{"title":"第14章:基于ICA的功能MRI序列组分分离方法的比较研究","authors":"G.R. Rad, H. Larijani","doi":"10.1109/GMAI.2008.9","DOIUrl":null,"url":null,"abstract":"This paper prepares a review of ICA based approaches that are used for separation of components in functional MRI sequences. In previous works, the FastICA and the Infomax algorithms are investigated in more details; therefore, in this paper we focus on methods such as \"radical ICA\", \"SDD ICA\", \"Erica\" and \"Evd\" for separation purposes. This comparative study provides reliable framework for brain researchers to choose appropriate ICA based method for their special investigation purposes.","PeriodicalId":393559,"journal":{"name":"2008 3rd International Conference on Geometric Modeling and Imaging","volume":"129 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Chapter 14: A Comparative Study of ICA Based Approaches for Separation of Components in Functional MRI Sequences\",\"authors\":\"G.R. Rad, H. Larijani\",\"doi\":\"10.1109/GMAI.2008.9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper prepares a review of ICA based approaches that are used for separation of components in functional MRI sequences. In previous works, the FastICA and the Infomax algorithms are investigated in more details; therefore, in this paper we focus on methods such as \\\"radical ICA\\\", \\\"SDD ICA\\\", \\\"Erica\\\" and \\\"Evd\\\" for separation purposes. This comparative study provides reliable framework for brain researchers to choose appropriate ICA based method for their special investigation purposes.\",\"PeriodicalId\":393559,\"journal\":{\"name\":\"2008 3rd International Conference on Geometric Modeling and Imaging\",\"volume\":\"129 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 3rd International Conference on Geometric Modeling and Imaging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/GMAI.2008.9\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 3rd International Conference on Geometric Modeling and Imaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GMAI.2008.9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Chapter 14: A Comparative Study of ICA Based Approaches for Separation of Components in Functional MRI Sequences
This paper prepares a review of ICA based approaches that are used for separation of components in functional MRI sequences. In previous works, the FastICA and the Infomax algorithms are investigated in more details; therefore, in this paper we focus on methods such as "radical ICA", "SDD ICA", "Erica" and "Evd" for separation purposes. This comparative study provides reliable framework for brain researchers to choose appropriate ICA based method for their special investigation purposes.
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