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

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.

@article{Abdelouahab2016,
  title     = {fMRI Data Analysis Using Dempster-Shafer Method with Estimating Voxel Selectivity by Belief Measure},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {1},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {ATTIA Abdelouahab and MOUSSAOUI Abdelouahab and TALEB-AHMED Abdelmalik},
  doi       = {10.14569/IJACSA.2016.070143},
  url       = {https://doi.org/10.14569/IJACSA.2016.070143}
}