eprintid: 15124
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/51/24
datestamp: 2023-11-10 03:29:44
lastmod: 2023-11-10 03:29:44
status_changed: 2023-11-10 01:58:42
type: conference_item
metadata_visibility: show
creators_name: Al-Quraishi, M.S.
creators_name: Elamvazuthi, I.
creators_name: Tang, T.B.
creators_name: Al-Qurishi, M.
creators_name: Parasuraman, S.
creators_name: Borboni, A.
title: Lower limb Movements' Classifications using Hemodynamic Response:fNIRS Study
ispublished: pub
keywords: Biomedical engineering; Biomedical signal processing; Brain; Brain computer interface; Hemodynamics; Infrared devices; Joints (anatomy); Near infrared spectroscopy, Brain functions; Brain machine interface (BMIs); Classification accuracy; Electromagnetic noise; Emitters and detectors; Functional near-infrared spectroscopy (fnirs); Hemodynamic response; Signal classification, Functional neuroimaging
note: cited By 2; Conference of 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020 ; Conference Date: 1 March 2021 Through 3 March 2021; Conference Code:168430
abstract: Functional near-infrared spectroscopy (fNIRS) has become a viable approach for brain function investigation and is an interesting modality for brain-machine interfaces (BMIs) due to its portability and resistance to electromagnetic noise. In this work, a hemodynamic response based on fNIRS signals was utilized to classify the right and left ankle joint movements. To achieve this objective, 32 optodes (emitters and detectors) were used to measure the hemodynamic responses in the motor cortex area during the motor execution task of the ankle joint movements. Two-channel sets were formed one including the channels directly related to the movement task, and another including all of the proposed channels. The results of this study reveal that the scheme based only on the selected channels outperformed the scheme that uses all channels. The classification accuracies were 91.38  and 89.86  respectively. These results demonstrated that fNIRS signal classification can be enhanced by eliminating the redundant channels. © 2021 IEEE.
date: 2021
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104827178&doi=10.1109%2fIECBES48179.2021.9398783&partnerID=40&md5=1d9a04d743f66b17978f0c1a1b7a54b7
id_number: 10.1109/IECBES48179.2021.9398783
full_text_status: none
publication: Proceedings - 2020 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2020
pagerange: 76-81
refereed: TRUE
isbn: 9781728142456
citation:   Al-Quraishi, M.S. and Elamvazuthi, I. and Tang, T.B. and Al-Qurishi, M. and Parasuraman, S. and Borboni, A.  (2021) Lower limb Movements' Classifications using Hemodynamic Response:fNIRS Study.  In: UNSPECIFIED.