软件导刊2024，Vol.23Issue(6)：38-43,6.DOI:10.11907/rjdk.231510
基于3D特征融合与轻量化CNN的情绪EEG识别
EEG Emotion Recognition Based on 3D Feature Fusion and Lightweight CNN
摘要
Abstract
Emotional changes can cause changes in scalp EEG signals,and emotion recognition based on EEG signals has become an impor-tant direction in emotional research in recent years.To this end,a sentiment EEG recognition method based on 3D feature fusion and light-weight convolutional neural network is proposed,using a 2D window 3D feature map as input and providing emotional states as output based on valence and arousal.A subject dependent experiment was conducted on the DEAP public dataset,and the results showed that the evalua-tion validity of emotion recognition performance and the accuracy of wake-up recognition were(97.08±0.32)%and(96.78±0.34)%,respec-tively.The proposed method has high accuracy in emotion recognition and low computational complexity,making it suitable for emotion recog-nition in practical scenarios.关键词
情绪识别/卷积神经网络/脑电信号/特征融合/轻量化模型Key words
emotion recognition/convolutional neural networks/EEG signals/feature fusion/lightweight model分类
信息技术与安全科学引用本文复制引用
陈紫扬,随力,胡磊..基于3D特征融合与轻量化CNN的情绪EEG识别[J].软件导刊,2024,23(6):38-43,6.基金项目
上海理工大学科技发展项目(2019KJFZ239,2020KJFZ232) （2019KJFZ239,2020KJFZ232）
