title: Scale-Dependent Signal Identification in Low-Dimensional Subspace: Motor Imagery Task Classification.
abstract: 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.
authors.affiliation: Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
authors.forename: Qingshan
authors.identifier: 0000-0001-5206-9833
authors.initials: Q
authors.lastname: She
authors.affiliation: Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
authors.forename: Haitao
authors.initials: H
authors.lastname: Gan
authors.affiliation: Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
authors.forename: Yuliang
authors.identifier: 0000-0003-1277-4663
authors.initials: Y
authors.lastname: Ma
authors.affiliation: Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China.
authors.forename: Zhizeng
authors.identifier: 0000-0002-2689-3455
authors.initials: Z
authors.lastname: Luo
authors.affiliation: Department of Biomedical Engineering, University of Houston, Houston, TX 77204, USA.
authors.forename: Tom
authors.initials: T
authors.lastname: Potter
authors.affiliation: Department of Biomedical Engineering, University of Houston, Houston, TX 77204, USA; Guangdong Provincial Work Injury Rehabilitation Center, Guangzhou 510000, China.
authors.forename: Yingchun
authors.identifier: 0000-0002-1927-4103
authors.initials: Y
authors.lastname: Zhang
mesh_terms: D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D019018:Imagery, Psychotherapy / Q000379:methods*; D009048:Motor Skills / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*; D060388:Support Vector Machine