Name
Abstract | ||
|---|---|---|
Corticomuscular coupling analysis, i.e., examining the relations between simultaneously recorded brain (e.g., electroencephalography--EEG) and muscle (e.g., electro-myography-EMG) signals, is a useful tool for understanding aspects of human motor control. Traditionally, the most popular method to assess corticomuscular coupling has been the pairwise magnitude-squared coherence (MSC) between EEG and concomitant EMG recordings. In this paper, we propose assessing corticomuscular coupling by combining partial least squares (PLS) and independent component analysis (ICA), which addresses many of the limitations of MSC, such as difficulty in robustly assessing group inference and relying on the biologically implausible assumption of pairwise interaction between brain and muscle recordings. In the proposed framework, response relevance and statistical independence are jointly incorporated into a multiobjective optimization function to meaningfully combine the goals of PLS and ICA under the same mathematical umbrella. Simulations, performed under realistic assumptions, illustrated the utility of such an approach. The method was extended to address intersubject variability to robustly discover common corticomuscular coupling patterns across subjects. We then applied the proposed framework to concurrent EEG and EMG data collected in a Parkinson's disease (PD) study. The results from applying the proposed technique revealed temporal components in the EEG and EMG that were significantly correlated with one another. In addition to the expected motor areas, the corresponding spatial activation patterns demonstrated enhanced occipital connectivity in PD subjects, consistent with previous studies suggesting that PD subjects rely excessively on visual information to counteract the deficiency in being able to generate internal commands from their affected basal ganglia. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TBME.2013.2248059 | IEEE Trans. Biomed. Engineering |
Keywords | DocType | Volume |
basal ganglia,optimisation,magnitude-squared coherence,eeg data collection,response relevance,muscle signal recording,diseases,statistical independence,electroencephalography (eeg),emg data collection,corticomuscular coupling patterns,electromyography (emg),ic-pls framework,electroencephalography,parkinson’s disease (pd),independent component analysis (ica),medical signal processing,spatiotemporal phenomena,independent component analysis,corticomuscular coupling analysis,data fusion,least squares approximations,brain signal recording,partial least squares (pls),corticomuscular coupling,multiobjective optimization function,electromyography,human motor control,group analysis,partial least squares,spatial activation patterns,occipital connectivity enhancement,parkinson disease,optimization,principal component analysis,excitation contraction coupling,couplings,isometric contraction,least squares analysis,feature extraction,correlation,algorithms | Journal | 60 |
Issue | ISSN | Citations |
7 | 1558-2531 | 8 |
PageRank | References | Authors |
0.52 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xun Chen | 1 | 458 | 52.73 |
| Chen He | 2 | 100 | 11.38 |
| Z Jane Wang | 3 | 8 | 1.20 |
| Martin J. Mckeown | 4 | 415 | 75.56 |