广西科学院学报2025，Vol.41Issue(1)：24-32,9.DOI:10.13657/j.cnki.gxkxyxb.20250429.003
CNN-RxLSTM:基于混合时空卷积和残差xLSTM的EEG情绪识别网络
CNN-RxLSTM:EEG Emotion Recognition Network Based on Hybrid Convolution and Residual xLSTM
摘要
Abstract
Aiming at the limitations of Electroence-phalogram(EEG)-based emotion recognition methods in insufficient feature extraction and spatio-temporal dependence modeling,this study proposes an EEG emotion recognition network CNN-RxLSTM based on hybrid spatio-temporal Convolutional Neural Network(CNN)and Residual Extended Long Short-Term Memory(RxLSTM).The model first uses CNN to extract the local spatio-temporal features of EEG signals,then introduces the xLSTM module to model the global spatio-tem-poral dependence of signals through bidirectional information flow processing and residual connection mecha-nism.Finally,the classification is completed by the classifier module.In order to verify the validity of the model,experiments are carried out on SEED dataset and DEAP dataset respectively.The results show that the classification accuracy of the CNN-RxLSTM model is 98.15%on the SEED dataset.On the DEAP data-set,the accuracy of valence classification and arousal classification is 94.60%and 95.89%,respectively.The research results verify the excellent performance of the model in EEG emotion recognition,which can provide new solutions for emotion decoding and other EEG related research.关键词
情绪识别/脑电图/卷积神经网络/扩展型长短时记忆网络/残差机制Key words
emotion recognition/Electroencephalogram(EEG)/Convolutional Neural Network(CNN)/Ex-tended Long Short-Term Memory Network(xLSTM)/residual mechanism分类
计算机与自动化引用本文复制引用
甘秋静,闭应洲,霍雷刚,刘善锐,熊凯睿..CNN-RxLSTM:基于混合时空卷积和残差xLSTM的EEG情绪识别网络[J].广西科学院学报,2025,41(1):24-32,9.基金项目
国家自然科学基金项目(62067007)和广西学位与研究生教改课题(JGY2023236)资助. （62067007）
