An EMD Based Method for Reduction of Ballistocardiogram Artifact from EEG Studies of Evoked Potentials

Ehtasham Javed, I. Faye, A. Malik, J. Abdullah
{"title":"An EMD Based Method for Reduction of Ballistocardiogram Artifact from EEG Studies of Evoked Potentials","authors":"Ehtasham Javed, I. Faye, A. Malik, J. Abdullah","doi":"10.1109/ICMLA.2015.81","DOIUrl":null,"url":null,"abstract":"Multi-modality data acquisition is a topic of research that gained interest in the recent years. It provides the opportunity to gather detailed information for analysis. Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging is one good example of it. The information we get after fusing data from EEG and fMRI have both high temporal and spatial resolution. On the other side, this EEG recording suffers from some additional artifacts due to the fMRI environment, in particular, the Ballistocardiogram artifact. In this article, a new method of removing Ballistocardiogram Artifact from evoked potential studies is proposed. The method does not require any reference signal or prior information. The results presented are using the data of three subjects (volunteers). The results show that the proposed method can efficiently reduce Ballistocardiogram artifact and has performed better compared to the conventional methods.","PeriodicalId":288427,"journal":{"name":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","volume":"187 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLA.2015.81","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Multi-modality data acquisition is a topic of research that gained interest in the recent years. It provides the opportunity to gather detailed information for analysis. Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging is one good example of it. The information we get after fusing data from EEG and fMRI have both high temporal and spatial resolution. On the other side, this EEG recording suffers from some additional artifacts due to the fMRI environment, in particular, the Ballistocardiogram artifact. In this article, a new method of removing Ballistocardiogram Artifact from evoked potential studies is proposed. The method does not require any reference signal or prior information. The results presented are using the data of three subjects (volunteers). The results show that the proposed method can efficiently reduce Ballistocardiogram artifact and has performed better compared to the conventional methods.
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
基于EMD的脑电诱发电位研究中弹道心图伪影还原方法
多模态数据采集是近年来备受关注的一个研究课题。它提供了收集详细信息进行分析的机会。同时使用脑电图(EEG)和功能磁共振成像就是一个很好的例子。脑电和功能磁共振数据融合后得到的信息具有较高的时间和空间分辨率。另一方面,由于fMRI环境,这种脑电图记录遭受一些额外的伪影,特别是ballistocardiography伪影。本文提出了一种从诱发电位研究中去除心电图伪影的新方法。该方法不需要任何参考信号或先验信息。所呈现的结果使用了三个受试者(志愿者)的数据。实验结果表明,该方法能有效地降低心电图伪影,与传统方法相比具有更好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Prediction of SPEI Using MLR and ANN: A Case Study for Wilsons Promontory Station in Victoria Statistical Downscaling of Climate Change Scenarios of Rainfall and Temperature over Indira Sagar Canal Command Area in Madhya Pradesh, India Lambda Consensus Clustering Time Series Prediction Based on Online Learning NewsCubeSum: A Personalized Multidimensional News Update Summarization System
×
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