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This article discusses the use of Electroencephalography (EEG) signals and hybrid deep learning models for accurate emotion recognition. The proposed Convolutional Fuzzy Neural Network (CFNN) achieved a high average accuracy of 98.21% for valence and 98.08% for arousal, outperforming other methods.