EMOTION RECOGNITION MODEL OF EEG SIGNALS BASED ON CNN
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.
万方数据
维普
