Scale-Dependent Signal Identification in Low-Dimensional Subspace: Motor Imagery Task Classification
Joint Authors
She, Qingshan
Ma, Yuliang
Luo, Zhizeng
Zhang, Yingchun
Gan, Haitao
Potter, Tom
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-11-03
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Motor imagery electroencephalography (EEG) has been successfully used in locomotor rehabilitation programs.
While the noise-assisted multivariate empirical mode decomposition (NA-MEMD) algorithm has been utilized to extract task-specific frequency bands from all channels in the same scale as the intrinsic mode functions (IMFs), identifying and extracting the specific IMFs that contain significant information remain difficult.
In this paper, a novel method has been developed to identify the information-bearing components in a low-dimensional subspace without prior knowledge.
Our method trains a Gaussian mixture model (GMM) of the composite data, which is comprised of the IMFs from both the original signal and noise, by employing kernel spectral regression to reduce the dimension of the composite data.
The informative IMFs are then discriminated using a GMM clustering algorithm, the common spatial pattern (CSP) approach is exploited to extract the task-related features from the reconstructed signals, and a support vector machine (SVM) is applied to the extracted features to recognize the classes of EEG signals during different motor imagery tasks.
The effectiveness of the proposed method has been verified by both computer simulations and motor imagery EEG datasets.
American Psychological Association (APA)
She, Qingshan& Gan, Haitao& Ma, Yuliang& Luo, Zhizeng& Potter, Tom& Zhang, Yingchun. 2016. Scale-Dependent Signal Identification in Low-Dimensional Subspace: Motor Imagery Task Classification. Neural Plasticity،Vol. 2016, no. 2016, pp.1-15.
https://search.emarefa.net/detail/BIM-1113273
Modern Language Association (MLA)
She, Qingshan…[et al.]. Scale-Dependent Signal Identification in Low-Dimensional Subspace: Motor Imagery Task Classification. Neural Plasticity No. 2016 (2016), pp.1-15.
https://search.emarefa.net/detail/BIM-1113273
American Medical Association (AMA)
She, Qingshan& Gan, Haitao& Ma, Yuliang& Luo, Zhizeng& Potter, Tom& Zhang, Yingchun. Scale-Dependent Signal Identification in Low-Dimensional Subspace: Motor Imagery Task Classification. Neural Plasticity. 2016. Vol. 2016, no. 2016, pp.1-15.
https://search.emarefa.net/detail/BIM-1113273
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1113273