广西科学院学报2025，Vol.41Issue(1)：12-23,12.DOI:10.13657/j.cnki.gxkxyxb.20250429.002
CRRT:基于改进CNN和ResRNN-Transformer的EEG数据分类网络
CRRT:EEG Data Classification Network Based on Improved CNN and ResRNN-Transformer
摘要
Abstract
Electroencephalogram(EEG)data is complex and susceptible to noise interference,and traditional analysis methods are difficult to process efficiently.In order to solve this problem,this study proposes a CRRT model based on dynamic position encoding.The model captures the temporal dependence of EEG data through Residual Recurrent Neural Network(ResRNN).The output of ResRNN is input into Transformer as dynamic position encoding to improve its ability to model the global information of EEG data.At the same time,the number of model parameters is reduced by sharing parameters to improve the accuracy of EEG data classification.In order to verify the effectiveness of the proposed model,experiments were conducted on four public EEG datasets,and the CRRT model was compared with other advanced methods to evaluate its per-formance on different datasets.The experimental results show that the average accuracy rates on BCI Compe-tition Ⅳ Dataset 2a and BCI Competition Ⅳ Dataset 2b are 81.09%(Kappa value is 0.745 3)and 87.66%(Kappa value is 0.725 3),respectively.The classification accuracy is 97.31%(Kappa value is 0.957 4)on the SJTU emotional EEG dataset(SEED dataset).On the DEAP dataset,the classification accuracy of valence and arousal are 99.37%and 99.39%,respectively.The above results show that the accuracy and efficiency of CRRT classification model in EEG data classification tasks have been significantly improved,which provides a strong support for brain science research.关键词
脑电图/参数共享/全局注意力机制/动态位置编码Key words
electroencephalogram/parameter sharing/global attention mechanism/dynamic positional enco-ding分类
计算机与自动化引用本文复制引用
刘善锐,闭应洲,霍雷刚,甘秋静,李永玉..CRRT:基于改进CNN和ResRNN-Transformer的EEG数据分类网络[J].广西科学院学报,2025,41(1):12-23,12.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）
