基于CNN的脑电信号情绪识别模型研究

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。

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.