Scale-dependent signal identification in low-dimensional subspace: Motor imagery task classification

Qingshan She, Haitao Gan, Yuliang Ma, Zhizeng Luo, Tom Potter, Yingchun Zhang

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

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.

Original languageEnglish (US)
Article number7431012
JournalNeural Plasticity
Volume2016
DOIs
StatePublished - 2016

ASJC Scopus subject areas

  • Neurology
  • Clinical Neurology

Fingerprint Dive into the research topics of 'Scale-dependent signal identification in low-dimensional subspace: Motor imagery task classification'. Together they form a unique fingerprint.

Cite this