Corticomuscular Activity Modeling by Combining Partial Least Squares and Canonical Correlation Analysis
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 (...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2013/401976 |
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| _version_ | 1832548789296037888 |
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| author | Xun Chen Aiping Liu Z. Jane Wang Hu Peng |
| author_facet | Xun Chen Aiping Liu Z. Jane Wang Hu Peng |
| author_sort | Xun Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ec7b9081ef0b434a92cf3945db8987b4 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-ec7b9081ef0b434a92cf3945db8987b42025-02-03T06:13:07ZengWileyJournal of Applied Mathematics1110-757X1687-00422013-01-01201310.1155/2013/401976401976Corticomuscular Activity Modeling by Combining Partial Least Squares and Canonical Correlation AnalysisXun Chen0Aiping Liu1Z. Jane Wang2Hu Peng3Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, V6T 1Z4, CanadaDepartment of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, V6T 1Z4, CanadaDepartment of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, V6T 1Z4, CanadaDepartment of Biomedical Engineering, School of Medical Engineering, Hefei University of Technology, Hefei, Anhui 230009, ChinaCorticomuscular 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.http://dx.doi.org/10.1155/2013/401976 |
| spellingShingle | Xun Chen Aiping Liu Z. Jane Wang Hu Peng Corticomuscular Activity Modeling by Combining Partial Least Squares and Canonical Correlation Analysis Journal of Applied Mathematics |
| title | Corticomuscular Activity Modeling by Combining Partial Least Squares and Canonical Correlation Analysis |
| title_full | Corticomuscular Activity Modeling by Combining Partial Least Squares and Canonical Correlation Analysis |
| title_fullStr | Corticomuscular Activity Modeling by Combining Partial Least Squares and Canonical Correlation Analysis |
| title_full_unstemmed | Corticomuscular Activity Modeling by Combining Partial Least Squares and Canonical Correlation Analysis |
| title_short | Corticomuscular Activity Modeling by Combining Partial Least Squares and Canonical Correlation Analysis |
| title_sort | corticomuscular activity modeling by combining partial least squares and canonical correlation analysis |
| url | http://dx.doi.org/10.1155/2013/401976 |
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