The Proposal and It’s Evalution of Biometric Authentication Method by EEG Analysis Using Image Stimulation

Masato Yamashita, M. Nakazawa, Yukinobu Nishikawa
{"title":"The Proposal and It’s Evalution of Biometric Authentication Method by EEG Analysis Using Image Stimulation","authors":"Masato Yamashita, M. Nakazawa, Yukinobu Nishikawa","doi":"10.23919/ICMU.2018.8653605","DOIUrl":null,"url":null,"abstract":"In recent years, techniques of Brain Machine Interface (BMI) which conducts human communication and robot manipulation using human brain activity are widely researched. This is the result of a noninvasive electroencephalograph device that can measure Electroencephalogram (EEG) in real time. However, there is a present condition that the authentication method when BMI is not much researched. In our research, we propose a biometric authentication method of electroencephalogram using image stimulation. In this research, we propose a biometric authentication method of electroencephalogram using image stimulation. In this paper, we construct and then evaluate a system that performs biometric authentication using EEG at image stimulus. We perform feature extraction using cross-correlation coefficient, and SVM for classification / authentication. Moreover We considered the method for preprocessing (digital filter, artifact countermeasure, epoch), we verify more appropriate preprocessing method. We verified the proposed method. In our proposed system, EER: 2.0% was obtained when artifact countermeasure, digital filter (IIR filter), and epoch method were used. From the result of FAR and FRR, our system was suggested that accuracy is improved by taking artifact countermeasure.","PeriodicalId":398108,"journal":{"name":"2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 Eleventh International Conference on Mobile Computing and Ubiquitous Network (ICMU)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/ICMU.2018.8653605","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

In recent years, techniques of Brain Machine Interface (BMI) which conducts human communication and robot manipulation using human brain activity are widely researched. This is the result of a noninvasive electroencephalograph device that can measure Electroencephalogram (EEG) in real time. However, there is a present condition that the authentication method when BMI is not much researched. In our research, we propose a biometric authentication method of electroencephalogram using image stimulation. In this research, we propose a biometric authentication method of electroencephalogram using image stimulation. In this paper, we construct and then evaluate a system that performs biometric authentication using EEG at image stimulus. We perform feature extraction using cross-correlation coefficient, and SVM for classification / authentication. Moreover We considered the method for preprocessing (digital filter, artifact countermeasure, epoch), we verify more appropriate preprocessing method. We verified the proposed method. In our proposed system, EER: 2.0% was obtained when artifact countermeasure, digital filter (IIR filter), and epoch method were used. From the result of FAR and FRR, our system was suggested that accuracy is improved by taking artifact countermeasure.
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
基于图像刺激的脑电分析生物特征认证方法的提出与评价
近年来,利用人脑活动进行人机交流和机器人操作的脑机接口(BMI)技术得到了广泛的研究。这是一种可以实时测量脑电图(EEG)的无创脑电图仪的结果。但目前的现状是,对BMI的鉴定方法研究较少。在我们的研究中,我们提出了一种基于图像刺激的脑电图生物识别认证方法。在这项研究中,我们提出了一种基于图像刺激的脑电图生物识别认证方法。在本文中,我们构建并评估了一个利用脑电在图像刺激下进行生物识别认证的系统。我们使用互相关系数进行特征提取,并使用支持向量机进行分类/认证。此外,我们还考虑了预处理方法(数字滤波、伪影对抗、历元),验证了更合适的预处理方法。我们验证了所提出的方法。采用伪干扰、数字滤波(IIR滤波)和历元法后,系统的干扰系数为2.0%。根据FAR和FRR的结果,提出了采用伪干扰来提高系统精度的建议。
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
Modelling and Analysing Overlay Networks by Ambients with Wormholes VR Classroom: Enhancing Learning Experience with Virtual Class Rooms [Copyright notice] ICMU 2018 Committees Deep Reinforcement Learning-Based Method of Mobile Data Offloading
×
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