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2013 Corticomuscular Activity Modeling by Combining Partial Least Squares and Canonical Correlation Analysis
Xun Chen, Aiping Liu, Z. Jane Wang, Hu Peng
J. Appl. Math. 2013(SI05): 1-11 (2013). DOI: 10.1155/2013/401976

Abstract

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.

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Xun Chen. Aiping Liu. Z. Jane Wang. Hu Peng. "Corticomuscular Activity Modeling by Combining Partial Least Squares and Canonical Correlation Analysis." J. Appl. Math. 2013 (SI05) 1 - 11, 2013. https://doi.org/10.1155/2013/401976

Information

Published: 2013
First available in Project Euclid: 14 March 2014

zbMATH: 1266.92051
Digital Object Identifier: 10.1155/2013/401976

Rights: Copyright © 2013 Hindawi

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