井冈山大学学报（自然科学版）2025，Vol.46Issue(4)：81-87,7.DOI:10.3969/j.issn.1674-8085.2025.04.010
基于VMD和融合通道注意力机制DenseNet的脑电情绪识别
EEG EMOTION RECOGNITION BASED ON VMD AND DENSENET WITH FUSION CHANNEL ATTENTION MECHANISM
摘要
Abstract
Given the current difficulties of EEG signals feature extraction and limited accuracy,a new EEG emotion recognition model based on VMD and DenseNet with fusion channel attention mechanism is proposed,mainly including two modules:feature extraction and classification.Firstly,the VMD algorithm was introduced.It could realize the denoising processing of EEG signals,and the differential entropy features were extracted from the effective IMF signals to improve the discriminability of features under different emotional states.Secondly,the multi-scale convolution kernel and channel attention mechanism were integrated into the DenseNet network,which could not only extract the features of different scales,but also given different weights to different EEG channels,so as to further improve the accuracy of emotion recognition.Finally,the effectiveness and robustness of the model were verified on the SEED data set,and the average classification accuracy of 15 subjects could reach 96.86%.The results show that the proposed model can effectively extract EEG features related to emotion and achieve better classification results.关键词
EEG/VMD/特征重用/多尺度卷积核/通道注意力机制Key words
EEG/VMD/feature reuse/multi-scale convolution kernel/channel attention分类
信息技术与安全科学引用本文复制引用
苏靖然,李秋生..基于VMD和融合通道注意力机制DenseNet的脑电情绪识别[J].井冈山大学学报（自然科学版）,2025,46(4):81-87,7.基金项目
国家自然科学基金项目(61561004) （61561004）
江西省自然科学基金项目(20242BAB25052) （20242BAB25052）
