A method based on independent component analysis for processing fMRI data / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 64-66, 2002.
Article
in Chinese
| WPRIM
| ID: wpr-334324
ABSTRACT
Independent component analysis (ICA) is a new technique in statistical signal processing to extract independent components from multidimensional measurements of mixed signals. In this paper, for the processing of functional magnetic resonance imaging(fMRI) data, two signals of near voxels are used as the mixed signals and are separated by ICA. The correlation coefficients between the reference signal and the separated signals are calculated and those voxels whose correlation coefficients are greater than a threshold are considered to be the activated voxels by the stimulation, and so the functional localization of the stimulation is completed. The validity of the method was primarily proved by trial of real brain functional magnetic resonance imaging data.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Pathology
/
Photic Stimulation
/
Physiology
/
Algorithms
/
Brain
/
Magnetic Resonance Imaging
/
Principal Component Analysis
Limits:
Humans
Language:
Chinese
Journal:
Journal of Biomedical Engineering
Year:
2002
Type:
Article
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