基于卷积神经网络的脑电信号情绪识别方法研究

Emotion is an indicator of evaluating a person\'s psychological state, and emotion recognition has significant meanings for people\'s researches and life. Electroencephalogram (EEG) is often used in the field of emotion recognition.Frequency and spatial features of EEG signals are often used for emotion recognition. Considering the strong complementarity between different kinds of features, this paper proposes a method using combination of frequency features and spatial features. At the same time, the computational method of pseudo 3D convolution proposed in this paper reduces the computational cost and memory requirements of model. The residual structure introduced by the model solves the problem of gradient vanishing and expolding caused by network deepening. Subsequently, the emotion recognition experiment of EEG signals is carried out on the SEED dataset in this paper. The experimental results show that the proposed method achieves excellent performance.