Jingxia Chen

In order to improve the accuracy of emotional recognition by end-to-end automatic learning of emotional features in spatial and temporal dimensions of electroencephalogram (EEG), an EEG emotional feature learning and classification method using deep convolution neural network (CNN) was proposed based on temporal features, frequential features, and their combinations of EEG signals in DEAP dataset. The shallow machine learning models including bagging tree (BT), support vector machine (SVM), linear discriminant analysis (LDA), and Bayesian linear discriminant analysis (BLDA) models and deep CNN models were used to make emotional binary classification experiments on DEAP datasets in valence and arousal dimensions. The experimental results showed that the deep CNN models which require no feature engineering achieved the best recognition performance on temporal and frequency combined features in both valence and arousal dimensions, which is 3.58% higher than the performance of the best traditional BT classifier in valence dimension and 3.29% higher than that of BT classifier in arousal dimension.

In this paper, we propose a hierarchical bidirectional Gated Recurrent Unit (GRU) network with attention for human emotion classification from continues electroencephalogram (EEG) signals. The structure of the model mirrors the hierarchical structure of EEG signals, and the attention mechanism is used at two levels of EEG samples and epochs. By paying different levels of attention to content with different importance, the model can learn more significant feature representation of EEG sequence which highlights the contribution of important samples and epochs to its emotional categories. We conduct the cross-subject emotion classification experiments on DEAP data set to evaluate the model performance. The experimental results show that in valence and arousal dimensions, our model on 1-s segmented EEG sequences outperforms the best deep baseline LSTM model by 4.2% and 4.6%, and outperforms the best shallow baseline model by 11.7% and 12% respectively. Moreover, with increase of the epoch's length of EEG sequences, our model shows more robust classification performance than baseline models, which demonstrates that the proposed model can effectively reduce the impact of long-term non-stationarity of EEG sequences and improve the accuracy and robustness of EEG-based emotion classification.