Fusion of Facial Expressions and EEG for Multimodal Emotion Recognition

Figure 2

The architecture of the proposed system for face expression classification: the network has one hidden layer with 200 neurons. The input of this network is 169 image features we get from dimensionality reduction, while the output is the scores of four emotion states (happiness, neutral, sadness, and fear). The learning rate of this network is 0.1. We use sigmoid function as the activation function of this network.