Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification
المؤلفون المشاركون
She, Qingshan
Ma, Yuliang
Zhang, Yingchun
Nguyen, Thinh
Chen, Kang
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-10-28
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Classification of motor imagery (MI) electroencephalogram (EEG) plays a vital role in brain-computer interface (BCI) systems.
Recent research has shown that nonlinear classification algorithms perform better than their linear counterparts, but most of them cannot extract sufficient significant information which leads to a less efficient classification.
In this paper, we propose a novel approach called FDDL-ELM, which combines the discriminative power of extreme learning machine (ELM) with the reconstruction capability of sparse representation.
Firstly, the common spatial pattern (CSP) algorithm is adopted to perform spatial filtering on raw EEG data to enhance the task-related neural activity.
Secondly, the Fisher discrimination criterion is employed to learn a structured dictionary and obtain sparse coding coefficients from the filtered data, and these discriminative coefficients are then used to acquire the reconstructed feature representations.
Finally, a nonlinear classifier ELM is used to identify these features in different MI tasks.
The proposed method is evaluated on 2-class Datasets IVa and IIIa of BCI Competition III and 4-class Dataset IIa of BCI Competition IV.
Experimental results show that our method achieved superior performance than the other existing algorithms and yielded the accuracies of 80.68%, 87.54%, and 63.76% across all subjects in the above-mentioned three datasets, respectively.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
She, Qingshan& Chen, Kang& Ma, Yuliang& Nguyen, Thinh& Zhang, Yingchun. 2018. Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1130855
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
She, Qingshan…[et al.]. Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1130855
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
She, Qingshan& Chen, Kang& Ma, Yuliang& Nguyen, Thinh& Zhang, Yingchun. Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1130855
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1130855
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