relation: https://khub.utp.edu.my/scholars/9591/
title: Enhancing EEG Signals in Brain Computer Interface Using Intrinsic Time-Scale Decomposition
creator: Abdalsalam Mohamed, E.
creator: Zuki Yusoff, M.
creator: Khalil Adam, I.
creator: Ali Hamid, E.
creator: Al-Shargie, F.
creator: Muzammel, M.
description: 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.
publisher: Institute of Physics Publishing
date: 2018
type: Conference or Workshop Item
type: PeerReviewed
identifier:   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.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058226411&doi=10.1088%2f1742-6596%2f1123%2f1%2f012004&partnerID=40&md5=42c7312889a988b6171b96779a8bf557
relation: 10.1088/1742-6596/1123/1/012004
identifier: 10.1088/1742-6596/1123/1/012004