Research on Emotion Recognition of EEG Signal Based on Convolutional Neural Networks and High-Order Cross-Analysis

Emotion recognition means the automatic identification of a human's emotional state by obtaining his/her physiological or nonphysiological signals. The EEG-based method is an effective mechanism, which is commonly used for the recognition of emotions in real environments. In this paper, the convolutional neural network is used to classify the EEG signal into three and four emotional states under the DEAP dataset, which is defined as a Database for Emotion Analysis using physiological signals. For this purpose, a high-order cross-feature sample is extracted to recognize the emotional state with a single channel. A seven-layer convolutional neural network is used to classify the 32-channel EEG signal, and the average accuracy of four and three emotional states is 65% and 58.62%. The single-channel high-order cross-sample is classified with convolutional neural networks, and the average accuracy of four emotional states is 43.5%. Among all the channels related to emotion recognition, the F4 channel gets the best classification accuracy of 44.25%, and the average accuracy of the even number channel is higher than the odd number channel. The proposed method provides a basis for the real-time application of EEG-based emotion recognition.