TY - JOUR
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 - http://www.scopus.com/inward/record.url?scp=35948935661&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:35948935661
SN - 1000-8608
VL - 47
SP - 773
EP - 776
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 -