Players-based emotion recognition can help the understanding game players’ emotional states, contributing to the improvement of the game's quality and value. This article develops a hybrid neural network learning framework called convolutional smooth feedback fuzzy network (CSFFN) to detect a player's emotional states in real-time during a gaming process based on electroencephalogram (EEG) signals. Specifically, CSFFN rationally combines a convolutional neural network (CNN), a fuzzy neural network (FNN), and a recurrent neural network (RNN). CNN not only captures spatial characteristics between EEG signals from different channels but also eliminates noise from EEG signals, improving the accuracy and anti-noise performance in game emotion recognition. FNN extracts the membership degree of a player's different emotional states, further improving the emotion recognition accuracy. Since a player's current emotional state is influenced by the previous emotional states during the game process, RNN is employed to capture the temporal characteristics of EEG signals, better improving the emotion recognition accuracy. Experimental results show that CSFFN has higher recognition accuracy and noise resistance in identifying four emotional states (happiness, sadness, superiority, and anger) compared to support vector machine (SVM) with different kernels, linear discrimination analysis (LDA), AlexNet, and VGG16 methods.
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