Real-Time EEG Signal Enhancement Using Canonical Correlation Analysis and Gaussian Mixture Clustering
المؤلفون المشاركون
Lin, Chin-Teng
Huang, Chih-Sheng
Yang, Wen-Yu
Singh, Avinash Kumar
Chuang, Chun-Hsiang
Wang, Yu-Kai
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-01-15
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص 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.
نمط استشهاد جمعية علماء النفس الأمريكية (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
نمط استشهاد الجمعية الأمريكية للغات الحديثة (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
نمط استشهاد الجمعية الطبية الأمريكية (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
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1187346
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر