{"title":"一种分离fMRI噪声分量的迭代ICA方法","authors":"Wanjun Huang, I. Panahi, R. Briggs","doi":"10.1109/EMBSW.2007.4454158","DOIUrl":null,"url":null,"abstract":"A new iterative algorithm for separating mixtures of multi-channel signals is proposed. This algorithm extends the instantaneous independent component analysis algorithm to multi-channel blind source separation algorithm. Separation is processed by decomposing convolutive mixtures to instantaneous mixtures. Simulation results for real fMRI (functional magnetic resonance imaging) scanner noise show that the proposed algorithm is very effective in blind source separation.","PeriodicalId":333843,"journal":{"name":"2007 IEEE Dallas Engineering in Medicine and Biology Workshop","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Iterative ICA Method for Separating fMRI Acoustic Noise Components\",\"authors\":\"Wanjun Huang, I. Panahi, R. Briggs\",\"doi\":\"10.1109/EMBSW.2007.4454158\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new iterative algorithm for separating mixtures of multi-channel signals is proposed. This algorithm extends the instantaneous independent component analysis algorithm to multi-channel blind source separation algorithm. Separation is processed by decomposing convolutive mixtures to instantaneous mixtures. Simulation results for real fMRI (functional magnetic resonance imaging) scanner noise show that the proposed algorithm is very effective in blind source separation.\",\"PeriodicalId\":333843,\"journal\":{\"name\":\"2007 IEEE Dallas Engineering in Medicine and Biology Workshop\",\"volume\":\"64 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2007 IEEE Dallas Engineering in Medicine and Biology Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EMBSW.2007.4454158\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 IEEE Dallas Engineering in Medicine and Biology Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EMBSW.2007.4454158","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Iterative ICA Method for Separating fMRI Acoustic Noise Components
A new iterative algorithm for separating mixtures of multi-channel signals is proposed. This algorithm extends the instantaneous independent component analysis algorithm to multi-channel blind source separation algorithm. Separation is processed by decomposing convolutive mixtures to instantaneous mixtures. Simulation results for real fMRI (functional magnetic resonance imaging) scanner noise show that the proposed algorithm is very effective in blind source separation.
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