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Description: ICA can be used in brain activation studies to reduce the number of dimension and filter out independent and interesting activations. This demonstration shows two studies. One provided by Hvidovre Universitets Hospital, Denmark, that consists of fMRI scannings of humans. Another provided by the EU sponsored MAPAWAMO project from fMRI scannings of monkeys. In the demo comparison between icaMS, icaML, icaMF, icaMF (positive sources) and PCA can be made. More detailes can found in [2].
-ICA can be used in brain activation studies to reduce the number of dimension and filter out independent and interesting activations. This demonstration shows two studies. One provided by Hvidovre Universitets Hospital, Denmark, that consists of fMRI scannings of humans. Another provided by the EU sponsored MAPAWAMO project from fMRI scannings of monkeys. In the demo comparison between icaMS, icaML, icaMF, icaMF (positive sources) and PCA can be made. More detailes can found in [2].
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Size: 2772862 |
Author: 海心 |
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Description: ICA can be used in brain activation studies to reduce the number of dimension and filter out independent and interesting activations. This demonstration shows two studies. One provided by Hvidovre Universitets Hospital, Denmark, that consists of fMRI scannings of humans. Another provided by the EU sponsored MAPAWAMO project from fMRI scannings of monkeys. In the demo comparison between icaMS, icaML, icaMF, icaMF (positive sources) and PCA can be made. More detailes can found in [2].
-ICA can be used in brain activation studies to reduce the number of dimension and filter out independent and interesting activations. This demonstration shows two studies. One provided by Hvidovre Universitets Hospital, Denmark, that consists of fMRI scannings of humans. Another provided by the EU sponsored MAPAWAMO project from fMRI scannings of monkeys. In the demo comparison between icaMS, icaML, icaMF, icaMF (positive sources) and PCA can be made. More detailes can found in [2].
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Size: 2772992 |
Author: 海心 |
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Description: We present the technique of the ICA with Reference (ICA-R) to extract an interesting subset of independent sources from their linear
mixtures when some a priori information of the sources are available in the form of rough templates (references). The constrained
independent component analysis (cICA) is extended to incorporate the reference signals that carry some information of the sources as
additional constraints into the ICA contrast function. A neural algorithm is then proposed using a Newton-like approach to obtain an
optimal solution to the constrained optimization problem. Stability of the convergence and selection of parameters in the learning
algorithm are analyzed. Experiments with synthetic signals and real fMRI data demonstrate the efficacy and accuracy of the proposed
algorithm.
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Size: 390144 |
Author: ma ming |
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Description: matlab code for doing ICA for fMRI data
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Size: 7669760 |
Author: 胡理 |
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Description: We consider an extension of ICA and BSS for separating
mutually dependent and independent components from two related data
sets. We propose a new method which first uses canonical correlation
analysis for detecting subspaces of independent and dependent components.
Different ICA and BSS methods can after this be used for final
separation of these components. Our method has a sound theoretical
basis, and it is straightforward to implement and computationally not
demanding. Experimental results on synthetic and real-world fMRI data
sets demonstrate its good performance.
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Size: 195584 |
Author: msreddy |
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Description: 实现fmri的ICA和IC selection的操作，找出与所需模板template对应的成分(Implement the operation of fMRI's ICA and IC selection, and find out the components corresponding to the required template template)
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Size: 60219392 |
Author: 时光如风 |
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