手康复训练混合脑机接口的脑肌电分析方法

IF 0.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Computing and Informatics Pub Date : 2023-01-01 DOI:10.31577/cai_2023_3_741
Lubo Fu, Hao Li, Hongfei Ji, Jie Li
{"title":"手康复训练混合脑机接口的脑肌电分析方法","authors":"Lubo Fu, Hao Li, Hongfei Ji, Jie Li","doi":"10.31577/cai_2023_3_741","DOIUrl":null,"url":null,"abstract":". Brain-computer interfaces (BCIs) have demonstrated immense potential in aiding stroke patients during their physical rehabilitation journey. By reshaping the neural circuits connecting the patient’s brain and limbs, these interfaces contribute to the restoration of motor functions, ultimately leading to a significant improvement in the patient’s overall quality of life. However, the current BCI primarily relies on Electroencephalogram (EEG) motor imagery (MI), which has relatively coarse recognition granularity and struggles to accurately recognize specific hand movements. To address this limitation, this paper proposes a hybrid BCI framework based on Electroencephalogram and Electromyography (EEG-∗ Corresponding author","PeriodicalId":55215,"journal":{"name":"Computing and Informatics","volume":"42 1","pages":"741-761"},"PeriodicalIF":0.7000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"EEG-EMG Analysis Method in Hybrid Brain Computer Interface for Hand Rehabilitation Training\",\"authors\":\"Lubo Fu, Hao Li, Hongfei Ji, Jie Li\",\"doi\":\"10.31577/cai_2023_3_741\",\"DOIUrl\":null,\"url\":null,\"abstract\":\". Brain-computer interfaces (BCIs) have demonstrated immense potential in aiding stroke patients during their physical rehabilitation journey. By reshaping the neural circuits connecting the patient’s brain and limbs, these interfaces contribute to the restoration of motor functions, ultimately leading to a significant improvement in the patient’s overall quality of life. However, the current BCI primarily relies on Electroencephalogram (EEG) motor imagery (MI), which has relatively coarse recognition granularity and struggles to accurately recognize specific hand movements. To address this limitation, this paper proposes a hybrid BCI framework based on Electroencephalogram and Electromyography (EEG-∗ Corresponding author\",\"PeriodicalId\":55215,\"journal\":{\"name\":\"Computing and Informatics\",\"volume\":\"42 1\",\"pages\":\"741-761\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computing and Informatics\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.31577/cai_2023_3_741\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computing and Informatics","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.31577/cai_2023_3_741","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

. 脑机接口(bci)在帮助中风患者进行身体康复过程中显示出巨大的潜力。通过重塑连接患者大脑和四肢的神经回路,这些接口有助于运动功能的恢复,最终显著改善患者的整体生活质量。然而,目前的脑机接口主要依赖于脑电图(EEG)运动图像(MI),其识别粒度相对粗糙,难以准确识别特定的手部动作。为了解决这一限制,本文提出了一个基于脑电图和肌电图(EEG)的混合脑机接口框架
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
EEG-EMG Analysis Method in Hybrid Brain Computer Interface for Hand Rehabilitation Training
. Brain-computer interfaces (BCIs) have demonstrated immense potential in aiding stroke patients during their physical rehabilitation journey. By reshaping the neural circuits connecting the patient’s brain and limbs, these interfaces contribute to the restoration of motor functions, ultimately leading to a significant improvement in the patient’s overall quality of life. However, the current BCI primarily relies on Electroencephalogram (EEG) motor imagery (MI), which has relatively coarse recognition granularity and struggles to accurately recognize specific hand movements. To address this limitation, this paper proposes a hybrid BCI framework based on Electroencephalogram and Electromyography (EEG-∗ Corresponding author
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
Computing and Informatics
Computing and Informatics 工程技术-计算机:人工智能
CiteScore
1.60
自引率
14.30%
发文量
19
审稿时长
9 months
期刊介绍: Main Journal Topics: COMPUTER ARCHITECTURES AND NETWORKING PARALLEL AND DISTRIBUTED COMPUTING THEORETICAL FOUNDATIONS SOFTWARE ENGINEERING KNOWLEDGE AND INFORMATION ENGINEERING Apart from the main topics given above, the Editorial Board welcomes papers from other areas of computing and informatics.
期刊最新文献
Attribute-Based Access Control Policy Generation Approach from Access Logs Based on the CatBoost Classification of Sentiment Using Optimized Hybrid Deep Learning Model BERTDom: Protein Domain Boundary Prediction Using BERT Adaptive Evolutionary Multitasking to Solve Inter-Domain Path Computation Under Node-Defined Domain Uniqueness Constraint: New Solution Encoding Scheme mTreeIllustrator: A Mixed-Initiative Framework for Visual Exploratory Analysis of Multidimensional Hierarchical Data
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1