Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering
Joint Authors
Lin, Chin-Teng
Huang, Chih-Sheng
Yang, Wen-Yu
Singh, Avinash Kumar
Chuang, Chun-Hsiang
Wang, Yu-Kai
Source
Journal of Healthcare Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-15
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Lin, Chin-Teng& Huang, Chih-Sheng& Yang, Wen-Yu& Singh, Avinash Kumar& Chuang, Chun-Hsiang& Wang, Yu-Kai. 2018. Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering. Journal of Healthcare Engineering،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1187346
Modern Language Association (MLA)
Lin, Chin-Teng…[et al.]. Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering. Journal of Healthcare Engineering No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1187346
American Medical Association (AMA)
Lin, Chin-Teng& Huang, Chih-Sheng& Yang, Wen-Yu& Singh, Avinash Kumar& Chuang, Chun-Hsiang& Wang, Yu-Kai. Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering. Journal of Healthcare Engineering. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1187346
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1187346