標題: Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering
作者: Lin, Chin-Teng
Huang, Chih-Sheng
Yang, Wen-Yu
Singh, Avinash Kumar
Chuang, Chun-Hsiang
Wang, Yu-Kai
生物科技學院
腦科學研究中心
College of Biological Science and Technology
Brain Research Center
公開日期: 1-Jan-2018
摘要: 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.
URI: http://dx.doi.org/10.1155/2018/5081258
http://hdl.handle.net/11536/144447
ISSN: 2040-2295
DOI: 10.1155/2018/5081258
期刊: JOURNAL OF HEALTHCARE ENGINEERING
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