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Journal of Applied Mathematics
Volume 2013, Article ID 401976, 11 pages
Research Article

Corticomuscular Activity Modeling by Combining Partial Least Squares and Canonical Correlation Analysis

1Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada V6T 1Z4
2Department of Biomedical Engineering, School of Medical Engineering, Hefei University of Technology, Hefei, Anhui 230009, China

Received 8 March 2013; Accepted 9 May 2013

Academic Editor: Chang-Hwan Im

Copyright © 2013 Xun Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Corticomuscular activity modeling based on multiple data sets such as electroencephalography (EEG) and electromyography (EMG) signals provides a useful tool for understanding human motor control systems. In this paper, we propose modeling corticomuscular activity by combining partial least squares (PLS) and canonical correlation analysis (CCA). The proposed method takes advantage of both PLS and CCA to ensure that the extracted components are maximally correlated across two data sets and meanwhile can well explain the information within each data set. This complementary combination generalizes the statistical assumptions beyond both PLS and CCA methods. Simulations were performed to illustrate the performance of the proposed method. We also applied the proposed method to concurrent EEG and EMG data collected in a Parkinson’s disease (PD) study. The results reveal several highly correlated temporal patterns between EEG and EMG signals and indicate meaningful corresponding spatial activation patterns. In PD subjects, enhanced connections between occipital region and other regions are noted, which is consistent with previous medical knowledge. The proposed framework is a promising technique for performing multisubject and bimodal data analysis.