![](/images/graphics-bg.png)
Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis
المؤلفون المشاركون
Zhang, Xu
Liu, Qingze
Chen, Xiang
Qian, Ruobing
Liu, Aiping
Chen, Xun
المصدر
Journal of Healthcare Engineering
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-12-31
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Electroencephalography (EEG) signals collected from human scalps are often polluted by diverse artifacts, for instance electromyogram (EMG), electrooculogram (EOG), and electrocardiogram (ECG) artifacts.
Muscle artifacts are particularly difficult to eliminate among all kinds of artifacts due to their complexity.
At present, several researchers have proved the superiority of combining single-channel decomposition algorithms with blind source separation (BSS) to make multichannel EEG recordings free from EMG contamination.
In our study, we come up with a novel and valid method to accomplish muscle artifact removal from EEG by using the combination of singular spectrum analysis (SSA) and canonical correlation analysis (CCA), which is named as SSA-CCA.
Unlike the traditional single-channel decomposition methods, for example, ensemble empirical mode decomposition (EEMD), SSA algorithm is a technique based on principles of multivariate statistics.
Our proposed approach can take advantage of SSA as well as cross-channel information.
The performance of SSA-CCA is evaluated on semisimulated and real data.
The results demonstrate that this method outperforms the state-of-the-art technique, EEMD-CCA, and the classic technique, CCA, under multichannel circumstances.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Liu, Qingze& Liu, Aiping& Zhang, Xu& Chen, Xiang& Qian, Ruobing& Chen, Xun. 2019. Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis. Journal of Healthcare Engineering،Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1175183
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Liu, Qingze…[et al.]. Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis. Journal of Healthcare Engineering No. 2019 (2019), pp.1-13.
https://search.emarefa.net/detail/BIM-1175183
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Liu, Qingze& Liu, Aiping& Zhang, Xu& Chen, Xiang& Qian, Ruobing& Chen, Xun. Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis. Journal of Healthcare Engineering. 2019. Vol. 2019, no. 2019, pp.1-13.
https://search.emarefa.net/detail/BIM-1175183
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1175183
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
![](/images/ebook-kashef.png)
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
![](/images/kashef-image.png)