Power and asymmetry ratio of spectral bands for mental task recognition

R. Palaniappan, R. Paramesran
{"title":"Power and asymmetry ratio of spectral bands for mental task recognition","authors":"R. Palaniappan, R. Paramesran","doi":"10.1109/ISSPA.2001.950258","DOIUrl":null,"url":null,"abstract":"We use the power and asymmetry ratio of spectral bands to recognise mental tasks from electroencephalogram signals using a fuzzy ARTMAP neural network. Classical spectral analysis using the Wiener-Khintchine theorem and modem parametric spectral analysis using the autoregressive method are used to obtain these features. The highest classification results of 90% for a subject recognising two mental tasks validate the method.","PeriodicalId":236050,"journal":{"name":"Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSPA.2001.950258","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

We use the power and asymmetry ratio of spectral bands to recognise mental tasks from electroencephalogram signals using a fuzzy ARTMAP neural network. Classical spectral analysis using the Wiener-Khintchine theorem and modem parametric spectral analysis using the autoregressive method are used to obtain these features. The highest classification results of 90% for a subject recognising two mental tasks validate the method.
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
脑任务识别的光谱波段功率和不对称比
我们使用模糊ARTMAP神经网络,利用谱带的功率和不对称比从脑电图信号中识别心理任务。利用维纳-钦定理的经典谱分析和自回归方法的现代参数谱分析得到了这些特征。一个受试者识别两种心理任务的最高分类结果为90%,证实了该方法的有效性。
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
Large dynamic range time-frequency signal analysis with application to helicopter Doppler radar data Statistical analysis of neural network modeling and identification of nonlinear systems with memory Design of oversampled uniform DFT filter banks with reduced inband aliasing and delay constraints Identification of DCT signs for sub-block coding Skin color detection for face localization in human-machine communications
×
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