LAUSR

OBJECTIVE emotion recognition on the basis of electroencephalography (EEG) signals has received a significant amount of attention in the areas of cognitive science and human-computer interaction (HCI). However, most existing studies either focus on one-dimensional EEG data ignoring the relationship between channels, or only extract time-frequency features while not involving spatial features. APPROACH we develop spatial-temporal features based EEG emotion recognition using graph convolution network (GCN) and long short-term memory (LSTM) named ERGL. Firstly, the one dimensional EEG vector is converted into a two-dimensional mesh matrix, so that the matrix configuration corresponds to the distribution of brain regions at EEG electrode locations, thus to represent the spatial correlation between multiple adjacent channels in a better way. Secondly, the GCN and LSTM are employed together to extract spatial-temporal features, and GCN is used to extract spatial features, while LSTM units are applied to extract temporal features. Finally, the softmax layer is applied to emotion classification. MAIN RESULTS extensive experiments are conducted on the DEAP and SEED datasets. The classification results of accuracy, precision and F-score for valence and arousal dimensions on DEAP achieved 90.67% and 90.33%, 92.38% and 91.72% and 91.34% and 90.86%, respectively. The accuracy, precision and F-score of positive, neutral and negative classification reached 94.92%, 95.34% and 94.17%, respectively on SEED dataset. SIGNIFICANCE the above results demonstrate the ERGL method is encouraging in comparison to the state-of-the-art recognition researches.