Double-Criteria Active Learning for Multiclass Brain-Computer Interfaces
Joint Authors
She, Qingshan
Luo, Zhizeng
Zhang, Yingchun
Nguyen, Thinh
Potter, Thomas
Chen, Kang
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-03-10
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract 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.
American Psychological Association (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
Modern Language Association (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
American Medical Association (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
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138741