• DocumentCode
    39418
  • Title

    A Three-Step Multimodal Analysis Framework for Modeling Corticomuscular Activity With Application to Parkinson’s Disease

  • Author

    Xun Chen ; Wang, Z. Jane ; McKeown, Martin J.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of British Columbia, Vancouver, BC, Canada
  • Volume
    18
  • Issue
    4
  • fYear
    2014
  • fDate
    Jul-14
  • Firstpage
    1232
  • Lastpage
    1241
  • Abstract
    Corticomuscular coupling analysis based on multiple datasets such as electroencephalography (EEG) and electromyography (EMG) signals provides a useful tool for understanding human motor control systems. A popular conventional method to assess corticomuscular coupling has been the pair-wise magnitude-squared coherence (MSC) between EEG and concomitant EMG recordings. However, there are certain limitations associated with the MSC, including the difficulty in robustly assessing group inference, only dealing with two types of datasets simultaneously and the biologically implausible assumption of pair-wise interactions. To overcome such limitations, in this paper, we propose assessing corticomuscular coupling by combining multiset canonical correlation analysis (M-CCA) and joint independent component analysis (jICA). The proposed method takes advantage of the M-CCA and jICA to ensure that the extracted components are maximally correlated across multiple datasets and meanwhile statistically independent within each dataset. Simulations were performed to illustrate the performance of the proposed method. We also applied the proposed method to concurrent EEG, EMG, and behavior data collected in a Parkinson´s disease (PD) study. The results reveal highly correlated temporal patterns among the three types of signals and corresponding spatial activation patterns. In addition to the expected motor areas, the corresponding spatial activation patterns demonstrate enhanced occipital connectivity in the PD subjects, consistent with previous medical findings.
  • Keywords
    diseases; electroencephalography; electromyography; feature extraction; independent component analysis; medical signal processing; EEG signals; EMG signals; M-CCA; MSC; Parkinson disease; corticomuscular coupling analysis; electroencephalography; electromyography; group inference; human motor control systems; jICA; joint independent component analysis; multiset canonical correlation analysis; pair-wise interactions; pair-wise magnitude-squared coherence; spatial activation patterns; statistical analysis; three-step multimodal analysis framework; Correlation; Data mining; Electroencephalography; Electromyography; Equations; Joints; Mathematical model; Blind source separation; Multimodal; Parkinson’s disease (PD); corticomuscular coupling; data fusion;
  • fLanguage
    English
  • Journal_Title
    Biomedical and Health Informatics, IEEE Journal of
  • Publisher
    ieee
  • ISSN
    2168-2194
  • Type

    jour

  • DOI
    10.1109/JBHI.2013.2284480
  • Filename
    6620985