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Sparse Representation-Based Extreme Learning Machine for Motor Imagery EEG Classification
Joint Authors
She, Qingshan
Ma, Yuliang
Zhang, Yingchun
Nguyen, Thinh
Chen, Kang
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-10-28
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract 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.
American Psychological Association (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
Modern Language Association (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
American Medical Association (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
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130855