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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46291

Title: Real-time EEG signal enhancement using canonical correlation analysis and Gaussian mixture clustering
Authors: Chin-Teng Lin
Chih-Sheng Huang
Wen-Yu Yang
Avinash Kumar Singh
Chun-Hsiang Chuang
Yu-Kai Wang
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Date: 2017-11
Issue Date: 2018-05-14T07:25:40Z
Publisher: Journal of Healthcare Engineering
Abstract: 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.
Relation: pp. 1-11
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/46291
Appears in Collections:[資訊工程學系] 期刊論文

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