FMRI Data Analysis Using Dempster-Shafer Method with Estimating Voxel Selectivity by Belief Measure - Université Polytechnique des Hauts-de-France
Article Dans Une Revue International journal of advanced computer science and applications (IJACSA) Année : 2016

FMRI Data Analysis Using Dempster-Shafer Method with Estimating Voxel Selectivity by Belief Measure

Résumé

In the functional Magnetic Resonance Imaging (fMRI) data analysis, detecting the activated voxels is a challenging research problem where the existing methods have shown some limits. We propose a new method wherein brain mapping is done based on Dempster-Shafer theory of evidence (DS) that is a useful method in uncertain representation analysis. Dempster-Shafer allows finding the activated regions by checking the activated voxels in fMRI data. The activated brain areas related to a given stimulus are detected by using a belief measure as a metric for evaluating activated voxels. To test the performance of the proposed method, artificial and real auditory data have been employed. The comparison of the introduced method with the t-test and GLM method has clearly shown that the proposed method can provide a higher correct detection of activated voxels
Fichier principal
Vignette du fichier
Paper_43-fMRI_Data_Analysis_Using_Dempster_Shafer_Method_with_Estimating_Voxel.pdf (365.2 Ko) Télécharger le fichier
Origine Publication financée par une institution
Licence

Dates et versions

hal-03427002 , version 1 (31-08-2022)

Licence

Identifiants

Citer

Abdelouahab Attia, Abdelouahab Moussaoui, Abdelmalik Taleb-Ahmed. FMRI Data Analysis Using Dempster-Shafer Method with Estimating Voxel Selectivity by Belief Measure. International journal of advanced computer science and applications (IJACSA), 2016, 7 (1), pp.316-324. ⟨10.14569/IJACSA.2016.070143⟩. ⟨hal-03427002⟩
46 Consultations
30 Téléchargements

Altmetric

Partager

More