自动化与信息工程2025，Vol.46Issue(5)：38-46,9.DOI:10.12475/aie.20250505
多域特征融合的脑电情绪识别方法
EEG Emotion Recognition Method for Multi-domain Feature Fusion
摘要
Abstract
To address the issue of limited emotional features and ineffective utilization of inter-electrode channel information in EEG-based emotion recognition,this study proposes an EEG emotion recognition method for multi-domain feature fusion.First,a three-dimensional feature structure is constructed based on EEG characteristics,integrating differential entropy features,wavelet packet energy features,and spatial features between electrode channels to fully preserve effective information in the frequency,time,and spatial domains.Then,for the 3D feature structure,a hybrid EEG emotion recognition model named CLANN is built by combining 3D convolutional neural network(3DCNN),long short-term memory(LSTM)network,and attention mechanism.Experimental results on the DEAP(dataset for emotion analysis using physiological signals)demonstrate that the CLANN model achieves a binary classification accuracy of 94.50%,improving the performance of EEG-based emotion recognition.关键词
脑电情绪识别/多域特征融合/三维卷积神经网络/长短时记忆网络/注意力机制Key words
EEG emotion recognition/multi-domain feature fusion/3D convolutional neural network/long short-term memory network/attention mechanism分类
信息技术与安全科学引用本文复制引用
邵文鑫,杜玉晓..多域特征融合的脑电情绪识别方法[J].自动化与信息工程,2025,46(5):38-46,9.基金项目
国家自然科学基金(61976059、61640213) （61976059、61640213）
