%0 Conference Paper
%A Abdalsalam Mohamed, E.
%A Zuki Yusoff, M.
%A Khalil Adam, I.
%A Ali Hamid, E.
%A Al-Shargie, F.
%A Muzammel, M.
%D 2018
%F scholars:9591
%I Institute of Physics Publishing
%K 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
%N 1
%R 10.1088/1742-6596/1123/1/012004
%T Enhancing EEG Signals in Brain Computer Interface Using Intrinsic Time-Scale Decomposition
%U https://khub.utp.edu.my/scholars/9591/
%V 1123
%X 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.
%Z 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