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Double-Criteria Active Learning for Multiclass Brain-Computer Interfaces
المؤلفون المشاركون
She, Qingshan
Luo, Zhizeng
Zhang, Yingchun
Nguyen, Thinh
Potter, Thomas
Chen, Kang
المصدر
Computational Intelligence and Neuroscience
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-03-10
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Recent technological advances have enabled researchers to collect large amounts of electroencephalography (EEG) signals in labeled and unlabeled datasets.
It is expensive and time consuming to collect labeled EEG data for use in brain-computer interface (BCI) systems, however.
In this paper, a novel active learning method is proposed to minimize the amount of labeled, subject-specific EEG data required for effective classifier training, by combining measures of uncertainty and representativeness within an extreme learning machine (ELM).
Following this approach, an ELM classifier was first used to select a relatively large batch of unlabeled examples, whose uncertainty was measured through the best-versus-second-best (BvSB) strategy.
The diversity of each sample was then measured between the limited labeled training data and previously selected unlabeled samples, and similarity is measured among the previously selected samples.
Finally, a tradeoff parameter is introduced to control the balance between informative and representative samples, and these samples are then used to construct a powerful ELM classifier.
Extensive experiments were conducted using benchmark and multiclass motor imagery EEG datasets to evaluate the efficacy of the proposed method.
Experimental results show that the performance of the new algorithm exceeds or matches those of several state-of-the-art active learning algorithms.
It is thereby shown that the proposed method improves classifier performance and reduces the need for training samples in BCI applications.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
She, Qingshan& Chen, Kang& Luo, Zhizeng& Nguyen, Thinh& Potter, Thomas& Zhang, Yingchun. 2020. Double-Criteria Active Learning for Multiclass Brain-Computer Interfaces. Computational Intelligence and Neuroscience،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1138741
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
She, Qingshan…[et al.]. Double-Criteria Active Learning for Multiclass Brain-Computer Interfaces. Computational Intelligence and Neuroscience No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1138741
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
She, Qingshan& Chen, Kang& Luo, Zhizeng& Nguyen, Thinh& Potter, Thomas& Zhang, Yingchun. Double-Criteria Active Learning for Multiclass Brain-Computer Interfaces. Computational Intelligence and Neuroscience. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1138741
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1138741
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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