%0 Journal Article
%T BLIND SOURCE SEPARATION FOR FMRI SIGNALS USING A NEW INDEPENDENT COMPONENT ANALYSIS ALGORITHM<br>新的独立成分分析算法实现功能磁共振成像信号的盲分离
%A WU Zhen-hua
%A SHI Zhen-wei
%A TANG Huan-wen
%A TANG Yi-yuan
%A <br>武振华
%A 史振威
%A 唐焕文
%A 唐一源
%J 生物物理学报
%D 2004
%I 
%X In order to separate independent components (task-related signal and other noises) from functional magnetic reasonance imaging(fMRI)signals, a new independent component analysis algorithm was used. In contrast to fastICA, the algorithm reduced computation and raised speed of operation. It also separated independent components from fMRI signals very well.
%K Newton algorithm
%K Independent component analysis
%K Functional magnetic reasonanceimaging
%K Blind source separation<br>牛顿型算法
%K 独立成分分析
%K 功能磁共振成像
%K 盲源分离
%K 信号
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=90BA3D13E7F3BC869AC96FB3DA594E3FE34FBF7B8BC0E591&jid=E0C9D9BBED813D6674AC13E942EAC86D&aid=C84501BF98ECC328&yid=D0E58B75BFD8E51C&vid=A04140E723CB732E&iid=38B194292C032A66&sid=847B14427F4BF76A&eid=C29816B2656377A7&journal_id=1000-6737&journal_name=生物物理学报&referenced_num=3&reference_num=11