%0 Conference Paper
%A Zafar, R.
%A Malik, A.S.
%A Kamel, N.
%A Dass, S.C.
%D 2016
%F scholars:6884
%I Institute of Electrical and Electronics Engineers Inc.
%K Artificial intelligence; Brain; Brain mapping; Classification (of information); Electroencephalography; Electrophysiology; Feature extraction; Image segmentation; Learning algorithms; Learning systems; Magnetic resonance imaging; Magnetoencephalography; Radial basis function networks, Anatomical regions; Dimension reduction; fMRI; fMRI data analysis; Functional magnetic resonance imaging; Generalized linear model; Visual experiments; voxel, Functional neuroimaging
%R 10.1109/MeMeA.2016.7533739
%T Role of voxel selection and ROI in fMRI data analysis
%U https://khub.utp.edu.my/scholars/6884/
%X Functional magnetic resonance imaging (fMRI) is one of the most popular and reliable modality to measure brain activities. The quality of fMRI data is best among other modalities such as Electroencephalography (EEG) and Magnetoencephalography (MEG). In fMRI, normally number of features are more than the number of instances so it is necessary to select the features and do dimension reduction to remove noisy and redundant data. Many techniques and methods are used to select the significant features (voxels). In this paper, the significant voxels are selected within the anatomical region of interest (ROI) based on the absolute values. In this study, we have predicted the brain states using two machine learning algorithm, i.e, Radial basis function (RBF) network and Naïve Bayes. A visual experiment with two categories is done. In conclusion, it is shown that less number of voxels and specific brain regions can increase the accuracy of prediction. © 2016 IEEE.
%Z cited By 7; Conference of 11th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2016 ; Conference Date: 15 May 2016 Through 18 May 2016; Conference Code:123196