Transformer for EEG Emotion Recognition.

Emotion is a manifestation of human’s internal psychological and physiological reactions. Understanding and recognizing emotions is one of the important ways to understand human behavior and human-computer interaction. However, with the widespread application of deep learning in the field of EEG emotion recognition, the number of parameters and model size have increased accordingly. In this paper, we combined the Bi-hemisphere asymmetry theory and Compact Convolutional Transformer to propose a model named BiCCT to recognize emotions, which has fewer training parameters and can achieve higher recognition performance. We first constructed three different matrices of the recorded EEG information according to the international 10-20 system to preserve the temporal information and spatial information of the EEG signals. Next, we applied an improved Transformer architecture, which achieves fewer model parameters and a lightweight structure through the token pooling module and the Convolutional Tokenizer module. We conducted a set of subject-dependent and a set of subject-dependent shuffle experiments on the DEAP dataset. The first set of experiments used a subject-known movie to predict a completely unknown movie. In the second set of experiments, all the data of the subject were randomly divided into training set and test set. We obtained 67.42% for valence and 67.81% for arousal in the first set of experiments. We achieved 94.41% accuracy in the valence dimension and 95.15% accuracy in the arousal dimension in the second set of experiments. At the same time, our model parameters are only 0.17M, which is far lower than other models. It means that our model is lighter and faster in training speed, and has the ability to be deployed in some scenarios with limited computing resources potential.