Multimodal Fused Emotion Recognition About Expression-EEG Interaction and Collaboration Using Deep Learning

The proposed emotion recognition model is based on the hierarchical long-short term memory neural network (LSTM) for video-electroencephalogram (Video-EEG) signal interaction. The inputs are facial-video and EEG signals from the subjects when they are watching the emotion-stimulated video. The outputs are the corresponding emotion recognition results. Facial-video features and corresponding EEG features are extracted based on a fully connected neural network (FC) at each time point. These features are fused through hierarchical LSTM to predict the key emotional signal frames at the next time point until the emotion recognition result is calculated at the last time point. Specially, a self-attention mechanism is applied to show the correlation of the stacked LSTM at different hierarchies. In this process, the “selective focus” is used to analyze the human-emotional temporal sequences in each model, which improves the utilization of the key spatial EEG signals. Moreover, the process includes the temporal attention mechanism to predict the key signal frame at next time point, which utilizes the key emotion data in temporal domain. The experimental results prove that the classification rate (CR) and F1-score of the proposed emotion recognition model are significantly increased by at least 2% and 0.015, respectively, compared to other methods.