现代电子技术2025，Vol.48Issue(7)：57-64,8.DOI:10.16652/j.issn.1004-373x.2025.07.009
基于CWGAN-ABiLSTM-FCN的运动想象脑电信号分类
Motion imaging EEG classification based on CWGAN-ABiLSTM-FCN
摘要
Abstract
In view of the poor real-time performance and low accuracy of motion intention recognition based on MI-EEG(motor imagery EEG),such as unbalanced distribution of sample data of MI-EEG,imbalance dependence and attention on long distance in the extraction of time-series feature,and the difficulties in extracting the local feature,an MI-EEG signal classification method combining improved bidirectional long short-term memory(BiLSTM)neural network and full convolutional network(FCN)is proposed.In the proposed method,the conditional generative adversarial network(CGAN)is used to generate false MI-EEG signal samples,so as to realize effective expansion of the training sample set,which avoids the fact that the data set is excessively small and the number of its categories is unbalanced.By the respective advantages of bidirectional self-attention long short-term memory(LSTM)neural network and FCN,the facts of long-distance dependence and unbalanced attention in time-series feature extraction,difficulties in local feature extraction and inability to take into account the time-space domain features of MI-EEG signals are avoided.On this basis,the nonlinear mapping relationship between fusion features and action classification labels is constructed,so as to improve the recognition accuracy of the model.Finally,this classification model is compared with the other MI-EEG classification models in the test data set.The experimental results show that the accuracy of the proposed MI-EEG classification model reaches 97%,so it has good generalization performance.关键词
运动想象/脑电信号分类/生成对抗网络/长短时记忆网络/全卷积神经网络/注意力机制Key words
MI/EEG classification/GAN/LSTM neural network/FCN/attention mechanism分类
电子信息工程引用本文复制引用
吴生彪,程显朋,李花宁..基于CWGAN-ABiLSTM-FCN的运动想象脑电信号分类[J].现代电子技术,2025,48(7):57-64,8.基金项目
国家自然科学基金项目(62141102) （62141102）
江西省教育厅科学技术研究项目(GJJ2200726) （GJJ2200726）
东华理工大学博士科学研究基金项目(DH-BK2019178) （DH-BK2019178）
