TY  - CONF
SN  - 17426588
N2  - 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.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058226411&doi=10.1088%2f1742-6596%2f1123%2f1%2f012004&partnerID=40&md5=42c7312889a988b6171b96779a8bf557
KW  - Biomedical signal processing; Electroencephalography; Electrophysiology; Extraction; Feature extraction; Neural networks
KW  -  EEG signals; Feature extraction methods; Human brain; Intelligent method; Intrinsic time-scale decompositions; Motor imagery; Nonstationary
KW  -  Brain computer interface
ID  - scholars9591
VL  - 1123
Y1  - 2018///
TI  - Enhancing EEG Signals in Brain Computer Interface Using Intrinsic Time-Scale Decomposition
PB  - Institute of Physics Publishing
A1  - Abdalsalam Mohamed, E.
A1  - Zuki Yusoff, M.
A1  - Khalil Adam, I.
A1  - Ali Hamid, E.
A1  - Al-Shargie, F.
A1  - Muzammel, M.
AV  - none
N1  - 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
ER  -