eprintid: 9591
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/95/91
datestamp: 2023-11-09 16:36:14
lastmod: 2023-11-09 16:36:14
status_changed: 2023-11-09 16:29:21
type: conference_item
metadata_visibility: show
creators_name: Abdalsalam Mohamed, E.
creators_name: Zuki Yusoff, M.
creators_name: Khalil Adam, I.
creators_name: Ali Hamid, E.
creators_name: Al-Shargie, F.
creators_name: Muzammel, M.
title: Enhancing EEG Signals in Brain Computer Interface Using Intrinsic Time-Scale Decomposition
ispublished: pub
keywords: Biomedical signal processing; Electroencephalography; Electrophysiology; Extraction; Feature extraction; Neural networks, EEG signals; Feature extraction methods; Human brain; Intelligent method; Intrinsic time-scale decompositions; Motor imagery; Nonstationary, Brain computer interface
note: cited By 4; Conference of 5th International Conference on Fundamental and Applied Sciences, ICFAS 2018 ; Conference Date: 13 August 2018 Through 15 August 2018; Conference Code:142772
abstract: A brain-computer interface (BCI) provides a link between the human brain and a computer. The EEG signal is nonlinear and non-stationary. Feature extraction is one of the most important steps in any BCI system; as such, enhancement to the existing methods has been incorporated in this work. For this, we propose a four-class movement imaginations of the right hand, left hand, both hands, and both feet, and develop feature extraction methods utilizing an intelligent method based on intrinsic time-scale decomposition (ITD) and Artificial neural networks (ANN). Based on the processed electroencephalography (EEG) data recorded from nine subjects, ITD accurately classified and discriminated the four classes of motor imagery; the average accuracy achieved is 92.20. © Published under licence by IOP Publishing Ltd.
date: 2018
publisher: Institute of Physics Publishing
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058226411&doi=10.1088%2f1742-6596%2f1123%2f1%2f012004&partnerID=40&md5=42c7312889a988b6171b96779a8bf557
id_number: 10.1088/1742-6596/1123/1/012004
full_text_status: none
publication: Journal of Physics: Conference Series
volume: 1123
number: 1
refereed: TRUE
issn: 17426588
citation:   Abdalsalam Mohamed, E. and Zuki Yusoff, M. and Khalil Adam, I. and Ali Hamid, E. and Al-Shargie, F. and Muzammel, M.  (2018) Enhancing EEG Signals in Brain Computer Interface Using Intrinsic Time-Scale Decomposition.  In: UNSPECIFIED.