Multimodal biomedical signal processing for corticomuscular coupling analysis

• Main Author: Chen, Xun
• Language: English
• Published: University of British Columbia 2014
• Online Access: http://hdl.handle.net/2429/45811

Corticomuscular coupling analysis using multiple data sets such as electroencepha-logram (EEG) and electromyogram (EMG) signals provides a useful tool for understanding human motor control systems. A popular conventional method to assess corticomuscular coupling is the pair-wise magnitude-squared coherence (MSC). However, there are certain limitations associated with MSC, including the difficulty in robustly assessing group inference, only dealing with two types of data sets simultaneously and the biologically implausible assumption of pair-wise interactions. In this thesis, we propose several novel signal processing techniques to overcome the disadvantages of current coupling analysis methods. We propose combining partial least squares (PLS) and canonical correlation analysis (CCA) to take advantage of both techniques to ensure that the extracted components are maximally correlated across two data sets and meanwhile can well explain the information within each data set. Furthermore, we propose jointly incorporating response-relevance and statistical independence into a multi-objective optimization function, meaningfully combining the goals of independent component analysis (ICA) and PLS under the same mathematical umbrella. In addition, we extend the coupling analysis to multiple data sets by proposing a joint multimodal group analysis framework. Finally, to acquire independent components but not just uncorrelated ones, we improve the multimodal framework by exploiting the complementary property of multiset canonical correlation analysis (M-CCA) and joint ICA. Simulations show that our proposed methods can achieve superior performances than conventional approaches. We also apply the proposed methods to concurrent EEG, EMG and behavior data collected in a Parkinson's disease (PD) study. The results reveal highly correlated temporal patterns among the multimodal signals and corresponding spatial activation patterns. In addition to the expected motor areas, the corresponding spatial activation patterns demonstrate enhanced occipital connectivity in PD subjects, consistent with previous medical findings. === Applied Science, Faculty of === Graduate