基于CNN的脑电信号情绪识别模型研究

EMOTION RECOGNITION MODEL OF EEG SIGNALS BASED ON CNN

针对现有深度学习模型在情绪识别方面种类少且准确率低的问题,采集并建立了脑电波信号数据集,提出了一种基于CNN的脑电波的智能多情绪识别模型,利用多层卷积神经网络提取脑电信号情感特征,在批归一化层和激活函数中引入非线性特性,构建了两层全连接神经网络,实现了情绪特征中积极、中性和悲伤的分类。实验结果表明,提出的模型复杂度低且分类准确率达到了81.43%,明显高于SVM、LSTM、VGGNet模型,证明了该模型的简洁性和高效性。

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.Nonlinear 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.