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Journal of Healthcare Engineering
Volume 2018 (2018), Article ID 5081258, 11 pages
https://doi.org/10.1155/2018/5081258
Research Article

Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering

1School of Software, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia
2Brain Research Center, National Chiao Tung University, Hsinchu, Taiwan
3College of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan

Correspondence should be addressed to Chin-Teng Lin

Received 17 March 2017; Revised 5 October 2017; Accepted 8 November 2017; Published 15 January 2018

Academic Editor: Tiago H. Falk

Copyright © 2018 Chin-Teng Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Electroencephalogram (EEG) signals are usually contaminated with various artifacts, such as signal associated with muscle activity, eye movement, and body motion, which have a noncerebral origin. The amplitude of such artifacts is larger than that of the electrical activity of the brain, so they mask the cortical signals of interest, resulting in biased analysis and interpretation. Several blind source separation methods have been developed to remove artifacts from the EEG recordings. However, the iterative process for measuring separation within multichannel recordings is computationally intractable. Moreover, manually excluding the artifact components requires a time-consuming offline process. This work proposes a real-time artifact removal algorithm that is based on canonical correlation analysis (CCA), feature extraction, and the Gaussian mixture model (GMM) to improve the quality of EEG signals. The CCA was used to decompose EEG signals into components followed by feature extraction to extract representative features and GMM to cluster these features into groups to recognize and remove artifacts. The feasibility of the proposed algorithm was demonstrated by effectively removing artifacts caused by blinks, head/body movement, and chewing from EEG recordings while preserving the temporal and spectral characteristics of the signals that are important to cognitive research.