[1]戴亮宙,王娆芬,王海玲.基于特征融合AEBGNet的运动想象脑电分类算法[J].中国医学物理学杂志,2024,41(8):1021-1030.[doi:DOI:10.3969/j.issn.1005-202X.2024.08.016]
　DAI Liangzhou,WANG Raofen,WANG Hailing.Motor imagery EEG classification algorithm using feature fusion based AEBGNet[J].Chinese Journal of Medical Physics,2024,41(8):1021-1030.[doi:DOI:10.3969/j.issn.1005-202X.2024.08.016]
点击复制

基于特征融合AEBGNet的运动想象脑电分类算法()
分享到：

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第8期
页码:
1021-1030
栏目:
医学信号处理与医学仪器
出版日期:
2024-08-31

文章信息/Info

Title:
Motor imagery EEG classification algorithm using feature fusion based AEBGNet
文章编号:
1005-202X（2024）08-1021-10
作者:
戴亮宙; 王娆芬; 王海玲
上海工程技术大学电子电气工程学院， 上海 201620
Author(s):
DAI Liangzhou;  WANG Raofen;  WANG Hailing
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
关键词:
脑机接口; 运动想象; 卷积神经网络; 双向门控循环单元; 注意力机制
Keywords:
Keywords: brain-computer interface motor imagery convolutional neural network bidirectional gated recurrent unit attention mechanism
分类号:
R318；TP391
DOI:
DOI:10.3969/j.issn.1005-202X.2024.08.016
文献标志码:
A
摘要:
针对机器学习方法在对脑电特征进行分类时无法同时兼顾脑电信号的时-空域特征的问题，利用添加注意力机制的卷积神经网络提取空间特征和双向门控循环单元提取时间特征，提出一种基于特征融合的运动想象（Motor Imagery, MI）脑电分类算法（Attention-EEGNet-BiGRU, AEBGNet），AEBGNet可将时、空域两类特征相融合，得到更具表征性的时-空域特征，最终构建的AEBGNet分类模型在BCI competition IV 2b数据集上取得80.37%的平均正确率，比标准的EEGNet方法提高6.09%。结果表明，本文方法可以有效提高MI脑电信号的分类正确率，为MI脑电信号的分类提供新的思路。
Abstract:
Abstract: To address the inability of the existing machine learning methods to simultaneously consider both the temporal and spatial domain features of electroencephalogram (EEG) signals in classifying EEG features, a feature fusion based Attention-EEGNet-BiGRU (AEBGNet) is presented for classifying motor imagery (MI) EEG signals. AEBGNet is capable of fusing the temporal domain features extracted by convolutional neural network with attention mechanism and spatial domain features extracted by a bidirectional gated recurrent unit to obtain more distinctive spatiotemporal features. The constructed AEBGNet classification model achieves an average accuracy of 80.37% on the BCI competition IV 2b dataset, and there is an improvement of 6.09% over the standard EEGNet method. The results demonstrate the effectiveness of the proposed method in enhancing the classification accuracy of MI EEG signals, providing a new idea for MI EEG signal classification.

相似文献/References:

[1]刘畅,覃玉荣,时文健.视听觉刺激下大脑头皮电位空间变化特性[J].中国医学物理学杂志,2018,35(10):1225.[doi:DOI:10.3969/j.issn.1005-202X.2018.010.023]
　LIU Chang,QIN Yurong,SHI Wenjian. Spatial variation characteristics of scalp potentials under audiovisual stimuli[J].Chinese Journal of Medical Physics,2018,35(8):1225.[doi:DOI:10.3969/j.issn.1005-202X.2018.010.023]
[2]苌文清,孙曜. 基于多变量格兰杰因果关系的运动想象因效网络构建[J].中国医学物理学杂志,2018,35(12):1457.[doi:DOI:10.3969/j.issn.1005-202X.2018.12.017]
　CHANG Wenqing,SUN Yao. Construction of motion imagination causal network based on multivariable Granger causality[J].Chinese Journal of Medical Physics,2018,35(8):1457.[doi:DOI:10.3969/j.issn.1005-202X.2018.12.017]
[3]周杰,杨国雨,徐涛. 基于空间频率与时间序列信息的多类运动想象脑电分类[J].中国医学物理学杂志,2019,36(1):81.[doi:DOI:10.3969/j.issn.1005-202X.2019.01.016]
　ZHOU Jie,YANG Guoyu,XU Tao. Classification of multi-class motor imagery EEG data based on spatial frequency and time-series information[J].Chinese Journal of Medical Physics,2019,36(8):81.[doi:DOI:10.3969/j.issn.1005-202X.2019.01.016]
[4]姜月,邹任玲. 基于多特征融合的运动想象脑电信号识别研究[J].中国医学物理学杂志,2019,36(5):590.[doi:DOI:10.3969/j.issn.1005-202X.2019.05.019]
　JIANG Yue,ZOU Renling. Recognition of motor imagery EEG signals based on multi-feature fusion[J].Chinese Journal of Medical Physics,2019,36(8):590.[doi:DOI:10.3969/j.issn.1005-202X.2019.05.019]
[5]冯琴昌.基于OpenBCI与OpenViBE的脑机接口设计[J].中国医学物理学杂志,2020,37(2):210.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.014]
　FENG Qinchang.Design of brain-computer interface based on OpenBCI and OpenViBE[J].Chinese Journal of Medical Physics,2020,37(8):210.[doi:DOI:10.3969/j.issn.1005-202X.2020.02.014]
[6]李红利,丁满,张荣华,等.基于特征融合神经网络的运动想象脑电分类算法[J].中国医学物理学杂志,2022,39(1):69.[doi:DOI:10.3969/j.issn.1005-202X.2022.01.012]
　LI?ongli,ING Man,HANG?onghua,et al.Motor imagery EEG classification algorithm based on feature fusion neural network[J].Chinese Journal of Medical Physics,2022,39(8):69.[doi:DOI:10.3969/j.issn.1005-202X.2022.01.012]
[7]吴拾瑶,随力,杨兰,等.运动想象重塑脑功能的研究进展[J].中国医学物理学杂志,2021,38(11):1449.[doi:DOI:10.3969/j.issn.1005-202X.2021.11.023]
　WU Shiyao,SUI Li,YANG Lan,et al.Research advances in motor imagery for remodeling brain functions[J].Chinese Journal of Medical Physics,2021,38(8):1449.[doi:DOI:10.3969/j.issn.1005-202X.2021.11.023]
[8]贾婷婷,董朝轶,马爽,等.基于互信息特征提取的运动想象脑机接口[J].中国医学物理学杂志,2022,39(1):63.[doi:DOI:10.3969/j.issn.1005-202X.2022.01.011]
　JIA Tingting,DONG Chaoyi,et al.Brain-computer interface of motion imagery based on mutual information-based feature extraction[J].Chinese Journal of Medical Physics,2022,39(8):63.[doi:DOI:10.3969/j.issn.1005-202X.2022.01.011]

备注/Memo

备注/Memo:
【收稿日期】2024-03-02 【基金项目】国家自然科学基金（62173222）；国家自然科学基金青年基金（62001284） 【作者简介】戴亮宙，硕士研究生，研究方向：运动想象脑电信号，E-mail: 476433389@qq.com 【通信作者】王娆芬，博士，副教授，研究方向：脑机接口、医学图像处理，E-mail: rfwangsues@163.com
更新日期/Last Update: 2024-08-31