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

Mahsa Akhbari (1) , Massoud Babaie-Zadeh (1) , Emad Fatemizadeh (1) , Christian Jutten (2)

1 EE department of SUT [Tehran] - Department of Electrical Engineering [Tehran]
2 GIPSA-VIBS - GIPSA - Vision and Brain Signal Processing

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.

Mots clés: fMRI; ICA; Entropy; Activation detection

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

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