GS11-4 Design of Automated Real-Time BCI Application Using EEG Signals

Title:	GS11-4 Design of Automated Real-Time BCI Application Using EEG Signals
Publication:	ICAROB2017
Volume:	22
Pages:	703-706
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2017.GS11-4
Author(s):	Chong Yeh Sai, Norrima Mokhtar, Hamzah Arof, Masahiro Iwahashi
Publication Date:	January 19, 2017
Keywords:	Electroencephalogram, EEG Pre-processing, Feature Extraction, Supervised Machine Learning, Artificial Neural Network
Abstract:	This study proposed a design of real time BCI application using EEG recording, pre-processing, feature extraction and classification of EEG signals. Recorded EEG signals are highly contaminated by noises and artifacts that originate from outside of cerebral origin. In this study, pre-processing of EEG signals using wavelet multiresolution analysis and independent component analysis is applied to automatically remove the noises and artifacts. Consequently, features of interest are extracted as descriptive properties of the EEG signals. Finally, classification algorithms using artificial neural network is used to distinguish the state of EEG signals for real time BCI application.
PDF File:	https://alife-robotics.co.jp/members2017/icarob/data/html/data/GS_pdf/GS11/GS11-4.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/