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

Xun Chen,Aiping Liu,Z Jane Wang,Hu Peng

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.

Highlights

Corticomuscular activity modeling is important for assessing functional interactions in the motor control system, that is, studying simultaneous cortical and muscular activities during a sustained isometric muscle contraction
In monkeys, coherent oscillations in the 20–30 Hz band could be detected between cortical local field potentials and the rectified electromyography (EMG) from contralateral hand muscles that were modulated during different phases of a precision grip task [1]
When the brain activity is measured by EEG, applying magnitudesquared coherence (MSC) directly to raw EEG and EMG signals normally yields a very low coherence value, because only a small fraction of ongoing EEG activity is related to the motor control [8]

Summary

Introduction

Corticomuscular activity modeling is important for assessing functional interactions in the motor control system, that is, studying simultaneous cortical and muscular activities during a sustained isometric muscle contraction. When the brain activity is measured by EEG, applying MSC directly to raw EEG and EMG signals normally yields a very low coherence value, because only a small fraction of ongoing EEG activity is related to the motor control [8]. This implies that extensive statistical testing is required to determine whether the EEG/EMG coherence is statistically significant

Objectives

Methods

Results

Conclusion