计算机工程与应用2019，Vol.55Issue(22)：99-105,7.DOI:10.3778/j.issn.1002-8331.1807-0219
基于改进的卷积神经网络脑电信号情感识别
Emotion Recognition of EEG Signal Based on Improved Convolutional Neural Network
摘要
Abstract
Considering that traditional machine learning requires artificial construction features and low feature quality, this paper proposes a novel automatic feature extraction approach in Electroencephalograph(EEG)signals based on 1-D Convolutional Neural Network(CNN). This approach uses the idea of compilation, at the same time the convolutional layer and the downsampling layer form the encoder network to extract the emotional characteristics of the EEG signal, then the Leaky ReLU activation function is applied to the feature map. For the convolution pre-training process, the cross-entropy and regularization terms are used to optimize the loss function, then the random forest classifier is used to obtain the emo-tion classification label. Finally, the experiment is carried out on the international public data set SEED, which achieves 94.7% sentiment classification accuracy, and the experimental results show the effectiveness and robustness of the pro-posed method.关键词
脑电信号(EEG)/特征提取/卷积神经网络(CNN)/随机森林/损失函数Key words
Electroencephalograph(EEG)/feature extracting/Convolutional Neural Network(CNN)/fandom forest/loss function分类
信息技术与安全科学引用本文复制引用
田莉莉,邹俊忠,张见,卫作臣,汪春梅..基于改进的卷积神经网络脑电信号情感识别[J].计算机工程与应用,2019,55(22):99-105,7.基金项目
国家自然科学基金(No.61071085). （No.61071085）
