EEG-based emotion recognition with cascaded convolutional recurrent neural networks

In recent years, deep learning has gradually become a prevailing way in EEG-based emotion recognition research because it can extract features and classify emotions automatically. To fully exploit the underlying information in EEG signals, we propose an emotion recognition method based on cascaded convolutional recurrent neural networks. Firstly, the differential entropy features of each channel signal are transformed into four-dimensional structure data, which are able to contain temporal-spatial-frequency information integratively. Then, the cascaded VGG16 and long short-term memory (LSTM) networks are applied to learn the spatiotemporal information of the samples, and the hidden layer of the last node of LSTM is output to a linear transformation classifier to perform classification. On DEAP dataset, the proposed method gives out an average accuracy of 94.43% and 94.85% in arousal-based and valence-based classification, respectively. On SEED dataset, the method achieves average accuracy of 94.16%. Compared with the existing methods, our method demonstrates superior performances in emotion recognition.