Independnet Component Analysis And Its Application In FMRI

When people study the function of the brain using the functional magnetic resonance imaging (fMRI), instrument only notes the activation of the different areas of the cortex which are the superposition of the different signals produced from the deep activation of the brain. These independent signals reflect the actual situation of the activation of the brain which is valuable for us. So we need to separate the independent signals to obtain the actual physiological signals in order to serve the practical problem. Independent component analysis (ICA) is an effective method for this problem. The main work of this paper is surrounding processing functional magnetic resonance imaging using ICA:1. A new ICA algorithm- new fixed-point (NewFP) is applied to deal with the fMRI data. And the standard information theoretic methods - Akaike s information criterion (AIC) or minimum description length (MDL) is used to estimate the number of the sources and principal component analysis (PCA) is used to reduce the data. These methods can estimate the number of the sources, reduce the superfluous signals and raise the speed of operation. We apply NewFP algorithm and another algorithm (FastICA) to the fMRI data, by comparison, the result shows that NewFP algorithm is superior to the FastICA.2. Three popular methods which can process the signals from a group of subjects- Group independent component analysis (Group ICA) are produced and analyzed, this is very useful for processing fMRI data.3. NewFP algorithm with an useful Group ICA method are used to deal with the fMRI data from group subjects. The result shows that NewFP algorithm is superior to the FastICA. And the result shows using Group ICA to process the data from the group subjects not only can reduce the data, raise the speed of operation but also can ensure the accuracy of the result.