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An entropy based method for activation detection of functional MRI data using independent component analysis
Akhbari, M
An entropy based method for activation detection of functional MRI data using independent component analysis
Akhbari, M ; Sharif University of Technology | 2010
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- Type of Document: Article
- DOI: 10.1109/ICASSP.2010.5494915
- Publisher: 2010
- Abstract:
- Independent Component Analysis (ICA) can be used to decompose functional Magnetic Resonance Imaging (fMRI) data into a set of statistically independent images which are likely to be the sources of fMRI data. After applying ICA, a set of independent components are produced, and then, a "meaningful" subset from these components must be identified, because a large majority of components are non-interesting. So, interpreting the components is an important and also difficult task. In this paper, we propose a criterion based on the entropy of time courses to automatically select the components of interest. This method does not require to know the stimulus pattern of the experiment
- Keywords:
- Activation detection ; Entropy-based methods ; FMRI ; fMRI data ; Functional magnetic resonance imaging ; Functional MRI ; ICA ; Independent components ; Stimulus pattern ; Time course ; Activation analysis ; Entropy ; Magnetic resonance imaging ; Multivariant analysis ; Resonance ; Signal detection ; Signal processing ; Independent component analysis
- Source: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 14 March 2010 through 19 March 2010 ; March , 2010 , Pages 2014-2017 ; 15206149 (ISSN) ; 9781424442966 (ISBN)
- URL: http://ieeexplore.ieee.org/document/5494915