In response to the limited variety and low accuracy of existing deep learning models for emotion recognition,a dataset of electroencephalogram(EEG)signals was collected and established,and an intelligent multi-emotion recognition model based on Convolutional Neural Networks(CNNs)was developed.The model utilizes multiple layers of convolutional neural networks to extract emotional features from EEG signals.Non-linear characteristics are introduced through batch normalization layers and activation functions.Additionally,a two-layer fully connected neural network is designed to classify emotional features into positive,neutral,and sad categories.The experimental results demonstrate that the proposed model exhibits low complexity and achieves a classification accuracy of 81.43%,surpassing SVM,LSTM,and VGGNet models.This confirms the efficiency and simplicity of the proposed model.
维普
万方数据