An Entropy Based Method for Activation Detection of functional MRI Data Using Independent Component Analysis

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.
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Mahsa Akhbari, Massoud Babaie-Zadeh, Emad Fatemizadeh, Christian Jutten. An Entropy Based Method for Activation Detection of functional MRI Data Using Independent Component Analysis. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2010), Mar 2010, Dallas, Texas, United States. pp.2014-2017. ⟨hal-00466285⟩

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