主体泛型模型中判别dof分布的最优肌电潜子空间搜索。

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Jiahao Fan;Yangyang Yuan;Tanying Su;Jionghui Liu;Chih-Hong Chou;Xinyu Jiang;Fumin Jia;Chenyun Dai
{"title":"主体泛型模型中判别dof分布的最优肌电潜子空间搜索。","authors":"Jiahao Fan;Yangyang Yuan;Tanying Su;Jionghui Liu;Chih-Hong Chou;Xinyu Jiang;Fumin Jia;Chenyun Dai","doi":"10.1109/TNSRE.2025.3608128","DOIUrl":null,"url":null,"abstract":"Recognizing hand gestures from surface electromyography (sEMG) signals is crucial for neural interfaces and human–machine interaction. However, developing subject-generic models remains challenging due to substantial inter-subject variability. Complicating matters further, the muscle groups driving gestures with varying degrees of freedom (DoFs) often overlap, producing highly convoluted feature distributions across subjects and DoFs. To address these challenges, we introduce a multi-branch autoencoder (AE) architecture that disentangles sEMG features into two latent subspaces: a DoF-specific (subject-invariant) space and a subject-specific (DoF-invariant) space. We systematically compare our approach against well-established feature projection methods: principal component analysis (PCA), kernel PCA (KPCA), linear discriminant analysis (LDA), kernel discriminant analysis (KDA), and a conventional AE, as well as two style-independent feature transformation methods: canonical correlation analysis (CCA) and spectral regression discriminant analysis (SRDA). Experimental results on 20 subjects across multiple days demonstrate that our multi-branch AE markedly improves DoF discrimination while maintaining subject invariance, leading to consistently higher inter-subject classification accuracy for all common classifiers. These findings underscore the potential of our approach for robust, user-independent sEMG-based gesture recognition.","PeriodicalId":13419,"journal":{"name":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","volume":"33 ","pages":"3723-3733"},"PeriodicalIF":5.2000,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11156143","citationCount":"0","resultStr":"{\"title\":\"Searching for Optimal EMG Latent Subspace With Discriminant DoF-Wise Distributions for Subject-Generic Model\",\"authors\":\"Jiahao Fan;Yangyang Yuan;Tanying Su;Jionghui Liu;Chih-Hong Chou;Xinyu Jiang;Fumin Jia;Chenyun Dai\",\"doi\":\"10.1109/TNSRE.2025.3608128\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recognizing hand gestures from surface electromyography (sEMG) signals is crucial for neural interfaces and human–machine interaction. However, developing subject-generic models remains challenging due to substantial inter-subject variability. Complicating matters further, the muscle groups driving gestures with varying degrees of freedom (DoFs) often overlap, producing highly convoluted feature distributions across subjects and DoFs. To address these challenges, we introduce a multi-branch autoencoder (AE) architecture that disentangles sEMG features into two latent subspaces: a DoF-specific (subject-invariant) space and a subject-specific (DoF-invariant) space. We systematically compare our approach against well-established feature projection methods: principal component analysis (PCA), kernel PCA (KPCA), linear discriminant analysis (LDA), kernel discriminant analysis (KDA), and a conventional AE, as well as two style-independent feature transformation methods: canonical correlation analysis (CCA) and spectral regression discriminant analysis (SRDA). Experimental results on 20 subjects across multiple days demonstrate that our multi-branch AE markedly improves DoF discrimination while maintaining subject invariance, leading to consistently higher inter-subject classification accuracy for all common classifiers. These findings underscore the potential of our approach for robust, user-independent sEMG-based gesture recognition.\",\"PeriodicalId\":13419,\"journal\":{\"name\":\"IEEE Transactions on Neural Systems and Rehabilitation Engineering\",\"volume\":\"33 \",\"pages\":\"3723-3733\"},\"PeriodicalIF\":5.2000,\"publicationDate\":\"2025-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11156143\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Neural Systems and Rehabilitation Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11156143/\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Neural Systems and Rehabilitation Engineering","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11156143/","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

摘要

从表面肌电信号中识别手势对于神经接口和人机交互至关重要。然而,由于学科间的差异,开发学科通用模型仍然具有挑战性。更复杂的是,控制不同自由度手势的肌肉群经常重叠,在受试者和自由度之间产生高度复杂的特征分布。为了解决这些挑战,我们引入了一个多分支自动编码器(AE)架构,该架构将表面肌电信号特征分解为两个潜在的子空间:dof特定(主体不变)空间和主体特定(dof不变)空间。我们系统地比较了我们的方法与成熟的特征投影方法:主成分分析(PCA)、核主成分分析(KPCA)、线性判别分析(LDA)、核判别分析(KDA)和传统AE,以及两种风格无关的特征转换方法:典型相关分析(CCA)和光谱回归判别分析(SRDA)。在20个受试者的多天实验结果表明,我们的多分支声发射在保持受试者不变性的同时显著提高了DoF识别率,使得所有常用分类器的主题间分类准确率始终较高。这些发现强调了我们稳健的、独立于用户的基于表面肌电信号的手势识别方法的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Searching for Optimal EMG Latent Subspace With Discriminant DoF-Wise Distributions for Subject-Generic Model
Recognizing hand gestures from surface electromyography (sEMG) signals is crucial for neural interfaces and human–machine interaction. However, developing subject-generic models remains challenging due to substantial inter-subject variability. Complicating matters further, the muscle groups driving gestures with varying degrees of freedom (DoFs) often overlap, producing highly convoluted feature distributions across subjects and DoFs. To address these challenges, we introduce a multi-branch autoencoder (AE) architecture that disentangles sEMG features into two latent subspaces: a DoF-specific (subject-invariant) space and a subject-specific (DoF-invariant) space. We systematically compare our approach against well-established feature projection methods: principal component analysis (PCA), kernel PCA (KPCA), linear discriminant analysis (LDA), kernel discriminant analysis (KDA), and a conventional AE, as well as two style-independent feature transformation methods: canonical correlation analysis (CCA) and spectral regression discriminant analysis (SRDA). Experimental results on 20 subjects across multiple days demonstrate that our multi-branch AE markedly improves DoF discrimination while maintaining subject invariance, leading to consistently higher inter-subject classification accuracy for all common classifiers. These findings underscore the potential of our approach for robust, user-independent sEMG-based gesture recognition.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信