Visual evoked potential estimation by artificial neural network filter: comparison with the ensemble averaging method

K. Fung, F. Chan, F. K. Lam, P. Poon, J.G. Liu
{"title":"Visual evoked potential estimation by artificial neural network filter: comparison with the ensemble averaging method","authors":"K. Fung, F. Chan, F. K. Lam, P. Poon, J.G. Liu","doi":"10.1109/IEMBS.1995.575372","DOIUrl":null,"url":null,"abstract":"The application of an artificial neural network filter (ANNF) to give a non-linear estimation of the visual evoked potential (VEP) is presented. A feed forward ANNF is designed and trained by a training set consisting of a training signal and a target signal. The training signal is the raw VEP from a single trial while the target signal has much higher SNR which is achieved by ensemble averaging of 100 trials. The result shows that 10 ensembles is needed by ANNF to generate a satisfactory result against 60 ensembles required by traditional ensemble averaging. VEP from individual trial could be obtained; thus the study of the variation of signal across trials is possible.","PeriodicalId":20509,"journal":{"name":"Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1995-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 17th International Conference of the Engineering in Medicine and Biology Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEMBS.1995.575372","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

The application of an artificial neural network filter (ANNF) to give a non-linear estimation of the visual evoked potential (VEP) is presented. A feed forward ANNF is designed and trained by a training set consisting of a training signal and a target signal. The training signal is the raw VEP from a single trial while the target signal has much higher SNR which is achieved by ensemble averaging of 100 trials. The result shows that 10 ensembles is needed by ANNF to generate a satisfactory result against 60 ensembles required by traditional ensemble averaging. VEP from individual trial could be obtained; thus the study of the variation of signal across trials is possible.
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
人工神经网络滤波的视觉诱发电位估计:与集合平均法的比较
应用人工神经网络滤波器(ANNF)对视觉诱发电位进行非线性估计。前馈神经网络由训练信号和目标信号组成的训练集进行设计和训练。训练信号是单次试验的原始VEP,而目标信号的信噪比要高得多,这是通过100次试验的集合平均得到的。结果表明,与传统的集合平均方法所需的60个集合相比,ANNF只需要10个集合就能产生令人满意的结果。单个试验可获得VEP;因此,研究不同试验间信号的变化是可能的。
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
Automatic seizure detection in newborns and infants Functional conditioning of skeletal muscle ventricles Electrical interactions between cardiac cells studied with "model clamp" An intelligent airway sensor system to increase safety in computer controlled mechanical ventilation A distributed health information network for consultative services in surgical pathology
×
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