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An entropy based method for activation detection of functional MRI data using Independent Component Analysis

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4 Author(s)
Akhbari, M. ; Dept. of Electr. Eng., Sharif Univ. of Technol., Tehran, Iran ; Babaie-Zadeh, M. ; Fatemizadeh, E. ; Jutten, C.

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.

Published in:

Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on

Date of Conference:

14-19 March 2010