eprintid: 6884
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/68/84
datestamp: 2023-11-09 16:18:41
lastmod: 2023-11-09 16:18:41
status_changed: 2023-11-09 16:07:55
type: conference_item
metadata_visibility: show
creators_name: Zafar, R.
creators_name: Malik, A.S.
creators_name: Kamel, N.
creators_name: Dass, S.C.
title: Role of voxel selection and ROI in fMRI data analysis
ispublished: pub
keywords: 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
note: 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
abstract: 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.
date: 2016
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84985022960&doi=10.1109%2fMeMeA.2016.7533739&partnerID=40&md5=97c1725380c32a7ad17082732529c5ad
id_number: 10.1109/MeMeA.2016.7533739
full_text_status: none
publication: 2016 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2016 - Proceedings
refereed: TRUE
isbn: 9781467391726
citation:   Zafar, R. and Malik, A.S. and Kamel, N. and Dass, S.C.  (2016) Role of voxel selection and ROI in fMRI data analysis.  In: UNSPECIFIED.