Detecting Emotions Through Eeg Signals Based On Modified Convolutional Fuzzy Neural Network

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.