Blind source separation for group fMRI signals using a new independent component analysis algorithm

Huan Wen Tang; Wei Wei Zhang; Zhen Wei Shi; Li Li Pan; Yi Yuan Tang

Blind source separation for group fMRI signals using a new independent component analysis algorithm

Huan Wen Tang^*
, Wei Wei Zhang
, Zhen Wei Shi
, Li Li Pan
, Yi Yuan Tang

^*此作品的通讯作者

Dalian University of Technology
Tsinghua University

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

Independent component analysis (ICA) has been used effectively for processing the functional magnetic resonance imaging (fMRI) data, but usually the data come from one subject. To process the signals from a group of subjects, an extended independent component analysis method, Group ICA is proposed. The results show that this method can reduce the data and receive the statistical result fast. In the processing, an independent component analysis method named new fixed-point is used, and the results show that the new method is superior to the FastICA on the accuracy of estimating the temporal dynamics of activations.

源语言	英语
页（从-至）	773-776
页数	4
期刊	Dalian Ligong Daxue Xuebao/Journal of Dalian University of Technology
卷	47
期	5
出版状态	已出版 - 9月 2007
已对外发布	是

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title = "Blind source separation for group fMRI signals using a new independent component analysis algorithm",

abstract = "Independent component analysis (ICA) has been used effectively for processing the functional magnetic resonance imaging (fMRI) data, but usually the data come from one subject. To process the signals from a group of subjects, an extended independent component analysis method, Group ICA is proposed. The results show that this method can reduce the data and receive the statistical result fast. In the processing, an independent component analysis method named new fixed-point is used, and the results show that the new method is superior to the FastICA on the accuracy of estimating the temporal dynamics of activations.",

keywords = "Blind source separation, Functional magnetic resonance imaging, Independent component analysis",

author = "Tang, \{Huan Wen\} and Zhang, \{Wei Wei\} and Shi, \{Zhen Wei\} and Pan, \{Li Li\} and Tang, \{Yi Yuan\}",

year = "2007",

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T1 - Blind source separation for group fMRI signals using a new independent component analysis algorithm

AU - Tang, Huan Wen

AU - Zhang, Wei Wei

AU - Shi, Zhen Wei

AU - Pan, Li Li

AU - Tang, Yi Yuan

PY - 2007/9

Y1 - 2007/9

N2 - Independent component analysis (ICA) has been used effectively for processing the functional magnetic resonance imaging (fMRI) data, but usually the data come from one subject. To process the signals from a group of subjects, an extended independent component analysis method, Group ICA is proposed. The results show that this method can reduce the data and receive the statistical result fast. In the processing, an independent component analysis method named new fixed-point is used, and the results show that the new method is superior to the FastICA on the accuracy of estimating the temporal dynamics of activations.

AB - Independent component analysis (ICA) has been used effectively for processing the functional magnetic resonance imaging (fMRI) data, but usually the data come from one subject. To process the signals from a group of subjects, an extended independent component analysis method, Group ICA is proposed. The results show that this method can reduce the data and receive the statistical result fast. In the processing, an independent component analysis method named new fixed-point is used, and the results show that the new method is superior to the FastICA on the accuracy of estimating the temporal dynamics of activations.

KW - Blind source separation

KW - Functional magnetic resonance imaging

KW - Independent component analysis

UR - https://www.scopus.com/pages/publications/35948935661

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SN - 1000-8608

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JO - Dalian Ligong Daxue Xuebao/Journal of Dalian University of Technology

JF - Dalian Ligong Daxue Xuebao/Journal of Dalian University of Technology

IS - 5

ER -

Blind source separation for group fMRI signals using a new independent component analysis algorithm

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